Publications

2020
[1]
H. Arnaout, S. Razniewski, and G. Weikum, “Negative Statements Considered Useful,” 2020. [Online]. Available: http://arxiv.org/abs/2001.04425. (arXiv: 2001.04425)
Abstract
Knowledge bases (KBs), pragmatic collections of knowledge about notable entities, are an important asset in applications such as search, question answering and dialogue. Rooted in a long tradition in knowledge representation, all popular KBs only store positive information, while they abstain from taking any stance towards statements not contained in them. In this paper, we make the case for explicitly stating interesting statements which are not true. Negative statements would be important to overcome current limitations of question answering, yet due to their potential abundance, any effort towards compiling them needs a tight coupling with ranking. We introduce two approaches towards compiling negative statements. (i) In peer-based statistical inferences, we compare entities with highly related entities in order to derive potential negative statements, which we then rank using supervised and unsupervised features. (ii) In query-log-based text extraction, we use a pattern-based approach for harvesting search engine query logs. Experimental results show that both approaches hold promising and complementary potential. Along with this paper, we publish the first datasets on interesting negative information, containing over 1.1M statements for 100K popular Wikidata entities.
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BibTeX
@online{Arnaout_arXiv2001.04425, TITLE = {Negative Statements Considered Useful}, AUTHOR = {Arnaout, Hiba and Razniewski, Simon and Weikum, Gerhard}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/2001.04425}, EPRINT = {2001.04425}, EPRINTTYPE = {arXiv}, YEAR = {2020}, ABSTRACT = {Knowledge bases (KBs), pragmatic collections of knowledge about notable entities, are an important asset in applications such as search, question answering and dialogue. Rooted in a long tradition in knowledge representation, all popular KBs only store positive information, while they abstain from taking any stance towards statements not contained in them. In this paper, we make the case for explicitly stating interesting statements which are not true. Negative statements would be important to overcome current limitations of question answering, yet due to their potential abundance, any effort towards compiling them needs a tight coupling with ranking. We introduce two approaches towards compiling negative statements. (i) In peer-based statistical inferences, we compare entities with highly related entities in order to derive potential negative statements, which we then rank using supervised and unsupervised features. (ii) In query-log-based text extraction, we use a pattern-based approach for harvesting search engine query logs. Experimental results show that both approaches hold promising and complementary potential. Along with this paper, we publish the first datasets on interesting negative information, containing over 1.1M statements for 100K popular Wikidata entities.}, }
Endnote
%0 Report %A Arnaout, Hiba %A Razniewski, Simon %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Negative Statements Considered Useful : %G eng %U http://hdl.handle.net/21.11116/0000-0005-821F-6 %U http://arxiv.org/abs/2001.04425 %D 2020 %X Knowledge bases (KBs), pragmatic collections of knowledge about notable entities, are an important asset in applications such as search, question answering and dialogue. Rooted in a long tradition in knowledge representation, all popular KBs only store positive information, while they abstain from taking any stance towards statements not contained in them. In this paper, we make the case for explicitly stating interesting statements which are not true. Negative statements would be important to overcome current limitations of question answering, yet due to their potential abundance, any effort towards compiling them needs a tight coupling with ranking. We introduce two approaches towards compiling negative statements. (i) In peer-based statistical inferences, we compare entities with highly related entities in order to derive potential negative statements, which we then rank using supervised and unsupervised features. (ii) In query-log-based text extraction, we use a pattern-based approach for harvesting search engine query logs. Experimental results show that both approaches hold promising and complementary potential. Along with this paper, we publish the first datasets on interesting negative information, containing over 1.1M statements for 100K popular Wikidata entities. %K Computer Science, Information Retrieval, cs.IR,Computer Science, Artificial Intelligence, cs.AI,Computer Science, Computation and Language, cs.CL,Computer Science, Databases, cs.DB
[2]
Y. Chalier, S. Razniewski, and G. Weikum, “Joint Reasoning for Multi-Faceted Commonsense Knowledge,” 2020. [Online]. Available: http://arxiv.org/abs/2001.04170. (arXiv: 2001.04170)
Abstract
Commonsense knowledge (CSK) supports a variety of AI applications, from visual understanding to chatbots. Prior works on acquiring CSK, such as ConceptNet, have compiled statements that associate concepts, like everyday objects or activities, with properties that hold for most or some instances of the concept. Each concept is treated in isolation from other concepts, and the only quantitative measure (or ranking) of properties is a confidence score that the statement is valid. This paper aims to overcome these limitations by introducing a multi-faceted model of CSK statements and methods for joint reasoning over sets of inter-related statements. Our model captures four different dimensions of CSK statements: plausibility, typicality, remarkability and salience, with scoring and ranking along each dimension. For example, hyenas drinking water is typical but not salient, whereas hyenas eating carcasses is salient. For reasoning and ranking, we develop a method with soft constraints, to couple the inference over concepts that are related in in a taxonomic hierarchy. The reasoning is cast into an integer linear programming (ILP), and we leverage the theory of reduction costs of a relaxed LP to compute informative rankings. This methodology is applied to several large CSK collections. Our evaluation shows that we can consolidate these inputs into much cleaner and more expressive knowledge. Results are available at https://dice.mpi-inf.mpg.de.
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BibTeX
@online{Chalier_arXiv2001.04170, TITLE = {Joint Reasoning for Multi-Faceted Commonsense Knowledge}, AUTHOR = {Chalier, Yohan and Razniewski, Simon and Weikum, Gerhard}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/2001.04170}, EPRINT = {2001.04170}, EPRINTTYPE = {arXiv}, YEAR = {2020}, ABSTRACT = {Commonsense knowledge (CSK) supports a variety of AI applications, from visual understanding to chatbots. Prior works on acquiring CSK, such as ConceptNet, have compiled statements that associate concepts, like everyday objects or activities, with properties that hold for most or some instances of the concept. Each concept is treated in isolation from other concepts, and the only quantitative measure (or ranking) of properties is a confidence score that the statement is valid. This paper aims to overcome these limitations by introducing a multi-faceted model of CSK statements and methods for joint reasoning over sets of inter-related statements. Our model captures four different dimensions of CSK statements: plausibility, typicality, remarkability and salience, with scoring and ranking along each dimension. For example, hyenas drinking water is typical but not salient, whereas hyenas eating carcasses is salient. For reasoning and ranking, we develop a method with soft constraints, to couple the inference over concepts that are related in in a taxonomic hierarchy. The reasoning is cast into an integer linear programming (ILP), and we leverage the theory of reduction costs of a relaxed LP to compute informative rankings. This methodology is applied to several large CSK collections. Our evaluation shows that we can consolidate these inputs into much cleaner and more expressive knowledge. Results are available at https://dice.mpi-inf.mpg.de.}, }
Endnote
%0 Report %A Chalier, Yohan %A Razniewski, Simon %A Weikum, Gerhard %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Joint Reasoning for Multi-Faceted Commonsense Knowledge : %G eng %U http://hdl.handle.net/21.11116/0000-0005-8226-D %U http://arxiv.org/abs/2001.04170 %D 2020 %X Commonsense knowledge (CSK) supports a variety of AI applications, from visual understanding to chatbots. Prior works on acquiring CSK, such as ConceptNet, have compiled statements that associate concepts, like everyday objects or activities, with properties that hold for most or some instances of the concept. Each concept is treated in isolation from other concepts, and the only quantitative measure (or ranking) of properties is a confidence score that the statement is valid. This paper aims to overcome these limitations by introducing a multi-faceted model of CSK statements and methods for joint reasoning over sets of inter-related statements. Our model captures four different dimensions of CSK statements: plausibility, typicality, remarkability and salience, with scoring and ranking along each dimension. For example, hyenas drinking water is typical but not salient, whereas hyenas eating carcasses is salient. For reasoning and ranking, we develop a method with soft constraints, to couple the inference over concepts that are related in in a taxonomic hierarchy. The reasoning is cast into an integer linear programming (ILP), and we leverage the theory of reduction costs of a relaxed LP to compute informative rankings. This methodology is applied to several large CSK collections. Our evaluation shows that we can consolidate these inputs into much cleaner and more expressive knowledge. Results are available at https://dice.mpi-inf.mpg.de. %K Computer Science, Computation and Language, cs.CL,Computer Science, Artificial Intelligence, cs.AI,Computer Science, Information Retrieval, cs.IR
[3]
C. X. Chu, S. Razniewski, and G. Weikum, “ENTYFI: Entity Typing in Fictional Texts,” in WSDM’19, 13th International Conference on Web Search and Data Mining, Houston, TX, USA, 2020.
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@inproceedings{ChuWSDM2020, TITLE = {{ENTYFI}: {E}ntity Typing in Fictional Texts}, AUTHOR = {Chu, Cuong Xuan and Razniewski, Simon and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {9781450368223}, DOI = {10.1145/3336191.3371808}, PUBLISHER = {ACM}, YEAR = {2020}, BOOKTITLE = {WSDM'19, 13th International Conference on Web Search and Data Mining}, EDITOR = {Caverlee, James and Hu, Xia Ben}, PAGES = {124--132}, ADDRESS = {Houston, TX, USA}, }
Endnote
%0 Conference Proceedings %A Chu, Cuong Xuan %A Razniewski, Simon %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T ENTYFI: Entity Typing in Fictional Texts : %G eng %U http://hdl.handle.net/21.11116/0000-0006-A27E-6 %R 10.1145/3336191.3371808 %D 2020 %B 13th International Conference on Web Search and Data Mining %Z date of event: 2020-02-03 - 2020-02-07 %C Houston, TX, USA %B WSDM'19 %E Caverlee, James; Hu, Xia Ben %P 124 - 132 %I ACM %@ 9781450368223
[4]
F. Darari, W. Nutt, S. Razniewski, and S. Rudolph, “Completeness and soundness guarantees for conjunctive SPARQL queries over RDF data sources with completeness statements,” Semantic Web, vol. 11, no. 1, 2020.
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@article{Darari2020, TITLE = {Completeness and soundness guarantees for conjunctive {SPARQL} queries over {RDF} data sources with completeness statements}, AUTHOR = {Darari, Fariza and Nutt, Werner and Razniewski, Simon and Rudolph, Sebastian}, LANGUAGE = {eng}, ISSN = {1570-0844}, DOI = {10.3233/SW-190344}, PUBLISHER = {IOS Press}, ADDRESS = {Amsterdam}, YEAR = {2020}, DATE = {2020}, JOURNAL = {Semantic Web}, VOLUME = {11}, NUMBER = {1}, PAGES = {441--482}, }
Endnote
%0 Journal Article %A Darari, Fariza %A Nutt, Werner %A Razniewski, Simon %A Rudolph, Sebastian %+ External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Completeness and soundness guarantees for conjunctive SPARQL queries over RDF data sources with completeness statements : %G eng %U http://hdl.handle.net/21.11116/0000-0006-9A06-6 %R 10.3233/SW-190344 %7 2020 %D 2020 %J Semantic Web %V 11 %N 1 %& 441 %P 441 - 482 %I IOS Press %C Amsterdam %@ false
[5]
V. T. Ho, K. Pal, N. Kleer, K. Berberich, and G. Weikum, “Entities with Quantities: Extraction, Search, and Ranking,” in WSDM’19, 13th International Conference on Web Search and Data Mining, Houston, TX, USA, 2020.
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@inproceedings{HoWSDM2020, TITLE = {Entities with Quantities: {E}xtraction, Search, and Ranking}, AUTHOR = {Ho, Vinh Thinh and Pal, Koninika and Kleer, Niko and Berberich, Klaus and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {9781450368223}, DOI = {10.1145/3336191.3371860}, PUBLISHER = {ACM}, YEAR = {2020}, BOOKTITLE = {WSDM'19, 13th International Conference on Web Search and Data Mining}, EDITOR = {Caverlee, James and Hu, Xia Ben}, PAGES = {833--836}, ADDRESS = {Houston, TX, USA}, }
Endnote
%0 Conference Proceedings %A Ho, Vinh Thinh %A Pal, Koninika %A Kleer, Niko %A Berberich, Klaus %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Entities with Quantities: Extraction, Search, and Ranking : %G eng %U http://hdl.handle.net/21.11116/0000-0006-A284-D %R 10.1145/3336191.3371860 %D 2020 %B 13th International Conference on Web Search and Data Mining %Z date of event: 2020-02-03 - 2020-02-07 %C Houston, TX, USA %B WSDM'19 %E Caverlee, James; Hu, Xia Ben %P 833 - 836 %I ACM %@ 9781450368223
[6]
S. Nag Chowdhury, W. Cheng, G. de Melo, S. Razniewski, and G. Weikum, “Illustrate Your Story: Enriching Text with Images,” in WSDM’19, 13th International Conference on Web Search and Data Mining, Houston, TX, USA, 2020.
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@inproceedings{NagWSDM2020, TITLE = {Illustrate Your Story: {Enriching} Text with Images}, AUTHOR = {Nag Chowdhury, Sreyasi and Cheng, William and de Melo, Gerard and Razniewski, Simon and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {9781450368223}, DOI = {10.1145/3336191.3371866}, PUBLISHER = {ACM}, YEAR = {2020}, BOOKTITLE = {WSDM'19, 13th International Conference on Web Search and Data Mining}, EDITOR = {Caverlee, James and Hu, Xia Ben}, PAGES = {849--852}, ADDRESS = {Houston, TX, USA}, }
Endnote
%0 Conference Proceedings %A Nag Chowdhury, Sreyasi %A Cheng, William %A de Melo, Gerard %A Razniewski, Simon %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Illustrate Your Story: Enriching Text with Images : %G eng %U http://hdl.handle.net/21.11116/0000-0006-A27C-8 %R 10.1145/3336191.3371866 %D 2020 %B 13th International Conference on Web Search and Data Mining %Z date of event: 2020-02-03 - 2020-02-07 %C Houston, TX, USA %B WSDM'19 %E Caverlee, James; Hu, Xia Ben %P 849 - 852 %I ACM %@ 9781450368223
[7]
V. Sathya, S. Ghosh, A. Ramamurthy, and B. R. Tamma, “Small Cell Planning: Resource Management and Interference Mitigation Mechanisms in LTE HetNets,” Wireless Personal Communications, 2020.
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@article{Sathya2020, TITLE = {Small Cell Planning: {R}esource Management and Interference Mitigation Mechanisms in {LTE HetNets}}, AUTHOR = {Sathya, Vanlin and Ghosh, Shrestha and Ramamurthy, Arun and Tamma, Bheemarjuna Reddy}, LANGUAGE = {eng}, ISSN = {0929-6212}, DOI = {10.1007/s11277-020-07574-x}, PUBLISHER = {Springer}, ADDRESS = {New York, NY}, YEAR = {2020}, JOURNAL = {Wireless Personal Communications}, }
Endnote
%0 Journal Article %A Sathya, Vanlin %A Ghosh, Shrestha %A Ramamurthy, Arun %A Tamma, Bheemarjuna Reddy %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T Small Cell Planning: Resource Management and Interference Mitigation Mechanisms in LTE HetNets : %G eng %U http://hdl.handle.net/21.11116/0000-0006-B963-A %R 10.1007/s11277-020-07574-x %7 2020 %D 2020 %J Wireless Personal Communications %I Springer %C New York, NY %@ false
[8]
A. Yates, S. Arora, X. Zhang, W. Yang, K. M. Jose, and J. Lin, “Capreolus: A Toolkit for End-to-End Neural Ad Hoc Retrieval,” in WSDM’19, 13th International Conference on Web Search and Data Mining, Houston, TX, USA, 2020.
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@inproceedings{YatesWSDM2020, TITLE = {Capreolus: {A} Toolkit for End-to-End Neural Ad Hoc Retrieval}, AUTHOR = {Yates, Andrew and Arora, Siddhant and Zhang, Xinyu and Yang, Wei and Jose, Kevin Martin and Lin, Jimmy}, LANGUAGE = {eng}, ISBN = {9781450368223}, DOI = {10.1145/3336191.3371868}, PUBLISHER = {ACM}, YEAR = {2020}, BOOKTITLE = {WSDM'19, 13th International Conference on Web Search and Data Mining}, EDITOR = {Caverlee, James and Hu, Xia Ben}, PAGES = {861--864}, ADDRESS = {Houston, TX, USA}, }
Endnote
%0 Conference Proceedings %A Yates, Andrew %A Arora, Siddhant %A Zhang, Xinyu %A Yang, Wei %A Jose, Kevin Martin %A Lin, Jimmy %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Capreolus: A Toolkit for End-to-End Neural Ad Hoc Retrieval : %G eng %U http://hdl.handle.net/21.11116/0000-0006-A28E-3 %R 10.1145/3336191.3371868 %D 2020 %B 13th International Conference on Web Search and Data Mining %Z date of event: 2020-02-03 - 2020-02-07 %C Houston, TX, USA %B WSDM'19 %E Caverlee, James; Hu, Xia Ben %P 861 - 864 %I ACM %@ 9781450368223
2019
[9]
M. Abouhamra, “AligNarr: Aligning Narratives of Different Length for Movie Summarization,” Universität des Saarlandes, Saarbrücken, 2019.
Abstract
Automatic text alignment is an important problem in natural language processing. It can be used to create the data needed to train different language models. Most research about automatic summarization revolves around summarizing news articles or scientific papers, which are somewhat small texts with simple and clear structure. The bigger the difference in size between the summary and the original text, the harder the problem will be since important information will be sparser and identifying them can be more difficult. Therefore, creating datasets from larger texts can help improve automatic summarization. In this project, we try to develop an algorithm which can automatically create a dataset for abstractive automatic summarization for bigger narrative text bodies such as movie scripts. To this end, we chose sentences as summary text units and scenes as script text units and developed an algorithm which uses some of the latest natural language processing techniques to align scenes and sentences based on the similarity in their meanings. Solving this alignment problem can provide us with important information about how to evaluate the meaning of a text, which can help us create better abstractive summariza- tion models. We developed a method which uses different similarity scoring techniques (embedding similarity, word inclusion and entity inclusion) to align script scenes and sum- mary sentences which achieved an F1 score of 0.39. Analyzing our results showed that the bigger the differences in the number of text units being aligned, the more difficult the alignment problem is. We also critiqued of our own similarity scoring techniques and dif- ferent alignment algorithms based on integer linear programming and local optimization and showed their limitations and discussed ideas to improve them.
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@mastersthesis{AbouhamraMSc2019, TITLE = {{AligNarr}: Aligning Narratives of Different Length for Movie Summarization}, AUTHOR = {Abouhamra, Mostafa}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, ABSTRACT = {Automatic text alignment is an important problem in natural language processing. It can be used to create the data needed to train different language models. Most research about automatic summarization revolves around summarizing news articles or scientific papers, which are somewhat small texts with simple and clear structure. The bigger the difference in size between the summary and the original text, the harder the problem will be since important information will be sparser and identifying them can be more difficult. Therefore, creating datasets from larger texts can help improve automatic summarization. In this project, we try to develop an algorithm which can automatically create a dataset for abstractive automatic summarization for bigger narrative text bodies such as movie scripts. To this end, we chose sentences as summary text units and scenes as script text units and developed an algorithm which uses some of the latest natural language processing techniques to align scenes and sentences based on the similarity in their meanings. Solving this alignment problem can provide us with important information about how to evaluate the meaning of a text, which can help us create better abstractive summariza- tion models. We developed a method which uses different similarity scoring techniques (embedding similarity, word inclusion and entity inclusion) to align script scenes and sum- mary sentences which achieved an F1 score of 0.39. Analyzing our results showed that the bigger the differences in the number of text units being aligned, the more difficult the alignment problem is. We also critiqued of our own similarity scoring techniques and dif- ferent alignment algorithms based on integer linear programming and local optimization and showed their limitations and discussed ideas to improve them.}, }
Endnote
%0 Thesis %A Abouhamra, Mostafa %Y Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T AligNarr: Aligning Narratives of Different Length for Movie Summarization : %G eng %U http://hdl.handle.net/21.11116/0000-0004-5836-D %I Universität des Saarlandes %C Saarbrücken %D 2019 %P 54 p. %V master %9 master %X Automatic text alignment is an important problem in natural language processing. It can be used to create the data needed to train different language models. Most research about automatic summarization revolves around summarizing news articles or scientific papers, which are somewhat small texts with simple and clear structure. The bigger the difference in size between the summary and the original text, the harder the problem will be since important information will be sparser and identifying them can be more difficult. Therefore, creating datasets from larger texts can help improve automatic summarization. In this project, we try to develop an algorithm which can automatically create a dataset for abstractive automatic summarization for bigger narrative text bodies such as movie scripts. To this end, we chose sentences as summary text units and scenes as script text units and developed an algorithm which uses some of the latest natural language processing techniques to align scenes and sentences based on the similarity in their meanings. Solving this alignment problem can provide us with important information about how to evaluate the meaning of a text, which can help us create better abstractive summariza- tion models. We developed a method which uses different similarity scoring techniques (embedding similarity, word inclusion and entity inclusion) to align script scenes and sum- mary sentences which achieved an F1 score of 0.39. Analyzing our results showed that the bigger the differences in the number of text units being aligned, the more difficult the alignment problem is. We also critiqued of our own similarity scoring techniques and dif- ferent alignment algorithms based on integer linear programming and local optimization and showed their limitations and discussed ideas to improve them.
[10]
A. Abujabal, “Question Answering over Knowledge Bases with Continuous Learning,” Universität des Saarlandes, Saarbrücken, 2019.
Abstract
Answering complex natural language questions with crisp answers is crucial towards satisfying the information needs of advanced users. With the rapid growth of knowledge bases (KBs) such as Yago and Freebase, this goal has become attainable by translating questions into formal queries like SPARQL queries. Such queries can then be evaluated over knowledge bases to retrieve crisp answers. To this end, three research issues arise: (i) how to develop methods that are robust to lexical and syntactic variations in questions and can handle complex questions, (ii) how to design and curate datasets to advance research in question answering, and (iii) how to efficiently identify named entities in questions. In this dissertation, we make the following five contributions in the areas of question answering (QA) and named entity recognition (NER). For issue (i), we make the following contributions: We present QUINT, an approach for answering natural language questions over knowledge bases using automatically learned templates. Templates are an important asset for QA over KBs, simplifying the semantic parsing of input questions and generating formal queries for interpretable answers. QUINT is capable of answering both simple and compositional questions. We introduce NEQA, a framework for continuous learning for QA over KBs. NEQA starts with a small seed of training examples in the form of question-answer pairs, and improves its performance over time. NEQA combines both syntax, through template-based answering, and semantics, via a semantic similarity function. %when templates fail to do so. Moreover, it adapts to the language used after deployment by periodically retraining its underlying models. For issues (i) and (ii), we present TEQUILA, a framework for answering complex questions with explicit and implicit temporal conditions over KBs. TEQUILA is built on a rule-based framework that detects and decomposes temporal questions into simpler sub-questions that can be answered by standard KB-QA systems. TEQUILA reconciles the results of sub-questions into final answers. TEQUILA is accompanied with a dataset called TempQuestions, which consists of 1,271 temporal questions with gold-standard answers over Freebase. This collection is derived by judiciously selecting time-related questions from existing QA datasets. For issue (ii), we publish ComQA, a large-scale manually-curated dataset for QA. ComQA contains questions that represent real information needs and exhibit a wide range of difficulties such as the need for temporal reasoning, comparison, and compositionality. ComQA contains paraphrase clusters of semantically-equivalent questions that can be exploited by QA systems. We harness a combination of community question-answering platforms and crowdsourcing to construct the ComQA dataset. For issue (iii), we introduce a neural network model based on subword units for named entity recognition. The model learns word representations using a combination of characters, bytes and phonemes. While achieving comparable performance with word-level based models, our model has an order-of-magnitude smaller vocabulary size and lower memory requirements, and it handles out-of-vocabulary words.
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BibTeX
@phdthesis{Abujabalphd2013, TITLE = {Question Answering over Knowledge Bases with Continuous Learning}, AUTHOR = {Abujabal, Abdalghani}, LANGUAGE = {eng}, DOI = {10.22028/D291-27968}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, ABSTRACT = {Answering complex natural language questions with crisp answers is crucial towards satisfying the information needs of advanced users. With the rapid growth of knowledge bases (KBs) such as Yago and Freebase, this goal has become attainable by translating questions into formal queries like SPARQL queries. Such queries can then be evaluated over knowledge bases to retrieve crisp answers. To this end, three research issues arise: (i) how to develop methods that are robust to lexical and syntactic variations in questions and can handle complex questions, (ii) how to design and curate datasets to advance research in question answering, and (iii) how to efficiently identify named entities in questions. In this dissertation, we make the following five contributions in the areas of question answering (QA) and named entity recognition (NER). For issue (i), we make the following contributions: We present QUINT, an approach for answering natural language questions over knowledge bases using automatically learned templates. Templates are an important asset for QA over KBs, simplifying the semantic parsing of input questions and generating formal queries for interpretable answers. QUINT is capable of answering both simple and compositional questions. We introduce NEQA, a framework for continuous learning for QA over KBs. NEQA starts with a small seed of training examples in the form of question-answer pairs, and improves its performance over time. NEQA combines both syntax, through template-based answering, and semantics, via a semantic similarity function. %when templates fail to do so. Moreover, it adapts to the language used after deployment by periodically retraining its underlying models. For issues (i) and (ii), we present TEQUILA, a framework for answering complex questions with explicit and implicit temporal conditions over KBs. TEQUILA is built on a rule-based framework that detects and decomposes temporal questions into simpler sub-questions that can be answered by standard KB-QA systems. TEQUILA reconciles the results of sub-questions into final answers. TEQUILA is accompanied with a dataset called TempQuestions, which consists of 1,271 temporal questions with gold-standard answers over Freebase. This collection is derived by judiciously selecting time-related questions from existing QA datasets. For issue (ii), we publish ComQA, a large-scale manually-curated dataset for QA. ComQA contains questions that represent real information needs and exhibit a wide range of difficulties such as the need for temporal reasoning, comparison, and compositionality. ComQA contains paraphrase clusters of semantically-equivalent questions that can be exploited by QA systems. We harness a combination of community question-answering platforms and crowdsourcing to construct the ComQA dataset. For issue (iii), we introduce a neural network model based on subword units for named entity recognition. The model learns word representations using a combination of characters, bytes and phonemes. While achieving comparable performance with word-level based models, our model has an order-of-magnitude smaller vocabulary size and lower memory requirements, and it handles out-of-vocabulary words.}, }
Endnote
%0 Thesis %A Abujabal, Abdalghani %Y Weikum, Gerhard %A referee: Linn, Jimmy %A referee: Berberich, Klaus %+ Databases and Information Systems, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Question Answering over Knowledge Bases with Continuous Learning : %G eng %U http://hdl.handle.net/21.11116/0000-0003-AEC0-0 %R 10.22028/D291-27968 %I Universität des Saarlandes %C Saarbrücken %D 2019 %P 141 p. %V phd %9 phd %X Answering complex natural language questions with crisp answers is crucial towards satisfying the information needs of advanced users. With the rapid growth of knowledge bases (KBs) such as Yago and Freebase, this goal has become attainable by translating questions into formal queries like SPARQL queries. Such queries can then be evaluated over knowledge bases to retrieve crisp answers. To this end, three research issues arise: (i) how to develop methods that are robust to lexical and syntactic variations in questions and can handle complex questions, (ii) how to design and curate datasets to advance research in question answering, and (iii) how to efficiently identify named entities in questions. In this dissertation, we make the following five contributions in the areas of question answering (QA) and named entity recognition (NER). For issue (i), we make the following contributions: We present QUINT, an approach for answering natural language questions over knowledge bases using automatically learned templates. Templates are an important asset for QA over KBs, simplifying the semantic parsing of input questions and generating formal queries for interpretable answers. QUINT is capable of answering both simple and compositional questions. We introduce NEQA, a framework for continuous learning for QA over KBs. NEQA starts with a small seed of training examples in the form of question-answer pairs, and improves its performance over time. NEQA combines both syntax, through template-based answering, and semantics, via a semantic similarity function. %when templates fail to do so. Moreover, it adapts to the language used after deployment by periodically retraining its underlying models. For issues (i) and (ii), we present TEQUILA, a framework for answering complex questions with explicit and implicit temporal conditions over KBs. TEQUILA is built on a rule-based framework that detects and decomposes temporal questions into simpler sub-questions that can be answered by standard KB-QA systems. TEQUILA reconciles the results of sub-questions into final answers. TEQUILA is accompanied with a dataset called TempQuestions, which consists of 1,271 temporal questions with gold-standard answers over Freebase. This collection is derived by judiciously selecting time-related questions from existing QA datasets. For issue (ii), we publish ComQA, a large-scale manually-curated dataset for QA. ComQA contains questions that represent real information needs and exhibit a wide range of difficulties such as the need for temporal reasoning, comparison, and compositionality. ComQA contains paraphrase clusters of semantically-equivalent questions that can be exploited by QA systems. We harness a combination of community question-answering platforms and crowdsourcing to construct the ComQA dataset. For issue (iii), we introduce a neural network model based on subword units for named entity recognition. The model learns word representations using a combination of characters, bytes and phonemes. While achieving comparable performance with word-level based models, our model has an order-of-magnitude smaller vocabulary size and lower memory requirements, and it handles out-of-vocabulary words. %U https://publikationen.sulb.uni-saarland.de/handle/20.500.11880/27438
[11]
A. Abujabal, R. Saha Roy, M. Yahya, and G. Weikum, “ComQA: A Community-sourced Dataset for Complex Factoid Question Answering with Paraphrase Clusters,” in The 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL HLT 2019), Minneapolis, MN, USA, 2019.
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@inproceedings{abujabal19comqa, TITLE = {{ComQA}: {A} Community-sourced Dataset for Complex Factoid Question Answering with Paraphrase Clusters}, AUTHOR = {Abujabal, Abdalghani and Saha Roy, Rishiraj and Yahya, Mohamed and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-950737-13-0}, URL = {https://www.aclweb.org/anthology/N19-1027}, PUBLISHER = {ACL}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {The 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL HLT 2019)}, EDITOR = {Burstein, Jill and Doran, Christy and Solorio, Thamar}, PAGES = {307--317}, ADDRESS = {Minneapolis, MN, USA}, }
Endnote
%0 Conference Proceedings %A Abujabal, Abdalghani %A Saha Roy, Rishiraj %A Yahya, Mohamed %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T ComQA: A Community-sourced Dataset for Complex Factoid Question Answering with Paraphrase Clusters : %G eng %U http://hdl.handle.net/21.11116/0000-0003-11A7-D %U https://www.aclweb.org/anthology/N19-1027 %D 2019 %B Annual Conference of the North American Chapter of the Association for Computational Linguistics %Z date of event: 2019-06-02 - 2019-06-07 %C Minneapolis, MN, USA %B The 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies %E Burstein, Jill; Doran, Christy; Solorio, Thamar %P 307 - 317 %I ACL %@ 978-1-950737-13-0 %U https://www.aclweb.org/anthology/N19-1027
[12]
M. Alikhani, S. Nag Chowdhury, G. de Melo, and M. Stone, “CITE: A Corpus Of Text-Image Discourse Relations,” in Proceedings of the 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT 2019), Minneapolis, MN, USA, 2019.
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@inproceedings{AlikhaniEtAl2019CITETextImageDiscourse, TITLE = {{CITE}: {A} Corpus Of Text-Image Discourse Relations}, AUTHOR = {Alikhani, Malihe and Nag Chowdhury, Sreyasi and de Melo, Gerard and Stone, Matthew}, LANGUAGE = {eng}, ISBN = {978-1-950737-13-0}, PUBLISHER = {ACL}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of the 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT 2019)}, EDITOR = {Burstein, Jill and Doran, Christy and Solorio, Thamar}, PAGES = {570--575}, ADDRESS = {Minneapolis, MN, USA}, }
Endnote
%0 Conference Proceedings %A Alikhani, Malihe %A Nag Chowdhury, Sreyasi %A de Melo, Gerard %A Stone, Matthew %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T CITE: A Corpus Of Text-Image Discourse Relations : %G eng %U http://hdl.handle.net/21.11116/0000-0003-78D8-3 %D 2019 %B Annual Conference of the North American Chapter of the Association for Computational Linguistics %Z date of event: 2019-06-02 - 2019-06-07 %C Minneapolis, MN, USA %B Proceedings of the 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics %E Burstein, Jill; Doran, Christy; Solorio, Thamar %P 570 - 575 %I ACL %@ 978-1-950737-13-0 %U https://aclweb.org/anthology/papers/N/N19/N19-1056/
[13]
S. Arora and A. Yates, “Investigating Retrieval Method Selection with Axiomatic Features,” in Proceedings of the 1st Interdisciplinary Workshop on Algorithm Selection and Meta-Learning in Information Retrieval co-located with the 41st European Conference on Information Retrieval (ECIR 2019) (AMIR 2019), Cologne, Germany, 2019.
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@inproceedings{Arora_AMIR2019, TITLE = {Investigating Retrieval Method Selection with Axiomatic Features}, AUTHOR = {Arora, Siddhant and Yates, Andrew}, LANGUAGE = {eng}, ISSN = {1613-0073}, URL = {urn:nbn:de:0074-2360-3}, PUBLISHER = {CEUR-WS}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of the 1st Interdisciplinary Workshop on Algorithm Selection and Meta-Learning in Information Retrieval co-located with the 41st European Conference on Information Retrieval (ECIR 2019) (AMIR 2019)}, EDITOR = {Beel, Joeran and Kolthoff, Lars}, PAGES = {18--31}, EID = {4}, SERIES = {CEUR Workshop Proceedings}, VOLUME = {2360}, ADDRESS = {Cologne, Germany}, }
Endnote
%0 Conference Proceedings %A Arora, Siddhant %A Yates, Andrew %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Investigating Retrieval Method Selection with Axiomatic Features : %G eng %U http://hdl.handle.net/21.11116/0000-0004-028E-A %D 2019 %B The 1st Interdisciplinary Workshop on Algorithm Selection and Meta-Learning in Information Retrieval %Z date of event: 2019-04-14 - 2019-04-14 %C Cologne, Germany %B Proceedings of the 1st Interdisciplinary Workshop on Algorithm Selection and Meta-Learning in Information Retrieval co-located with the 41st European Conference on Information Retrieval (ECIR 2019) %E Beel, Joeran; Kolthoff, Lars %P 18 - 31 %Z sequence number: 4 %I CEUR-WS %B CEUR Workshop Proceedings %N 2360 %@ false %U http://ceur-ws.org/Vol-2360/paper4Axiomatic.pdf
[14]
S. Arora and A. Yates, “Investigating Retrieval Method Selection with Axiomatic Features,” 2019. [Online]. Available: http://arxiv.org/abs/1904.05737. (arXiv: 1904.05737)
Abstract
We consider algorithm selection in the context of ad-hoc information retrieval. Given a query and a pair of retrieval methods, we propose a meta-learner that predicts how to combine the methods' relevance scores into an overall relevance score. Inspired by neural models' different properties with regard to IR axioms, these predictions are based on features that quantify axiom-related properties of the query and its top ranked documents. We conduct an evaluation on TREC Web Track data and find that the meta-learner often significantly improves over the individual methods. Finally, we conduct feature and query weight analyses to investigate the meta-learner's behavior.
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@online{Arora_arXiv1904.05737, TITLE = {Investigating Retrieval Method Selection with Axiomatic Features}, AUTHOR = {Arora, Siddhant and Yates, Andrew}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1904.05737}, EPRINT = {1904.05737}, EPRINTTYPE = {arXiv}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, ABSTRACT = {We consider algorithm selection in the context of ad-hoc information retrieval. Given a query and a pair of retrieval methods, we propose a meta-learner that predicts how to combine the methods' relevance scores into an overall relevance score. Inspired by neural models' different properties with regard to IR axioms, these predictions are based on features that quantify axiom-related properties of the query and its top ranked documents. We conduct an evaluation on TREC Web Track data and find that the meta-learner often significantly improves over the individual methods. Finally, we conduct feature and query weight analyses to investigate the meta-learner's behavior.}, }
Endnote
%0 Report %A Arora, Siddhant %A Yates, Andrew %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Investigating Retrieval Method Selection with Axiomatic Features : %G eng %U http://hdl.handle.net/21.11116/0000-0004-02BF-3 %U http://arxiv.org/abs/1904.05737 %D 2019 %X We consider algorithm selection in the context of ad-hoc information retrieval. Given a query and a pair of retrieval methods, we propose a meta-learner that predicts how to combine the methods' relevance scores into an overall relevance score. Inspired by neural models' different properties with regard to IR axioms, these predictions are based on features that quantify axiom-related properties of the query and its top ranked documents. We conduct an evaluation on TREC Web Track data and find that the meta-learner often significantly improves over the individual methods. Finally, we conduct feature and query weight analyses to investigate the meta-learner's behavior. %K Computer Science, Information Retrieval, cs.IR
[15]
J. A. Biega, “Enhancing Privacy and Fairness in Search Systems,” Universität des Saarlandes, Saarbrücken, 2019.
Abstract
Following a period of expedited progress in the capabilities of digital systems, the society begins to realize that systems designed to assist people in various tasks can also harm individuals and society. Mediating access to information and explicitly or implicitly ranking people in increasingly many applications, search systems have a substantial potential to contribute to such unwanted outcomes. Since they collect vast amounts of data about both searchers and search subjects, they have the potential to violate the privacy of both of these groups of users. Moreover, in applications where rankings influence people's economic livelihood outside of the platform, such as sharing economy or hiring support websites, search engines have an immense economic power over their users in that they control user exposure in ranked results. This thesis develops new models and methods broadly covering different aspects of privacy and fairness in search systems for both searchers and search subjects. Specifically, it makes the following contributions: (1) We propose a model for computing individually fair rankings where search subjects get exposure proportional to their relevance. The exposure is amortized over time using constrained optimization to overcome searcher attention biases while preserving ranking utility. (2) We propose a model for computing sensitive search exposure where each subject gets to know the sensitive queries that lead to her profile in the top-k search results. The problem of finding exposing queries is technically modeled as reverse nearest neighbor search, followed by a weekly-supervised learning to rank model ordering the queries by privacy-sensitivity. (3) We propose a model for quantifying privacy risks from textual data in online communities. The method builds on a topic model where each topic is annotated by a crowdsourced sensitivity score, and privacy risks are associated with a user's relevance to sensitive topics. We propose relevance measures capturing different dimensions of user interest in a topic and show how they correlate with human risk perceptions. (4) We propose a model for privacy-preserving personalized search where search queries of different users are split and merged into synthetic profiles. The model mediates the privacy-utility trade-off by keeping semantically coherent fragments of search histories within individual profiles, while trying to minimize the similarity of any of the synthetic profiles to the original user profiles. The models are evaluated using information retrieval techniques and user studies over a variety of datasets, ranging from query logs, through social media and community question answering postings, to item listings from sharing economy platforms.
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@phdthesis{biegaphd2019, TITLE = {Enhancing Privacy and Fairness in Search Systems}, AUTHOR = {Biega, Joanna Asia}, LANGUAGE = {eng}, URL = {urn:nbn:de:bsz:291--ds-278861}, DOI = {10.22028/D291-27886}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, ABSTRACT = {Following a period of expedited progress in the capabilities of digital systems, the society begins to realize that systems designed to assist people in various tasks can also harm individuals and society. Mediating access to information and explicitly or implicitly ranking people in increasingly many applications, search systems have a substantial potential to contribute to such unwanted outcomes. Since they collect vast amounts of data about both searchers and search subjects, they have the potential to violate the privacy of both of these groups of users. Moreover, in applications where rankings influence people's economic livelihood outside of the platform, such as sharing economy or hiring support websites, search engines have an immense economic power over their users in that they control user exposure in ranked results. This thesis develops new models and methods broadly covering different aspects of privacy and fairness in search systems for both searchers and search subjects. Specifically, it makes the following contributions: (1) We propose a model for computing individually fair rankings where search subjects get exposure proportional to their relevance. The exposure is amortized over time using constrained optimization to overcome searcher attention biases while preserving ranking utility. (2) We propose a model for computing sensitive search exposure where each subject gets to know the sensitive queries that lead to her profile in the top-k search results. The problem of finding exposing queries is technically modeled as reverse nearest neighbor search, followed by a weekly-supervised learning to rank model ordering the queries by privacy-sensitivity. (3) We propose a model for quantifying privacy risks from textual data in online communities. The method builds on a topic model where each topic is annotated by a crowdsourced sensitivity score, and privacy risks are associated with a user's relevance to sensitive topics. We propose relevance measures capturing different dimensions of user interest in a topic and show how they correlate with human risk perceptions. (4) We propose a model for privacy-preserving personalized search where search queries of different users are split and merged into synthetic profiles. The model mediates the privacy-utility trade-off by keeping semantically coherent fragments of search histories within individual profiles, while trying to minimize the similarity of any of the synthetic profiles to the original user profiles. The models are evaluated using information retrieval techniques and user studies over a variety of datasets, ranging from query logs, through social media and community question answering postings, to item listings from sharing economy platforms.}, }
Endnote
%0 Thesis %A Biega, Joanna Asia %Y Weikum, Gerhard %A referee: Gummadi, Krishna %A referee: Nejdl, Wolfgang %+ International Max Planck Research School, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Group K. Gummadi, Max Planck Institute for Software Systems, Max Planck Society External Organizations %T Enhancing Privacy and Fairness in Search Systems : %G eng %U http://hdl.handle.net/21.11116/0000-0003-9AED-5 %R 10.22028/D291-27886 %U urn:nbn:de:bsz:291--ds-278861 %I Universität des Saarlandes %C Saarbrücken %D 2019 %P 111 p. %V phd %9 phd %X Following a period of expedited progress in the capabilities of digital systems, the society begins to realize that systems designed to assist people in various tasks can also harm individuals and society. Mediating access to information and explicitly or implicitly ranking people in increasingly many applications, search systems have a substantial potential to contribute to such unwanted outcomes. Since they collect vast amounts of data about both searchers and search subjects, they have the potential to violate the privacy of both of these groups of users. Moreover, in applications where rankings influence people's economic livelihood outside of the platform, such as sharing economy or hiring support websites, search engines have an immense economic power over their users in that they control user exposure in ranked results. This thesis develops new models and methods broadly covering different aspects of privacy and fairness in search systems for both searchers and search subjects. Specifically, it makes the following contributions: (1) We propose a model for computing individually fair rankings where search subjects get exposure proportional to their relevance. The exposure is amortized over time using constrained optimization to overcome searcher attention biases while preserving ranking utility. (2) We propose a model for computing sensitive search exposure where each subject gets to know the sensitive queries that lead to her profile in the top-k search results. The problem of finding exposing queries is technically modeled as reverse nearest neighbor search, followed by a weekly-supervised learning to rank model ordering the queries by privacy-sensitivity. (3) We propose a model for quantifying privacy risks from textual data in online communities. The method builds on a topic model where each topic is annotated by a crowdsourced sensitivity score, and privacy risks are associated with a user's relevance to sensitive topics. We propose relevance measures capturing different dimensions of user interest in a topic and show how they correlate with human risk perceptions. (4) We propose a model for privacy-preserving personalized search where search queries of different users are split and merged into synthetic profiles. The model mediates the privacy-utility trade-off by keeping semantically coherent fragments of search histories within individual profiles, while trying to minimize the similarity of any of the synthetic profiles to the original user profiles. The models are evaluated using information retrieval techniques and user studies over a variety of datasets, ranging from query logs, through social media and community question answering postings, to item listings from sharing economy platforms. %U https://publikationen.sulb.uni-saarland.de/handle/20.500.11880/27389
[16]
A. Chakraborty, N. Mota, A. J. Biega, K. P. Gummadi, and H. Heidari, “On the Impact of Choice Architectures on Inequality in Online Donation Platforms,” in Proceedings of The World Wide Web Conference (WWW 2019), San Francisco, CA, USA, 2019.
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@inproceedings{Chakraborty_WWW2019, TITLE = {On the Impact of Choice Architectures on Inequality in Online Donation Platforms}, AUTHOR = {Chakraborty, Abhijnan and Mota, Nuno and Biega, Asia J. and Gummadi, Krishna P. and Heidari, Hoda}, LANGUAGE = {eng}, ISBN = {978-1-4503-6674-8}, DOI = {10.1145/3308558.3313663}, PUBLISHER = {ACM}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, BOOKTITLE = {Proceedings of The World Wide Web Conference (WWW 2019)}, PAGES = {2623--2629}, ADDRESS = {San Francisco, CA, USA}, }
Endnote
%0 Conference Proceedings %A Chakraborty, Abhijnan %A Mota, Nuno %A Biega, Asia J. %A Gummadi, Krishna P. %A Heidari, Hoda %+ External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T On the Impact of Choice Architectures on Inequality in Online Donation Platforms : %G eng %U http://hdl.handle.net/21.11116/0000-0002-FC88-9 %R 10.1145/3308558.3313663 %D 2019 %B The Web Conference %Z date of event: 2019-05-13 - 2019-05-17 %C San Francisco, CA, USA %B Proceedings of The World Wide Web Conference %P 2623 - 2629 %I ACM %@ 978-1-4503-6674-8
[17]
F. Chierichetti, R. Kumar, A. Panconesi, and E. Terolli, “On the Distortion of Locality Sensitive Hashing,” SIAM Journal on Computing, vol. 48, no. 2, 2019.
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@article{Chierichetti2019, TITLE = {On the Distortion of Locality Sensitive Hashing}, AUTHOR = {Chierichetti, Flavio and Kumar, Ravi and Panconesi, Alessandro and Terolli, Erisa}, LANGUAGE = {eng}, ISSN = {0097-5397}, DOI = {10.1137/17M1127752}, PUBLISHER = {SIAM}, ADDRESS = {Philadelphia, PA}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, JOURNAL = {SIAM Journal on Computing}, VOLUME = {48}, NUMBER = {2}, PAGES = {350--372}, }
Endnote
%0 Journal Article %A Chierichetti, Flavio %A Kumar, Ravi %A Panconesi, Alessandro %A Terolli, Erisa %+ External Organizations External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T On the Distortion of Locality Sensitive Hashing : %G eng %U http://hdl.handle.net/21.11116/0000-0003-A7E7-C %R 10.1137/17M1127752 %7 2019 %D 2019 %J SIAM Journal on Computing %V 48 %N 2 %& 350 %P 350 - 372 %I SIAM %C Philadelphia, PA %@ false
[18]
P. Christmann, R. Saha Roy, A. Abujabal, J. Singh, and G. Weikum, “Look before you Hop: Conversational Question Answering over Knowledge Graphs Using Judicious Context Expansion,” 2019. [Online]. Available: http://arxiv.org/abs/1910.03262. (arXiv: 1910.03262)
Abstract
Fact-centric information needs are rarely one-shot; users typically ask follow-up questions to explore a topic. In such a conversational setting, the user's inputs are often incomplete, with entities or predicates left out, and ungrammatical phrases. This poses a huge challenge to question answering (QA) systems that typically rely on cues in full-fledged interrogative sentences. As a solution, we develop CONVEX: an unsupervised method that can answer incomplete questions over a knowledge graph (KG) by maintaining conversation context using entities and predicates seen so far and automatically inferring missing or ambiguous pieces for follow-up questions. The core of our method is a graph exploration algorithm that judiciously expands a frontier to find candidate answers for the current question. To evaluate CONVEX, we release ConvQuestions, a crowdsourced benchmark with 11,200 distinct conversations from five different domains. We show that CONVEX: (i) adds conversational support to any stand-alone QA system, and (ii) outperforms state-of-the-art baselines and question completion strategies.
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@online{Christmann_arXiv1910.03262, TITLE = {Look before you Hop: Conversational Question Answering over Knowledge Graphs Using Judicious Context Expansion}, AUTHOR = {Christmann, Phlipp and Saha Roy, Rishiraj and Abujabal, Abdalghani and Singh, Jyotsna and Weikum, Gerhard}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1910.03262}, EPRINT = {1910.03262}, EPRINTTYPE = {arXiv}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Fact-centric information needs are rarely one-shot; users typically ask follow-up questions to explore a topic. In such a conversational setting, the user's inputs are often incomplete, with entities or predicates left out, and ungrammatical phrases. This poses a huge challenge to question answering (QA) systems that typically rely on cues in full-fledged interrogative sentences. As a solution, we develop CONVEX: an unsupervised method that can answer incomplete questions over a knowledge graph (KG) by maintaining conversation context using entities and predicates seen so far and automatically inferring missing or ambiguous pieces for follow-up questions. The core of our method is a graph exploration algorithm that judiciously expands a frontier to find candidate answers for the current question. To evaluate CONVEX, we release ConvQuestions, a crowdsourced benchmark with 11,200 distinct conversations from five different domains. We show that CONVEX: (i) adds conversational support to any stand-alone QA system, and (ii) outperforms state-of-the-art baselines and question completion strategies.}, }
Endnote
%0 Report %A Christmann, Phlipp %A Saha Roy, Rishiraj %A Abujabal, Abdalghani %A Singh, Jyotsna %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Look before you Hop: Conversational Question Answering over Knowledge Graphs Using Judicious Context Expansion : %G eng %U http://hdl.handle.net/21.11116/0000-0005-83DC-F %U http://arxiv.org/abs/1910.03262 %D 2019 %X Fact-centric information needs are rarely one-shot; users typically ask follow-up questions to explore a topic. In such a conversational setting, the user's inputs are often incomplete, with entities or predicates left out, and ungrammatical phrases. This poses a huge challenge to question answering (QA) systems that typically rely on cues in full-fledged interrogative sentences. As a solution, we develop CONVEX: an unsupervised method that can answer incomplete questions over a knowledge graph (KG) by maintaining conversation context using entities and predicates seen so far and automatically inferring missing or ambiguous pieces for follow-up questions. The core of our method is a graph exploration algorithm that judiciously expands a frontier to find candidate answers for the current question. To evaluate CONVEX, we release ConvQuestions, a crowdsourced benchmark with 11,200 distinct conversations from five different domains. We show that CONVEX: (i) adds conversational support to any stand-alone QA system, and (ii) outperforms state-of-the-art baselines and question completion strategies. %K Computer Science, Information Retrieval, cs.IR,Computer Science, Computation and Language, cs.CL
[19]
P. Christmann, R. Saha Roy, A. Abujabal, J. Singh, and G. Weikum, “Look before you Hop: Conversational Question Answering over Knowledge Graphs Using Judicious Context Expansion,” in CIKM’19, 28th ACM International Conference on Information and Knowledge Management, Beijing China, 2019.
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@inproceedings{Christmann_CIKM2019, TITLE = {Look before you Hop: {C}onversational Question Answering over Knowledge Graphs Using Judicious Context Expansion}, AUTHOR = {Christmann, Phlipp and Saha Roy, Rishiraj and Abujabal, Abdalghani and Singh, Jyotsna and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {9781450369763}, DOI = {10.1145/3357384.3358016}, PUBLISHER = {ACM}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, BOOKTITLE = {CIKM'19, 28th ACM International Conference on Information and Knowledge Management}, EDITOR = {Zhu, Wenwu and Tao, Dacheng}, PAGES = {729--738}, ADDRESS = {Beijing China}, }
Endnote
%0 Conference Proceedings %A Christmann, Phlipp %A Saha Roy, Rishiraj %A Abujabal, Abdalghani %A Singh, Jyotsna %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Look before you Hop: Conversational Question Answering over Knowledge Graphs Using Judicious Context Expansion : %G eng %U http://hdl.handle.net/21.11116/0000-0005-8231-0 %R 10.1145/3357384.3358016 %D 2019 %B 28th ACM International Conference on Information and Knowledge Management %Z date of event: 2019-11-03 - 2019-11-07 %C Beijing China %B CIKM'19 %E Zhu, Wenwu; Tao, Dacheng %P 729 - 738 %I ACM %@ 9781450369763
[20]
C. X. Chu, S. Razniewski, and G. Weikum, “TiFi: Taxonomy Induction for Fictional Domains [Extended version],” 2019. [Online]. Available: http://arxiv.org/abs/1901.10263. (arXiv: 1901.10263)
Abstract
Taxonomies are important building blocks of structured knowledge bases, and their construction from text sources and Wikipedia has received much attention. In this paper we focus on the construction of taxonomies for fictional domains, using noisy category systems from fan wikis or text extraction as input. Such fictional domains are archetypes of entity universes that are poorly covered by Wikipedia, such as also enterprise-specific knowledge bases or highly specialized verticals. Our fiction-targeted approach, called TiFi, consists of three phases: (i) category cleaning, by identifying candidate categories that truly represent classes in the domain of interest, (ii) edge cleaning, by selecting subcategory relationships that correspond to class subsumption, and (iii) top-level construction, by mapping classes onto a subset of high-level WordNet categories. A comprehensive evaluation shows that TiFi is able to construct taxonomies for a diverse range of fictional domains such as Lord of the Rings, The Simpsons or Greek Mythology with very high precision and that it outperforms state-of-the-art baselines for taxonomy induction by a substantial margin.
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@online{Chu_arXIv1901.10263, TITLE = {{TiFi}: Taxonomy Induction for Fictional Domains [Extended version]}, AUTHOR = {Chu, Cuong Xuan and Razniewski, Simon and Weikum, Gerhard}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1901.10263}, EPRINT = {1901.10263}, EPRINTTYPE = {arXiv}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Taxonomies are important building blocks of structured knowledge bases, and their construction from text sources and Wikipedia has received much attention. In this paper we focus on the construction of taxonomies for fictional domains, using noisy category systems from fan wikis or text extraction as input. Such fictional domains are archetypes of entity universes that are poorly covered by Wikipedia, such as also enterprise-specific knowledge bases or highly specialized verticals. Our fiction-targeted approach, called TiFi, consists of three phases: (i) category cleaning, by identifying candidate categories that truly represent classes in the domain of interest, (ii) edge cleaning, by selecting subcategory relationships that correspond to class subsumption, and (iii) top-level construction, by mapping classes onto a subset of high-level WordNet categories. A comprehensive evaluation shows that TiFi is able to construct taxonomies for a diverse range of fictional domains such as Lord of the Rings, The Simpsons or Greek Mythology with very high precision and that it outperforms state-of-the-art baselines for taxonomy induction by a substantial margin.}, }
Endnote
%0 Report %A Chu, Cuong Xuan %A Razniewski, Simon %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T TiFi: Taxonomy Induction for Fictional Domains [Extended version] : %G eng %U http://hdl.handle.net/21.11116/0000-0003-FE67-C %U http://arxiv.org/abs/1901.10263 %D 2019 %X Taxonomies are important building blocks of structured knowledge bases, and their construction from text sources and Wikipedia has received much attention. In this paper we focus on the construction of taxonomies for fictional domains, using noisy category systems from fan wikis or text extraction as input. Such fictional domains are archetypes of entity universes that are poorly covered by Wikipedia, such as also enterprise-specific knowledge bases or highly specialized verticals. Our fiction-targeted approach, called TiFi, consists of three phases: (i) category cleaning, by identifying candidate categories that truly represent classes in the domain of interest, (ii) edge cleaning, by selecting subcategory relationships that correspond to class subsumption, and (iii) top-level construction, by mapping classes onto a subset of high-level WordNet categories. A comprehensive evaluation shows that TiFi is able to construct taxonomies for a diverse range of fictional domains such as Lord of the Rings, The Simpsons or Greek Mythology with very high precision and that it outperforms state-of-the-art baselines for taxonomy induction by a substantial margin. %K Computer Science, Computation and Language, cs.CL,Computer Science, Artificial Intelligence, cs.AI,Computer Science, Information Retrieval, cs.IR
[21]
C. X. Chu, S. Razniewski, and G. Weikum, “TiFi: Taxonomy Induction for Fictional Domains,” in Proceedings of The World Wide Web Conference (WWW 2019), San Francisco, CA, USA, 2019.
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@inproceedings{Chu_WWW2019, TITLE = {{TiFi}: {T}axonomy Induction for Fictional Domains}, AUTHOR = {Chu, Cuong Xuan and Razniewski, Simon and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-6674-8}, DOI = {10.1145/3308558.3313519}, PUBLISHER = {ACM}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of The World Wide Web Conference (WWW 2019)}, EDITOR = {McAuley, Julian}, PAGES = {2673--2679}, ADDRESS = {San Francisco, CA, USA}, }
Endnote
%0 Conference Proceedings %A Chu, Cuong Xuan %A Razniewski, Simon %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T TiFi: Taxonomy Induction for Fictional Domains : %G eng %U http://hdl.handle.net/21.11116/0000-0003-6558-9 %R 10.1145/3308558.3313519 %D 2019 %B The Web Conference %Z date of event: 2019-05-13 - 2019-05-17 %C San Francisco, CA, USA %B Proceedings of The World Wide Web Conference %E McAuley, Julian %P 2673 - 2679 %I ACM %@ 978-1-4503-6674-8
[22]
I. Dikeoulias, J. Strötgen, and S. Razniewski, “Epitaph or Breaking News? Analyzing and Predicting the Stability of Knowledge Base Properties,” in Companion of The World Wide Web Conference (WWW 2019), San Francisco, CA, USA, 2019.
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@inproceedings{Dikeoulias_WWW2019, TITLE = {Epitaph or Breaking News? {A}nalyzing and Predicting the Stability of Knowledge Base Properties}, AUTHOR = {Dikeoulias, Ioannis and Str{\"o}tgen, Jannik and Razniewski, Simon}, LANGUAGE = {eng}, ISBN = {978-1-4503-6675-5}, DOI = {10.1145/3308560.3314998}, PUBLISHER = {ACM}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Companion of The World Wide Web Conference (WWW 2019)}, EDITOR = {McAuley, Julian}, PAGES = {1155--1158}, ADDRESS = {San Francisco, CA, USA}, }
Endnote
%0 Conference Proceedings %A Dikeoulias, Ioannis %A Strötgen, Jannik %A Razniewski, Simon %+ External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Epitaph or Breaking News? Analyzing and Predicting the Stability of Knowledge Base Properties : %G eng %U http://hdl.handle.net/21.11116/0000-0004-0281-7 %R 10.1145/3308560.3314998 %D 2019 %B The Web Conference %Z date of event: 2019-05-13 - 2019-05-17 %C San Francisco, CA, USA %B Companion of The World Wide Web Conference %E McAuley, Julian %P 1155 - 1158 %I ACM %@ 978-1-4503-6675-5
[23]
P. Ernst, E. Terolli, and G. Weikum, “LongLife: a Platform for Personalized Searchfor Health and Life Sciences,” in Proceedings of the ISWC 2019 Satellite Tracks (Posters & Demonstrations, Industry, and Outrageous Ideas) co-located with 18th International Semantic Web Conference (ISWC 2019) (ISWC 2019 Satellites), Auckland, New Zealand, 2019.
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@inproceedings{Ernst_ISWC2019, TITLE = {{LongLife}: a Platform for Personalized Searchfor Health and Life Sciences}, AUTHOR = {Ernst, Patrick and Terolli, Erisa and Weikum, Gerhard}, LANGUAGE = {eng}, ISSN = {1613-0073}, URL = {http://ceur-ws.org/Vol-2456/paper62.pdf; urn:nbn:de:0074-2456-4}, PUBLISHER = {ceur-ws.org}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of the ISWC 2019 Satellite Tracks (Posters \& Demonstrations, Industry, and Outrageous Ideas) co-located with 18th International Semantic Web Conference (ISWC 2019) (ISWC 2019 Satellites)}, EDITOR = {Su{\'a}rez-Figueroa, Mari Carmen and Cheng, Gong and Gentile, Anna Lisa and Gu{\'e}ret, Christophe and Keet, Maria and Bernstein, Abraham}, PAGES = {237--240}, EID = {62}, SERIES = {CEUR Workshop Proceedings}, VOLUME = {2456}, ADDRESS = {Auckland, New Zealand}, }
Endnote
%0 Conference Proceedings %A Ernst, Patrick %A Terolli, Erisa %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T LongLife: a Platform for Personalized Searchfor Health and Life Sciences : %G eng %U http://hdl.handle.net/21.11116/0000-0005-83A6-B %U http://ceur-ws.org/Vol-2456/paper62.pdf %D 2019 %B 18th Semantic Web Conference %Z date of event: 2019-10-26 - 2019-10-30 %C Auckland, New Zealand %B Proceedings of the ISWC 2019 Satellite Tracks (Posters & Demonstrations, Industry, and Outrageous Ideas) co-located with 18th International Semantic Web Conference (ISWC 2019) %E Suárez-Figueroa, Mari Carmen; Cheng, Gong; Gentile, Anna Lisa; Guéret, Christophe; Keet, Maria; Bernstein, Abraham %P 237 - 240 %Z sequence number: 62 %I ceur-ws.org %B CEUR Workshop Proceedings %N 2456 %@ false
[24]
M. H. Gad-Elrab, D. Stepanova, J. Urbani, and G. Weikum, “Tracy: Tracing Facts over Knowledge Graphs and Text,” in Proceedings of The World Wide Web Conference (WWW 2019), San Francisco, CA, USA, 2019.
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@inproceedings{Gad-Elrab_WWW2019, TITLE = {Tracy: {T}racing Facts over Knowledge Graphs and Text}, AUTHOR = {Gad-Elrab, Mohamed Hassan and Stepanova, Daria and Urbani, Jacopo and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-6674-8}, DOI = {10.1145/3308558.3314126}, PUBLISHER = {ACM}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of The World Wide Web Conference (WWW 2019)}, EDITOR = {McAuley, Julian}, PAGES = {3516--3520}, ADDRESS = {San Francisco, CA, USA}, }
Endnote
%0 Conference Proceedings %A Gad-Elrab, Mohamed Hassan %A Stepanova, Daria %A Urbani, Jacopo %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Tracy: Tracing Facts over Knowledge Graphs and Text : %G eng %U http://hdl.handle.net/21.11116/0000-0003-08AA-5 %R 10.1145/3308558.3314126 %D 2019 %B The Web Conference %Z date of event: 2019-05-13 - 2019-05-17 %C San Francisco, CA, USA %B Proceedings of The World Wide Web Conference %E McAuley, Julian %P 3516 - 3520 %I ACM %@ 978-1-4503-6674-8
[25]
M. H. Gad-Elrab, D. Stepanova, J. Urbani, and G. Weikum, “ExFaKT: A Framework for Explaining Facts over Knowledge Graphs and Text ,” in WSDM’19, 12h ACM International Conference on Web Search and Data Mining, Melbourne, Australia, 2019.
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@inproceedings{Gad-Elrab_WSDM2019, TITLE = {{ExFaKT}: {A} Framework for Explaining Facts over Knowledge Graphs and Text}, AUTHOR = {Gad-Elrab, Mohamed Hassan and Stepanova, Daria and Urbani, Jacopo and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-5940-5}, DOI = {10.1145/3289600.3290996}, PUBLISHER = {ACM}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, BOOKTITLE = {WSDM'19, 12h ACM International Conference on Web Search and Data Mining}, PAGES = {87--95}, ADDRESS = {Melbourne, Australia}, }
Endnote
%0 Conference Proceedings %A Gad-Elrab, Mohamed Hassan %A Stepanova, Daria %A Urbani, Jacopo %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T ExFaKT: A Framework for Explaining Facts over Knowledge Graphs and Text  : %G eng %U http://hdl.handle.net/21.11116/0000-0002-9C44-2 %R 10.1145/3289600.3290996 %D 2019 %B 12h ACM International Conference on Web Search and Data Mining %Z date of event: 2019-02-11 - 2019-02-15 %C Melbourne, Australia %B WSDM'19 %P 87 - 95 %I ACM %@ 978-1-4503-5940-5
[26]
A. Ghazimatin, O. Balalau, R. Saha Roy, and G. Weikum, “PRINCE: Provider-side Interpretability with Counterfactual Explanations in Recommender Systems,” 2019. [Online]. Available: http://arxiv.org/abs/1911.08378. (arXiv: 1911.08378)
Abstract
Interpretable explanations for recommender systems and other machine learning models are crucial to gain user trust. Prior works that have focused on paths connecting users and items in a heterogeneous network have several limitations, such as discovering relationships rather than true explanations, or disregarding other users' privacy. In this work, we take a fresh perspective, and present PRINCE: a provider-side mechanism to produce tangible explanations for end-users, where an explanation is defined to be a set of minimal actions performed by the user that, if removed, changes the recommendation to a different item. Given a recommendation, PRINCE uses a polynomial-time optimal algorithm for finding this minimal set of a user's actions from an exponential search space, based on random walks over dynamic graphs. Experiments on two real-world datasets show that PRINCE provides more compact explanations than intuitive baselines, and insights from a crowdsourced user-study demonstrate the viability of such action-based explanations. We thus posit that PRINCE produces scrutable, actionable, and concise explanations, owing to its use of counterfactual evidence, a user's own actions, and minimal sets, respectively.
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@online{Ghazimatin_arXiv1911.08378, TITLE = {{PRINCE}: Provider-side Interpretability with Counterfactual Explanations in Recommender Systems}, AUTHOR = {Ghazimatin, Azin and Balalau, Oana and Saha Roy, Rishiraj and Weikum, Gerhard}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1911.08378}, EPRINT = {1911.08378}, EPRINTTYPE = {arXiv}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Interpretable explanations for recommender systems and other machine learning models are crucial to gain user trust. Prior works that have focused on paths connecting users and items in a heterogeneous network have several limitations, such as discovering relationships rather than true explanations, or disregarding other users' privacy. In this work, we take a fresh perspective, and present PRINCE: a provider-side mechanism to produce tangible explanations for end-users, where an explanation is defined to be a set of minimal actions performed by the user that, if removed, changes the recommendation to a different item. Given a recommendation, PRINCE uses a polynomial-time optimal algorithm for finding this minimal set of a user's actions from an exponential search space, based on random walks over dynamic graphs. Experiments on two real-world datasets show that PRINCE provides more compact explanations than intuitive baselines, and insights from a crowdsourced user-study demonstrate the viability of such action-based explanations. We thus posit that PRINCE produces scrutable, actionable, and concise explanations, owing to its use of counterfactual evidence, a user's own actions, and minimal sets, respectively.}, }
Endnote
%0 Report %A Ghazimatin, Azin %A Balalau, Oana %A Saha Roy, Rishiraj %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T PRINCE: Provider-side Interpretability with Counterfactual Explanations in Recommender Systems : %G eng %U http://hdl.handle.net/21.11116/0000-0005-8415-E %U http://arxiv.org/abs/1911.08378 %D 2019 %X Interpretable explanations for recommender systems and other machine learning models are crucial to gain user trust. Prior works that have focused on paths connecting users and items in a heterogeneous network have several limitations, such as discovering relationships rather than true explanations, or disregarding other users' privacy. In this work, we take a fresh perspective, and present PRINCE: a provider-side mechanism to produce tangible explanations for end-users, where an explanation is defined to be a set of minimal actions performed by the user that, if removed, changes the recommendation to a different item. Given a recommendation, PRINCE uses a polynomial-time optimal algorithm for finding this minimal set of a user's actions from an exponential search space, based on random walks over dynamic graphs. Experiments on two real-world datasets show that PRINCE provides more compact explanations than intuitive baselines, and insights from a crowdsourced user-study demonstrate the viability of such action-based explanations. We thus posit that PRINCE produces scrutable, actionable, and concise explanations, owing to its use of counterfactual evidence, a user's own actions, and minimal sets, respectively. %K Computer Science, Learning, cs.LG,Computer Science, Artificial Intelligence, cs.AI,Statistics, Machine Learning, stat.ML
[27]
A. Ghazimatin, R. Saha Roy, and G. Weikum, “FAIRY: A Framework for Understanding Relationships between Users’ Actions and their Social Feeds,” 2019. [Online]. Available: http://arxiv.org/abs/1908.03109. (arXiv: 1908.03109)
Abstract
Users increasingly rely on social media feeds for consuming daily information. The items in a feed, such as news, questions, songs, etc., usually result from the complex interplay of a user's social contacts, her interests and her actions on the platform. The relationship of the user's own behavior and the received feed is often puzzling, and many users would like to have a clear explanation on why certain items were shown to them. Transparency and explainability are key concerns in the modern world of cognitive overload, filter bubbles, user tracking, and privacy risks. This paper presents FAIRY, a framework that systematically discovers, ranks, and explains relationships between users' actions and items in their social media feeds. We model the user's local neighborhood on the platform as an interaction graph, a form of heterogeneous information network constructed solely from information that is easily accessible to the concerned user. We posit that paths in this interaction graph connecting the user and her feed items can act as pertinent explanations for the user. These paths are scored with a learning-to-rank model that captures relevance and surprisal. User studies on two social platforms demonstrate the practical viability and user benefits of the FAIRY method.
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@online{Ghazimatin_arXiv1908.03109, TITLE = {{FAIRY}: A Framework for Understanding Relationships between Users' Actions and their Social Feeds}, AUTHOR = {Ghazimatin, Azin and Saha Roy, Rishiraj and Weikum, Gerhard}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1908.03109}, EPRINT = {1908.03109}, EPRINTTYPE = {arXiv}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Users increasingly rely on social media feeds for consuming daily information. The items in a feed, such as news, questions, songs, etc., usually result from the complex interplay of a user's social contacts, her interests and her actions on the platform. The relationship of the user's own behavior and the received feed is often puzzling, and many users would like to have a clear explanation on why certain items were shown to them. Transparency and explainability are key concerns in the modern world of cognitive overload, filter bubbles, user tracking, and privacy risks. This paper presents FAIRY, a framework that systematically discovers, ranks, and explains relationships between users' actions and items in their social media feeds. We model the user's local neighborhood on the platform as an interaction graph, a form of heterogeneous information network constructed solely from information that is easily accessible to the concerned user. We posit that paths in this interaction graph connecting the user and her feed items can act as pertinent explanations for the user. These paths are scored with a learning-to-rank model that captures relevance and surprisal. User studies on two social platforms demonstrate the practical viability and user benefits of the FAIRY method.}, }
Endnote
%0 Report %A Ghazimatin, Azin %A Saha Roy, Rishiraj %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T FAIRY: A Framework for Understanding Relationships between Users' Actions and their Social Feeds : %G eng %U http://hdl.handle.net/21.11116/0000-0005-83B9-6 %U http://arxiv.org/abs/1908.03109 %D 2019 %X Users increasingly rely on social media feeds for consuming daily information. The items in a feed, such as news, questions, songs, etc., usually result from the complex interplay of a user's social contacts, her interests and her actions on the platform. The relationship of the user's own behavior and the received feed is often puzzling, and many users would like to have a clear explanation on why certain items were shown to them. Transparency and explainability are key concerns in the modern world of cognitive overload, filter bubbles, user tracking, and privacy risks. This paper presents FAIRY, a framework that systematically discovers, ranks, and explains relationships between users' actions and items in their social media feeds. We model the user's local neighborhood on the platform as an interaction graph, a form of heterogeneous information network constructed solely from information that is easily accessible to the concerned user. We posit that paths in this interaction graph connecting the user and her feed items can act as pertinent explanations for the user. These paths are scored with a learning-to-rank model that captures relevance and surprisal. User studies on two social platforms demonstrate the practical viability and user benefits of the FAIRY method. %K cs.SI,Computer Science, Learning, cs.LG,Statistics, Machine Learning, stat.ML,
[28]
A. Ghazimatin, R. Saha Roy, and G. Weikum, “FAIRY: A Framework for Understanding Relationships between Users’ Actions and their Social Feeds,” in WSDM’19, 12h ACM International Conference on Web Search and Data Mining, Melbourne, Australia, 2019.
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@inproceedings{Ghazimatin_WSDM2019, TITLE = {{FAIRY}: {A} Framework for Understanding Relationships between Users' Actions and their Social Feeds}, AUTHOR = {Ghazimatin, Azin and Saha Roy, Rishiraj and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-5940-5}, DOI = {10.1145/3289600.3290990}, PUBLISHER = {ACM}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, BOOKTITLE = {WSDM'19, 12h ACM International Conference on Web Search and Data Mining}, PAGES = {240--248}, ADDRESS = {Melbourne, Australia}, }
Endnote
%0 Conference Proceedings %A Ghazimatin, Azin %A Saha Roy, Rishiraj %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T FAIRY: A Framework for Understanding Relationships between Users' Actions and their Social Feeds : %G eng %U http://hdl.handle.net/21.11116/0000-0002-9BD9-B %R 10.1145/3289600.3290990 %D 2019 %B 12h ACM International Conference on Web Search and Data Mining %Z date of event: 2019-02-11 - 2019-02-15 %C Melbourne, Australia %B WSDM'19 %P 240 - 248 %I ACM %@ 978-1-4503-5940-5
[29]
A. Guimarães, O. Balalau, E. Terolli, and G. Weikum, “Analyzing the Traits and Anomalies of Political Discussions on Reddit,” in Proceedings of the Thirteenth International Conference on Web and Social Media (ICWSM 2019), Munich, Germany, 2019.
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@inproceedings{Guimaraes_ICWSM2019, TITLE = {Analyzing the Traits and Anomalies of Political Discussions on {R}eddit}, AUTHOR = {Guimar{\~a}es, Anna and Balalau, Oana and Terolli, Erisa and Weikum, Gerhard}, LANGUAGE = {eng}, ISSN = {2334-0770}, PUBLISHER = {AAAI}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of the Thirteenth International Conference on Web and Social Media (ICWSM 2019)}, PAGES = {205--213}, ADDRESS = {Munich, Germany}, }
Endnote
%0 Conference Proceedings %A Guimarães, Anna %A Balalau, Oana %A Terolli, Erisa %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Analyzing the Traits and Anomalies of Political Discussions on Reddit : %G eng %U http://hdl.handle.net/21.11116/0000-0003-3649-F %D 2019 %B 13th International Conference on Web and Social Media %Z date of event: 2019-06-11 - 2019-06-14 %C Munich, Germany %B Proceedings of the Thirteenth International Conference on Web and Social Media %P 205 - 213 %I AAAI %@ false
[30]
D. Gupta and K. Berberich, “Structured Search in Annotated Document Collections,” in WSDM’19, 12h ACM International Conference on Web Search and Data Mining, Melbourne, Australia, 2019.
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@inproceedings{Gupta_WSDM2019Demo, TITLE = {Structured Search in Annotated Document Collections}, AUTHOR = {Gupta, Dhruv and Berberich, Klaus}, LANGUAGE = {eng}, ISBN = {978-1-4503-5940-5}, DOI = {10.1145/3289600.3290618}, PUBLISHER = {ACM}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, BOOKTITLE = {WSDM'19, 12h ACM International Conference on Web Search and Data Mining}, PAGES = {794--797}, ADDRESS = {Melbourne, Australia}, }
Endnote
%0 Conference Proceedings %A Gupta, Dhruv %A Berberich, Klaus %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Structured Search in Annotated Document Collections : Demo paper %G eng %U http://hdl.handle.net/21.11116/0000-0002-A8D6-F %R 10.1145/3289600.3290618 %D 2019 %B 12h ACM International Conference on Web Search and Data Mining %Z date of event: 2019-02-11 - 2019-02-15 %C Melbourne, Australia %B WSDM'19 %P 794 - 797 %I ACM %@ 978-1-4503-5940-5
[31]
D. Gupta, “Search and Analytics Using Semantic Annotations,” Universität des Saarlandes, Saarbrücken, 2019.
Abstract
Search systems help users locate relevant information in the form of text documents for keyword queries. Using text alone, it is often difficult to satisfy the user's information need. To discern the user's intent behind queries, we turn to semantic annotations (e.g., named entities and temporal expressions) that natural language processing tools can now deliver with great accuracy. This thesis develops methods and an infrastructure that leverage semantic annotations to efficiently and effectively search large document collections. This thesis makes contributions in three areas: indexing, querying, and mining of semantically annotated document collections. First, we describe an indexing infrastructure for semantically annotated document collections. The indexing infrastructure can support knowledge-centric tasks such as information extraction, relationship extraction, question answering, fact spotting and semantic search at scale across millions of documents. Second, we propose methods for exploring large document collections by suggesting semantic aspects for queries. These semantic aspects are generated by considering annotations in the form of temporal expressions, geographic locations, and other named entities. The generated aspects help guide the user to relevant documents without the need to read their contents. Third and finally, we present methods that can generate events, structured tables, and insightful visualizations from semantically annotated document collections.
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@phdthesis{GUPTAphd2019, TITLE = {Search and Analytics Using Semantic Annotations}, AUTHOR = {Gupta, Dhruv}, LANGUAGE = {eng}, URL = {urn:nbn:de:bsz:291--ds-300780}, DOI = {10.22028/D291-30078}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, ABSTRACT = {Search systems help users locate relevant information in the form of text documents for keyword queries. Using text alone, it is often difficult to satisfy the user's information need. To discern the user's intent behind queries, we turn to semantic annotations (e.g., named entities and temporal expressions) that natural language processing tools can now deliver with great accuracy. This thesis develops methods and an infrastructure that leverage semantic annotations to efficiently and effectively search large document collections. This thesis makes contributions in three areas: indexing, querying, and mining of semantically annotated document collections. First, we describe an indexing infrastructure for semantically annotated document collections. The indexing infrastructure can support knowledge-centric tasks such as information extraction, relationship extraction, question answering, fact spotting and semantic search at scale across millions of documents. Second, we propose methods for exploring large document collections by suggesting semantic aspects for queries. These semantic aspects are generated by considering annotations in the form of temporal expressions, geographic locations, and other named entities. The generated aspects help guide the user to relevant documents without the need to read their contents. Third and finally, we present methods that can generate events, structured tables, and insightful visualizations from semantically annotated document collections.}, }
Endnote
%0 Thesis %A Gupta, Dhruv %Y Berberich, Klaus %A referee: Weikum, Gerhard %A referee: Bedathur, Srikanta %+ Databases and Information Systems, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Search and Analytics Using Semantic Annotations : %G eng %U http://hdl.handle.net/21.11116/0000-0005-7695-E %R 10.22028/D291-30078 %U urn:nbn:de:bsz:291--ds-300780 %F OTHER: hdl:20.500.11880/28516 %I Universität des Saarlandes %C Saarbrücken %D 2019 %P xxviii, 211 p. %V phd %9 phd %X Search systems help users locate relevant information in the form of text documents for keyword queries. Using text alone, it is often difficult to satisfy the user's information need. To discern the user's intent behind queries, we turn to semantic annotations (e.g., named entities and temporal expressions) that natural language processing tools can now deliver with great accuracy. This thesis develops methods and an infrastructure that leverage semantic annotations to efficiently and effectively search large document collections. This thesis makes contributions in three areas: indexing, querying, and mining of semantically annotated document collections. First, we describe an indexing infrastructure for semantically annotated document collections. The indexing infrastructure can support knowledge-centric tasks such as information extraction, relationship extraction, question answering, fact spotting and semantic search at scale across millions of documents. Second, we propose methods for exploring large document collections by suggesting semantic aspects for queries. These semantic aspects are generated by considering annotations in the form of temporal expressions, geographic locations, and other named entities. The generated aspects help guide the user to relevant documents without the need to read their contents. Third and finally, we present methods that can generate events, structured tables, and insightful visualizations from semantically annotated document collections. %U https://publikationen.sulb.uni-saarland.de/handle/20.500.11880/28516
[32]
D. Gupta, “Search and Analytics Using Semantic Annotations,” ACM SIGIR Forum, vol. 53, no. 2, 2019.
Abstract
Search systems help users locate relevant information in the form of text documents for keyword queries. Using text alone, it is often difficult to satisfy the user's information need. To discern the user's intent behind queries, we turn to semantic annotations (e.g., named entities and temporal expressions) that natural language processing tools can now deliver with great accuracy. This thesis develops methods and an infrastructure that leverage semantic annotations to efficiently and effectively search large document collections. This thesis makes contributions in three areas: indexing, querying, and mining of semantically annotated document collections. First, we describe an indexing infrastructure for semantically annotated document collections. The indexing infrastructure can support knowledge-centric tasks such as information extraction, relationship extraction, question answering, fact spotting and semantic search at scale across millions of documents. Second, we propose methods for exploring large document collections by suggesting semantic aspects for queries. These semantic aspects are generated by considering annotations in the form of temporal expressions, geographic locations, and other named entities. The generated aspects help guide the user to relevant documents without the need to read their contents. Third and finally, we present methods that can generate events, structured tables, and insightful visualizations from semantically annotated document collections.
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@article{Gupta_SIGIR19, TITLE = {Search and Analytics Using Semantic Annotations}, AUTHOR = {Gupta, Dhruv}, LANGUAGE = {eng}, PUBLISHER = {ACM}, ADDRESS = {New York, NY}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Search systems help users locate relevant information in the form of text documents for keyword queries. Using text alone, it is often difficult to satisfy the user's information need. To discern the user's intent behind queries, we turn to semantic annotations (e.g., named entities and temporal expressions) that natural language processing tools can now deliver with great accuracy. This thesis develops methods and an infrastructure that leverage semantic annotations to efficiently and effectively search large document collections. This thesis makes contributions in three areas: indexing, querying, and mining of semantically annotated document collections. First, we describe an indexing infrastructure for semantically annotated document collections. The indexing infrastructure can support knowledge-centric tasks such as information extraction, relationship extraction, question answering, fact spotting and semantic search at scale across millions of documents. Second, we propose methods for exploring large document collections by suggesting semantic aspects for queries. These semantic aspects are generated by considering annotations in the form of temporal expressions, geographic locations, and other named entities. The generated aspects help guide the user to relevant documents without the need to read their contents. Third and finally, we present methods that can generate events, structured tables, and insightful visualizations from semantically annotated document collections.}, JOURNAL = {ACM SIGIR Forum}, VOLUME = {53}, NUMBER = {2}, PAGES = {100--101}, }
Endnote
%0 Journal Article %A Gupta, Dhruv %+ Databases and Information Systems, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society %T Search and Analytics Using Semantic Annotations : Doctorial Abstract %G eng %U http://hdl.handle.net/21.11116/0000-0005-A1C2-9 %7 2019 %D 2019 %X Search systems help users locate relevant information in the form of text documents for keyword queries. Using text alone, it is often difficult to satisfy the user's information need. To discern the user's intent behind queries, we turn to semantic annotations (e.g., named entities and temporal expressions) that natural language processing tools can now deliver with great accuracy. This thesis develops methods and an infrastructure that leverage semantic annotations to efficiently and effectively search large document collections. This thesis makes contributions in three areas: indexing, querying, and mining of semantically annotated document collections. First, we describe an indexing infrastructure for semantically annotated document collections. The indexing infrastructure can support knowledge-centric tasks such as information extraction, relationship extraction, question answering, fact spotting and semantic search at scale across millions of documents. Second, we propose methods for exploring large document collections by suggesting semantic aspects for queries. These semantic aspects are generated by considering annotations in the form of temporal expressions, geographic locations, and other named entities. The generated aspects help guide the user to relevant documents without the need to read their contents. Third and finally, we present methods that can generate events, structured tables, and insightful visualizations from semantically annotated document collections. %J ACM SIGIR Forum %V 53 %N 2 %& 100 %P 100 - 101 %I ACM %C New York, NY %U http://sigir.org/wp-content/uploads/2019/december/p100.pdf
[33]
D. Gupta and K. Berberich, “JIGSAW: Structuring Text into Tables,” in ICTIR’19, ACM SIGIR International Conference on Theory of Information Retrieval, Santa Clara, CA, USA, 2019.
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@inproceedings{Gupta_ICTIR2019, TITLE = {JIGSAW: {S}tructuring Text into Tables}, AUTHOR = {Gupta, Dhruv and Berberich, Klaus}, LANGUAGE = {eng}, ISBN = {978-1-4503-6881-0}, DOI = {10.1145/3341981.3344228}, PUBLISHER = {ACM}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {ICTIR'19, ACM SIGIR International Conference on Theory of Information Retrieval}, EDITOR = {Fang, Yi and Zhang, Yi}, PAGES = {237--244}, ADDRESS = {Santa Clara, CA, USA}, }
Endnote
%0 Conference Proceedings %A Gupta, Dhruv %A Berberich, Klaus %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T JIGSAW: Structuring Text into Tables : %G eng %U http://hdl.handle.net/21.11116/0000-0005-8479-E %R 10.1145/3341981.3344228 %D 2019 %B ACM SIGIR International Conference on Theory of Information Retrieval %Z date of event: 2019-10-02 - 2019-10-05 %C Santa Clara, CA, USA %B ICTIR'19 %E Fang, Yi; Zhang, Yi %P 237 - 244 %I ACM %@ 978-1-4503-6881-0
[34]
D. Gupta, K. Berberich, J. Strötgen, and D. Zeinalipour-Yazti, “Generating Semantic Aspects for Queries,” in The Semantic Web (ESWC 2019), Portorož, Slovenia, 2019.
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@inproceedings{GuptaESWC2019, TITLE = {Generating Semantic Aspects for Queries}, AUTHOR = {Gupta, Dhruv and Berberich, Klaus and Str{\"o}tgen, Jannik and Zeinalipour-Yazti, Demetrios}, LANGUAGE = {eng}, ISBN = {978-3-030-21347-3}, DOI = {10.1007/978-3-030-21348-0_11}, PUBLISHER = {Springer}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, BOOKTITLE = {The Semantic Web (ESWC 2019)}, EDITOR = {Hitzler, Pascal and Fern{\'a}ndez, Miriam and Janowicz, Krzysztof and Zaveri, Amrapali and Gray, Alasdair J. G. and Lopez, Vanessa and Haller, Armin and Hammar, Karl}, PAGES = {162--178}, SERIES = {Lecture Notes in Computer Science}, VOLUME = {11503}, ADDRESS = {Portoro{\v z}, Slovenia}, }
Endnote
%0 Conference Proceedings %A Gupta, Dhruv %A Berberich, Klaus %A Strötgen, Jannik %A Zeinalipour-Yazti, Demetrios %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T Generating Semantic Aspects for Queries : %G eng %U http://hdl.handle.net/21.11116/0000-0003-FF5F-5 %R 10.1007/978-3-030-21348-0_11 %D 2019 %B 16th Extended Semantic Web Conference %Z date of event: 2019-06-02 - 2019-06-06 %C Portorož, Slovenia %B The Semantic Web %E Hitzler, Pascal; Fernández, Miriam; Janowicz, Krzysztof; Zaveri, Amrapali; Gray, Alasdair J. G.; Lopez, Vanessa; Haller, Armin; Hammar, Karl %P 162 - 178 %I Springer %@ 978-3-030-21347-3 %B Lecture Notes in Computer Science %N 11503
[35]
D. Gupta and K. Berberich, “Efficient Retrieval of Knowledge Graph Fact Evidences,” in The Semantic Web: ESWC 2019 Satellite Events, Portorož, Slovenia, 2019.
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@inproceedings{GuptaESWC2019a, TITLE = {Efficient Retrieval of Knowledge Graph Fact Evidences}, AUTHOR = {Gupta, Dhruv and Berberich, Klaus}, LANGUAGE = {eng}, ISBN = {978-3-030-32326-4}, DOI = {10.1007/978-3-030-32327-1_18}, PUBLISHER = {Springer}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, BOOKTITLE = {The Semantic Web: ESWC 2019 Satellite Events}, EDITOR = {Hitzler, Pascal and Kirrane, Sabrina and Hartig, Olaf and de Boer, Victor and Vidal, Maria-Esther and Maleshova, Maria and Schlobach, Stefan and Hammar, Karl and Lasierra, Nelia and Stadtm{\"u}ller, Steffen and Hose, Katja and Verborgh, Ruben}, PAGES = {90--94}, SERIES = {Lecture Notes in Computer Science}, VOLUME = {11762}, ADDRESS = {Portoro{\v z}, Slovenia}, }
Endnote
%0 Conference Proceedings %A Gupta, Dhruv %A Berberich, Klaus %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Efficient Retrieval of Knowledge Graph Fact Evidences : %G eng %U http://hdl.handle.net/21.11116/0000-0005-8477-0 %R 10.1007/978-3-030-32327-1_18 %D 2019 %B 16th Extended Semantic Web Conference %Z date of event: 2019-06-02 - 2019-06-06 %C Portorož, Slovenia %B The Semantic Web: ESWC 2019 Satellite Events %E Hitzler, Pascal; Kirrane, Sabrina; Hartig, Olaf; de Boer, Victor; Vidal, Maria-Esther; Maleshova, Maria; Schlobach, Stefan; Hammar, Karl; Lasierra, Nelia; Stadtmüller, Steffen; Hose, Katja; Verborgh, Ruben %P 90 - 94 %I Springer %@ 978-3-030-32326-4 %B Lecture Notes in Computer Science %N 11762
[36]
M. A. Hedderich, A. Yates, D. Klakow, and G. de Melo, “Using Multi-Sense Vector Embeddings for Reverse Dictionaries,” in Proceedings of the 13th International Conference on Computational Semantics - Long Papers (IWCS 2019), Gothenburg, Sweden, 2019.
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@inproceedings{Hedderich_IWCS2019, TITLE = {Using Multi-Sense Vector Embeddings for Reverse Dictionaries}, AUTHOR = {Hedderich, Michael A. and Yates, Andrew and Klakow, Dietrich and de Melo, Gerard}, LANGUAGE = {eng}, ISBN = {978-1-950737-19-2}, PUBLISHER = {ACL}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of the 13th International Conference on Computational Semantics -- Long Papers (IWCS 2019)}, EDITOR = {Dobnik, Simon and Chatzikyriakidis, Stergios and Demberg, Vera}, PAGES = {247--258}, ADDRESS = {Gothenburg, Sweden}, }
Endnote
%0 Conference Proceedings %A Hedderich, Michael A. %A Yates, Andrew %A Klakow, Dietrich %A de Melo, Gerard %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T Using Multi-Sense Vector Embeddings for Reverse Dictionaries : %G eng %U http://hdl.handle.net/21.11116/0000-0004-02A4-0 %D 2019 %B 13th International Conference on Computational Semantics %Z date of event: 2019-05-23 - 2019-05-27 %C Gothenburg, Sweden %B Proceedings of the 13th International Conference on Computational Semantics - Long Papers %E Dobnik, Simon; Chatzikyriakidis, Stergios; Demberg, Vera %P 247 - 258 %I ACL %@ 978-1-950737-19-2 %U https://www.aclweb.org/anthology/W19-0421
[37]
M. A. Hedderich, A. Yates, D. Klakow, and G. de Melo, “Using Multi-Sense Vector Embeddings for Reverse Dictionaries,” 2019. [Online]. Available: http://arxiv.org/abs/1904.01451. (arXiv: 1904.01451)
Abstract
Popular word embedding methods such as word2vec and GloVe assign a single vector representation to each word, even if a word has multiple distinct meanings. Multi-sense embeddings instead provide different vectors for each sense of a word. However, they typically cannot serve as a drop-in replacement for conventional single-sense embeddings, because the correct sense vector needs to be selected for each word. In this work, we study the effect of multi-sense embeddings on the task of reverse dictionaries. We propose a technique to easily integrate them into an existing neural network architecture using an attention mechanism. Our experiments demonstrate that large improvements can be obtained when employing multi-sense embeddings both in the input sequence as well as for the target representation. An analysis of the sense distributions and of the learned attention is provided as well.
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@online{Hedderich_arXiv1904.01451, TITLE = {Using Multi-Sense Vector Embeddings for Reverse Dictionaries}, AUTHOR = {Hedderich, Michael A. and Yates, Andrew and Klakow, Dietrich and de Melo, Gerard}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1904.01451}, EPRINT = {1904.01451}, EPRINTTYPE = {arXiv}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Popular word embedding methods such as word2vec and GloVe assign a single vector representation to each word, even if a word has multiple distinct meanings. Multi-sense embeddings instead provide different vectors for each sense of a word. However, they typically cannot serve as a drop-in replacement for conventional single-sense embeddings, because the correct sense vector needs to be selected for each word. In this work, we study the effect of multi-sense embeddings on the task of reverse dictionaries. We propose a technique to easily integrate them into an existing neural network architecture using an attention mechanism. Our experiments demonstrate that large improvements can be obtained when employing multi-sense embeddings both in the input sequence as well as for the target representation. An analysis of the sense distributions and of the learned attention is provided as well.}, }
Endnote
%0 Report %A Hedderich, Michael A. %A Yates, Andrew %A Klakow, Dietrich %A de Melo, Gerard %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T Using Multi-Sense Vector Embeddings for Reverse Dictionaries : %G eng %U http://hdl.handle.net/21.11116/0000-0004-02B4-E %U http://arxiv.org/abs/1904.01451 %D 2019 %X Popular word embedding methods such as word2vec and GloVe assign a single vector representation to each word, even if a word has multiple distinct meanings. Multi-sense embeddings instead provide different vectors for each sense of a word. However, they typically cannot serve as a drop-in replacement for conventional single-sense embeddings, because the correct sense vector needs to be selected for each word. In this work, we study the effect of multi-sense embeddings on the task of reverse dictionaries. We propose a technique to easily integrate them into an existing neural network architecture using an attention mechanism. Our experiments demonstrate that large improvements can be obtained when employing multi-sense embeddings both in the input sequence as well as for the target representation. An analysis of the sense distributions and of the learned attention is provided as well. %K Computer Science, Computation and Language, cs.CL,Computer Science, Learning, cs.LG
[38]
V. T. Ho, Y. Ibrahim, K. Pal, K. Berberich, and G. Weikum, “Qsearch: Answering Quantity Queries from Text,” in The Semantic Web -- ISWC 2019, Auckland, New Zealand, 2019.
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@inproceedings{Ho_ISWC2019, TITLE = {Qsearch: {A}nswering Quantity Queries from Text}, AUTHOR = {Ho, Vinh Thinh and Ibrahim, Yusra and Pal, Koninika and Berberich, Klaus and Weikum, Gerhard}, LANGUAGE = {eng}, ISSN = {0302-9743}, ISBN = {978-3-030-30792-9}, DOI = {10.1007/978-3-030-30793-6_14}, PUBLISHER = {Springer}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, BOOKTITLE = {The Semantic Web -- ISWC 2019}, DEBUG = {author: Gandon, Fabien}, EDITOR = {Ghidini, Chiara and Hartig, Olaf and Maleshkova, Maria and Sv{\'a}tek, Vojt{\u e}ch and Cruz, Isabel and Hogan, Aidan and Song, Jie and Lefran{\c c}ois, Maxime}, PAGES = {237--257}, EID = {62}, SERIES = {Lecture Notes in Computer Science}, VOLUME = {11778}, ADDRESS = {Auckland, New Zealand}, }
Endnote
%0 Conference Proceedings %A Ho, Vinh Thinh %A Ibrahim, Yusra %A Pal, Koninika %A Berberich, Klaus %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Qsearch: Answering Quantity Queries from Text : %G eng %U http://hdl.handle.net/21.11116/0000-0005-83AB-6 %R 10.1007/978-3-030-30793-6_14 %D 2019 %B 18th Semantic Web Conference %Z date of event: 2019-10-26 - 2019-10-30 %C Auckland, New Zealand %B The Semantic Web -- ISWC 2019 %E Ghidini, Chiara; Hartig, Olaf; Maleshkova, Maria; Svátek, Vojtĕch; Cruz, Isabel; Hogan, Aidan; Song, Jie; Lefrançois, Maxime; Gandon, Fabien %P 237 - 257 %Z sequence number: 62 %I Springer %@ 978-3-030-30792-9 %B Lecture Notes in Computer Science %N 11778 %@ false
[39]
Y. Ibrahim, “Understanding Quantities in Web Tables and Text,” Universität des Saarlandes, Saarbrücken, 2019.
Abstract
There is a wealth of schema-free tables on the web. The text accompanying these tables explains and qualifies the numerical quantities given in the tables. Despite this ubiquity of tabular data, there is little research that harnesses this wealth of data by semantically understanding the information that is conveyed rather ambiguously in these tables. This information can be disambiguated only by the help of the accompanying text. In the process of understanding quantity mentions in tables and text, we are faced with the following challenges; First, there is no comprehensive knowledge base for anchoring quantity mentions. Second, tables are created ad-hoc without a standard schema and with ambiguous header names; also table cells usually contain abbreviations. Third, quantities can be written in multiple forms and units of measures. Fourth, the text usually refers to the quantities in tables using aggregation, approximation, and different scales. In this thesis, we target these challenges through the following contributions: - We present the Quantity Knowledge Base (QKB), a knowledge base for representing Quantity mentions. We construct the QKB by importing information from Freebase, Wikipedia, and other online sources. - We propose Equity: a system for automatically canonicalizing header names and cell values onto concepts, classes, entities, and uniquely represented quantities registered in a knowledge base. We devise a probabilistic graphical model that captures coherence dependencies between cells in tables and candidate items in the space of concepts, entities, and quantities. Then, we cast the inference problem into an efficient algorithm based on random walks over weighted graphs. baselines. - We introduce the quantity alignment problem: computing bidirectional links between textual mentions of quantities and the corresponding table cells. We propose BriQ: a system for computing such alignments. BriQ copes with the specific challenges of approximate quantities, aggregated quantities, and calculated quantities. - We design ExQuisiTe: a web application that identifies mentions of quantities in text and tables, aligns quantity mentions in the text with related quantity mentions in tables, and generates salient suggestions for extractive text summarization systems.
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@phdthesis{yusraphd2019, TITLE = {Understanding Quantities in Web Tables and Text}, AUTHOR = {Ibrahim, Yusra}, LANGUAGE = {eng}, DOI = {10.22028/D291-29657}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, ABSTRACT = {There is a wealth of schema-free tables on the web. The text accompanying these tables explains and qualifies the numerical quantities given in the tables. Despite this ubiquity of tabular data, there is little research that harnesses this wealth of data by semantically understanding the information that is conveyed rather ambiguously in these tables. This information can be disambiguated only by the help of the accompanying text. In the process of understanding quantity mentions in tables and text, we are faced with the following challenges; First, there is no comprehensive knowledge base for anchoring quantity mentions. Second, tables are created ad-hoc without a standard schema and with ambiguous header names; also table cells usually contain abbreviations. Third, quantities can be written in multiple forms and units of measures. Fourth, the text usually refers to the quantities in tables using aggregation, approximation, and different scales. In this thesis, we target these challenges through the following contributions: -- We present the Quantity Knowledge Base (QKB), a knowledge base for representing Quantity mentions. We construct the QKB by importing information from Freebase, Wikipedia, and other online sources. -- We propose Equity: a system for automatically canonicalizing header names and cell values onto concepts, classes, entities, and uniquely represented quantities registered in a knowledge base. We devise a probabilistic graphical model that captures coherence dependencies between cells in tables and candidate items in the space of concepts, entities, and quantities. Then, we cast the inference problem into an efficient algorithm based on random walks over weighted graphs. baselines. -- We introduce the quantity alignment problem: computing bidirectional links between textual mentions of quantities and the corresponding table cells. We propose BriQ: a system for computing such alignments. BriQ copes with the specific challenges of approximate quantities, aggregated quantities, and calculated quantities. -- We design ExQuisiTe: a web application that identifies mentions of quantities in text and tables, aligns quantity mentions in the text with related quantity mentions in tables, and generates salient suggestions for extractive text summarization systems.}, }
Endnote
%0 Thesis %A Ibrahim, Yusra %Y Weikum, Gerhard %A referee: Riedewald, Mirek %A referee: Berberich, Klaus %+ Databases and Information Systems, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Algorithms and Complexity, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Understanding Quantities in Web Tables and Text : %G eng %U http://hdl.handle.net/21.11116/0000-0005-4384-A %R 10.22028/D291-29657 %I Universität des Saarlandes %C Saarbrücken %D 2019 %P 116 p. %V phd %9 phd %X There is a wealth of schema-free tables on the web. The text accompanying these tables explains and qualifies the numerical quantities given in the tables. Despite this ubiquity of tabular data, there is little research that harnesses this wealth of data by semantically understanding the information that is conveyed rather ambiguously in these tables. This information can be disambiguated only by the help of the accompanying text. In the process of understanding quantity mentions in tables and text, we are faced with the following challenges; First, there is no comprehensive knowledge base for anchoring quantity mentions. Second, tables are created ad-hoc without a standard schema and with ambiguous header names; also table cells usually contain abbreviations. Third, quantities can be written in multiple forms and units of measures. Fourth, the text usually refers to the quantities in tables using aggregation, approximation, and different scales. In this thesis, we target these challenges through the following contributions: - We present the Quantity Knowledge Base (QKB), a knowledge base for representing Quantity mentions. We construct the QKB by importing information from Freebase, Wikipedia, and other online sources. - We propose Equity: a system for automatically canonicalizing header names and cell values onto concepts, classes, entities, and uniquely represented quantities registered in a knowledge base. We devise a probabilistic graphical model that captures coherence dependencies between cells in tables and candidate items in the space of concepts, entities, and quantities. Then, we cast the inference problem into an efficient algorithm based on random walks over weighted graphs. baselines. - We introduce the quantity alignment problem: computing bidirectional links between textual mentions of quantities and the corresponding table cells. We propose BriQ: a system for computing such alignments. BriQ copes with the specific challenges of approximate quantities, aggregated quantities, and calculated quantities. - We design ExQuisiTe: a web application that identifies mentions of quantities in text and tables, aligns quantity mentions in the text with related quantity mentions in tables, and generates salient suggestions for extractive text summarization systems. %U https://publikationen.sulb.uni-saarland.de/handle/20.500.11880/28300
[40]
Y. Ibrahim and G. Weikum, “ExQuisiTe: Explaining Quantities in Text,” in Proceedings of the World Wide Web Conference (WWW 2019), San Francisco, CA, USA, 2019.
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@inproceedings{Ibrahim_WWW2019, TITLE = {{ExQuisiTe}: {E}xplaining Quantities in Text}, AUTHOR = {Ibrahim, Yusra and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-6674-8}, DOI = {10.1145/3308558.3314134}, PUBLISHER = {ACM}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of the World Wide Web Conference (WWW 2019)}, EDITOR = {McAuley, Julian}, PAGES = {3541--3544}, ADDRESS = {San Francisco, CA, USA}, }
Endnote
%0 Conference Proceedings %A Ibrahim, Yusra %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T ExQuisiTe: Explaining Quantities in Text : %G eng %U http://hdl.handle.net/21.11116/0000-0003-01B3-1 %R 10.1145/3308558.3314134 %D 2019 %B The Web Conference %Z date of event: 2019-05-13 - 2019-05-17 %C San Francisco, CA, USA %B Proceedings of the World Wide Web Conference %E McAuley, Julian %P 3541 - 3544 %I ACM %@ 978-1-4503-6674-8
[41]
Y. Ibrahim, M. Riedewald, G. Weikum, and D. Zeinalipour-Yazti, “Bridging Quantities in Tables and Text,” in ICDE 2019, 35th IEEE International Conference on Data Engineering, Macau, China, 2019.
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@inproceedings{Ibrahim_ICDE2019, TITLE = {Bridging Quantities in Tables and Text}, AUTHOR = {Ibrahim, Yusra and Riedewald, Mirek and Weikum, Gerhard and Zeinalipour-Yazti, Demetrios}, LANGUAGE = {eng}, ISBN = {978-1-5386-7474-1}, DOI = {10.1109/ICDE.2019.00094}, PUBLISHER = {IEEE}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {ICDE 2019, 35th IEEE International Conference on Data Engineering}, PAGES = {1010--1021}, ADDRESS = {Macau, China}, }
Endnote
%0 Conference Proceedings %A Ibrahim, Yusra %A Riedewald, Mirek %A Weikum, Gerhard %A Zeinalipour-Yazti, Demetrios %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Algorithms and Complexity, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Bridging Quantities in Tables and Text : %G eng %U http://hdl.handle.net/21.11116/0000-0003-01AB-B %R 10.1109/ICDE.2019.00094 %D 2019 %B 35th IEEE International Conference on Data Engineering %Z date of event: 2019-04-08 - 2019-04-12 %C Macau, China %B ICDE 2019 %P 1010 - 1021 %I IEEE %@ 978-1-5386-7474-1
[42]
Z. Jia, A. Abujabal, R. Saha Roy, J. Strötgen, and G. Weikum, “TEQUILA: Temporal Question Answering over Knowledge Bases,” 2019. [Online]. Available: http://arxiv.org/abs/1908.03650. (arXiv: 1908.03650)
Abstract
Question answering over knowledge bases (KB-QA) poses challenges in handling complex questions that need to be decomposed into sub-questions. An important case, addressed here, is that of temporal questions, where cues for temporal relations need to be discovered and handled. We present TEQUILA, an enabler method for temporal QA that can run on top of any KB-QA engine. TEQUILA has four stages. It detects if a question has temporal intent. It decomposes and rewrites the question into non-temporal sub-questions and temporal constraints. Answers to sub-questions are then retrieved from the underlying KB-QA engine. Finally, TEQUILA uses constraint reasoning on temporal intervals to compute final answers to the full question. Comparisons against state-of-the-art baselines show the viability of our method.
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@online{Jia_arXiv1908.03650, TITLE = {{TEQUILA}: Temporal Question Answering over Knowledge Bases}, AUTHOR = {Jia, Zhen and Abujabal, Abdalghani and Saha Roy, Rishiraj and Str{\"o}tgen, Jannik and Weikum, Gerhard}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1908.03650}, EPRINT = {1908.03650}, EPRINTTYPE = {arXiv}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Question answering over knowledge bases (KB-QA) poses challenges in handling complex questions that need to be decomposed into sub-questions. An important case, addressed here, is that of temporal questions, where cues for temporal relations need to be discovered and handled. We present TEQUILA, an enabler method for temporal QA that can run on top of any KB-QA engine. TEQUILA has four stages. It detects if a question has temporal intent. It decomposes and rewrites the question into non-temporal sub-questions and temporal constraints. Answers to sub-questions are then retrieved from the underlying KB-QA engine. Finally, TEQUILA uses constraint reasoning on temporal intervals to compute final answers to the full question. Comparisons against state-of-the-art baselines show the viability of our method.}, }
Endnote
%0 Report %A Jia, Zhen %A Abujabal, Abdalghani %A Saha Roy, Rishiraj %A Strötgen, Jannik %A Weikum, Gerhard %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T TEQUILA: Temporal Question Answering over Knowledge Bases : %G eng %U http://hdl.handle.net/21.11116/0000-0005-83BE-1 %U http://arxiv.org/abs/1908.03650 %D 2019 %X Question answering over knowledge bases (KB-QA) poses challenges in handling complex questions that need to be decomposed into sub-questions. An important case, addressed here, is that of temporal questions, where cues for temporal relations need to be discovered and handled. We present TEQUILA, an enabler method for temporal QA that can run on top of any KB-QA engine. TEQUILA has four stages. It detects if a question has temporal intent. It decomposes and rewrites the question into non-temporal sub-questions and temporal constraints. Answers to sub-questions are then retrieved from the underlying KB-QA engine. Finally, TEQUILA uses constraint reasoning on temporal intervals to compute final answers to the full question. Comparisons against state-of-the-art baselines show the viability of our method. %K Computer Science, Information Retrieval, cs.IR,Computer Science, Computation and Language, cs.CL
[43]
M. Kaiser, R. Saha Roy, and G. Weikum, “CROWN: Conversational Passage Ranking by Reasoning over Word Networks,” 2019. [Online]. Available: http://arxiv.org/abs/1911.02850. (arXiv: 1911.02850)
Abstract
Information needs around a topic cannot be satisfied in a single turn; users typically ask follow-up questions referring to the same theme and a system must be capable of understanding the conversational context of a request to retrieve correct answers. In this paper, we present our submission to the TREC Conversational Assistance Track 2019, in which such a conversational setting is explored. We propose a simple unsupervised method for conversational passage ranking by formulating the passage score for a query as a combination of similarity and coherence. To be specific, passages are preferred that contain words semantically similar to the words used in the question, and where such words appear close by. We built a word-proximity network (WPN) from a large corpus, where words are nodes and there is an edge between two nodes if they co-occur in the same passages in a statistically significant way, within a context window. Our approach, named CROWN, improved nDCG scores over a provided Indri baseline on the CAsT training data. On the evaluation data for CAsT, our best run submission achieved above-average performance with respect to AP@5 and nDCG@1000.
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@online{Kaiser_arXiv1911.02850, TITLE = {{CROWN}: Conversational Passage Ranking by Reasoning over Word Networks}, AUTHOR = {Kaiser, Magdalena and Saha Roy, Rishiraj and Weikum, Gerhard}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1911.02850}, EPRINT = {1911.02850}, EPRINTTYPE = {arXiv}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Information needs around a topic cannot be satisfied in a single turn; users typically ask follow-up questions referring to the same theme and a system must be capable of understanding the conversational context of a request to retrieve correct answers. In this paper, we present our submission to the TREC Conversational Assistance Track 2019, in which such a conversational setting is explored. We propose a simple unsupervised method for conversational passage ranking by formulating the passage score for a query as a combination of similarity and coherence. To be specific, passages are preferred that contain words semantically similar to the words used in the question, and where such words appear close by. We built a word-proximity network (WPN) from a large corpus, where words are nodes and there is an edge between two nodes if they co-occur in the same passages in a statistically significant way, within a context window. Our approach, named CROWN, improved nDCG scores over a provided Indri baseline on the CAsT training data. On the evaluation data for CAsT, our best run submission achieved above-average performance with respect to AP@5 and nDCG@1000.}, }
Endnote
%0 Report %A Kaiser, Magdalena %A Saha Roy, Rishiraj %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T CROWN: Conversational Passage Ranking by Reasoning over Word Networks : %G eng %U http://hdl.handle.net/21.11116/0000-0005-83ED-C %U http://arxiv.org/abs/1911.02850 %D 2019 %X Information needs around a topic cannot be satisfied in a single turn; users typically ask follow-up questions referring to the same theme and a system must be capable of understanding the conversational context of a request to retrieve correct answers. In this paper, we present our submission to the TREC Conversational Assistance Track 2019, in which such a conversational setting is explored. We propose a simple unsupervised method for conversational passage ranking by formulating the passage score for a query as a combination of similarity and coherence. To be specific, passages are preferred that contain words semantically similar to the words used in the question, and where such words appear close by. We built a word-proximity network (WPN) from a large corpus, where words are nodes and there is an edge between two nodes if they co-occur in the same passages in a statistically significant way, within a context window. Our approach, named CROWN, improved nDCG scores over a provided Indri baseline on the CAsT training data. On the evaluation data for CAsT, our best run submission achieved above-average performance with respect to AP@5 and nDCG@1000. %K Computer Science, Information Retrieval, cs.IR,Computer Science, Computation and Language, cs.CL
[44]
D. Kaltenpoth and J. Vreeken, “We Are Not Your Real Parents: Telling Causal from Confounded using MDL,” 2019. [Online]. Available: http://arxiv.org/abs/1901.06950. (arXiv: 1901.06950)
Abstract
Given data over variables $(X_1,...,X_m, Y)$ we consider the problem of finding out whether $X$ jointly causes $Y$ or whether they are all confounded by an unobserved latent variable $Z$. To do so, we take an information-theoretic approach based on Kolmogorov complexity. In a nutshell, we follow the postulate that first encoding the true cause, and then the effects given that cause, results in a shorter description than any other encoding of the observed variables. The ideal score is not computable, and hence we have to approximate it. We propose to do so using the Minimum Description Length (MDL) principle. We compare the MDL scores under the models where $X$ causes $Y$ and where there exists a latent variables $Z$ confounding both $X$ and $Y$ and show our scores are consistent. To find potential confounders we propose using latent factor modeling, in particular, probabilistic PCA (PPCA). Empirical evaluation on both synthetic and real-world data shows that our method, CoCa, performs very well -- even when the true generating process of the data is far from the assumptions made by the models we use. Moreover, it is robust as its accuracy goes hand in hand with its confidence.
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@online{Kaltenpoth_arXiv1901.06950, TITLE = {We Are Not Your Real Parents: Telling Causal from Confounded using {MDL}}, AUTHOR = {Kaltenpoth, David and Vreeken, Jilles}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1901.06950}, EPRINT = {1901.06950}, EPRINTTYPE = {arXiv}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Given data over variables $(X_1,...,X_m, Y)$ we consider the problem of finding out whether $X$ jointly causes $Y$ or whether they are all confounded by an unobserved latent variable $Z$. To do so, we take an information-theoretic approach based on Kolmogorov complexity. In a nutshell, we follow the postulate that first encoding the true cause, and then the effects given that cause, results in a shorter description than any other encoding of the observed variables. The ideal score is not computable, and hence we have to approximate it. We propose to do so using the Minimum Description Length (MDL) principle. We compare the MDL scores under the models where $X$ causes $Y$ and where there exists a latent variables $Z$ confounding both $X$ and $Y$ and show our scores are consistent. To find potential confounders we propose using latent factor modeling, in particular, probabilistic PCA (PPCA). Empirical evaluation on both synthetic and real-world data shows that our method, CoCa, performs very well -- even when the true generating process of the data is far from the assumptions made by the models we use. Moreover, it is robust as its accuracy goes hand in hand with its confidence.}, }
Endnote
%0 Report %A Kaltenpoth, David %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T We Are Not Your Real Parents: Telling Causal from Confounded using MDL : %G eng %U http://hdl.handle.net/21.11116/0000-0003-FFEE-3 %U http://arxiv.org/abs/1901.06950 %D 2019 %X Given data over variables $(X_1,...,X_m, Y)$ we consider the problem of finding out whether $X$ jointly causes $Y$ or whether they are all confounded by an unobserved latent variable $Z$. To do so, we take an information-theoretic approach based on Kolmogorov complexity. In a nutshell, we follow the postulate that first encoding the true cause, and then the effects given that cause, results in a shorter description than any other encoding of the observed variables. The ideal score is not computable, and hence we have to approximate it. We propose to do so using the Minimum Description Length (MDL) principle. We compare the MDL scores under the models where $X$ causes $Y$ and where there exists a latent variables $Z$ confounding both $X$ and $Y$ and show our scores are consistent. To find potential confounders we propose using latent factor modeling, in particular, probabilistic PCA (PPCA). Empirical evaluation on both synthetic and real-world data shows that our method, CoCa, performs very well -- even when the true generating process of the data is far from the assumptions made by the models we use. Moreover, it is robust as its accuracy goes hand in hand with its confidence. %K Computer Science, Learning, cs.LG,Statistics, Machine Learning, stat.ML
[45]
D. Kaltenpoth and J. Vreeken, “We Are Not Your Real Parents: Telling Causal from Confounded by MDL,” in Proceedings of the 2019 SIAM International Conference on Data Mining (SDM 2019), Calgary, Canada, 2019.
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@inproceedings{Kaltenpoth_SDM2019, TITLE = {We Are Not Your Real Parents: {T}elling Causal from Confounded by {MDL}}, AUTHOR = {Kaltenpoth, David and Vreeken, Jilles}, LANGUAGE = {eng}, ISBN = {978-1-61197-567-3}, DOI = {10.1137/1.9781611975673.23}, PUBLISHER = {SIAM}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of the 2019 SIAM International Conference on Data Mining (SDM 2019)}, EDITOR = {Berger-Wolf, Tanya and Chawla, Nitesh}, PAGES = {199--207}, ADDRESS = {Calgary, Canada}, }
Endnote
%0 Conference Proceedings %A Kaltenpoth, David %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T We Are Not Your Real Parents: Telling Causal from Confounded by MDL : %G eng %U http://hdl.handle.net/21.11116/0000-0003-0D37-2 %R 10.1137/1.9781611975673.23 %D 2019 %B SIAM International Conference on Data Mining %Z date of event: 2019-05-02 - 2019-05-04 %C Calgary, Canada %B Proceedings of the 2019 SIAM International Conference on Data Mining %E Berger-Wolf, Tanya; Chawla, Nitesh %P 199 - 207 %I SIAM %@ 978-1-61197-567-3
[46]
S. Karaev, “Matrix factorization over diods and its applications in data mining,” Universität des Saarlandes, Saarbrücken, 2019.
Abstract
Matrix factorizations are an important tool in data mining, and they have been used extensively for finding latent patterns in the data. They often allow to separate structure from noise, as well as to considerably reduce the dimensionality of the input matrix. While classical matrix decomposition methods, such as nonnegative matrix factorization (NMF) and singular value decomposition (SVD), proved to be very useful in data analysis, they are limited by the underlying algebraic structure. NMF, in particular, tends to break patterns into smaller bits, often mixing them with each other. This happens because overlapping patterns interfere with each other, making it harder to tell them apart. In this thesis we study matrix factorization over algebraic structures known as dioids, which are characterized by the lack of additive inverse (“negative numbers”) and the idempotency of addition (a + a = a). Using dioids makes it easier to separate overlapping features, and, in particular, it allows to better deal with the above mentioned pattern breaking problem. We consider different types of dioids, that range from continuous (subtropical and tropical algebras) to discrete (Boolean algebra). Among these, the Boolean algebra is perhaps the most well known, and there exist methods that allow one to obtain high quality Boolean matrix factorizations in terms of the reconstruction error. In this work, however, a different objective function is used – the description length of the data, which enables us to obtain compact and highly interpretable results. The tropical and subtropical algebras, on the other hand, are much less known in the data mining field. While they find applications in areas such as job scheduling and discrete event systems, they are virtually unknown in the context of data analysis. We will use them to obtain idempotent nonnegative factorizations that are similar to NMF, but are better at separating the most prominent features of the data.
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@phdthesis{Karaevphd2019, TITLE = {Matrix factorization over diods and its applications in data mining}, AUTHOR = {Karaev, Sanjar}, LANGUAGE = {eng}, DOI = {10.22028/D291-28661}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, ABSTRACT = {Matrix factorizations are an important tool in data mining, and they have been used extensively for finding latent patterns in the data. They often allow to separate structure from noise, as well as to considerably reduce the dimensionality of the input matrix. While classical matrix decomposition methods, such as nonnegative matrix factorization (NMF) and singular value decomposition (SVD), proved to be very useful in data analysis, they are limited by the underlying algebraic structure. NMF, in particular, tends to break patterns into smaller bits, often mixing them with each other. This happens because overlapping patterns interfere with each other, making it harder to tell them apart. In this thesis we study matrix factorization over algebraic structures known as dioids, which are characterized by the lack of additive inverse ({\textquotedblleft}negative numbers{\textquotedblright}) and the idempotency of addition (a + a = a). Using dioids makes it easier to separate overlapping features, and, in particular, it allows to better deal with the above mentioned pattern breaking problem. We consider different types of dioids, that range from continuous (subtropical and tropical algebras) to discrete (Boolean algebra). Among these, the Boolean algebra is perhaps the most well known, and there exist methods that allow one to obtain high quality Boolean matrix factorizations in terms of the reconstruction error. In this work, however, a different objective function is used -- the description length of the data, which enables us to obtain compact and highly interpretable results. The tropical and subtropical algebras, on the other hand, are much less known in the data mining field. While they find applications in areas such as job scheduling and discrete event systems, they are virtually unknown in the context of data analysis. We will use them to obtain idempotent nonnegative factorizations that are similar to NMF, but are better at separating the most prominent features of the data.}, }
Endnote
%0 Thesis %A Karaev, Sanjar %Y Miettinen, Pauli %A referee: Weikum, Gerhard %A referee: van Leeuwen, Matthijs %+ Databases and Information Systems, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Matrix factorization over diods and its applications in data mining : %G eng %U http://hdl.handle.net/21.11116/0000-0005-4369-A %R 10.22028/D291-28661 %I Universität des Saarlandes %C Saarbrücken %D 2019 %P 113 p. %V phd %9 phd %X Matrix factorizations are an important tool in data mining, and they have been used extensively for finding latent patterns in the data. They often allow to separate structure from noise, as well as to considerably reduce the dimensionality of the input matrix. While classical matrix decomposition methods, such as nonnegative matrix factorization (NMF) and singular value decomposition (SVD), proved to be very useful in data analysis, they are limited by the underlying algebraic structure. NMF, in particular, tends to break patterns into smaller bits, often mixing them with each other. This happens because overlapping patterns interfere with each other, making it harder to tell them apart. In this thesis we study matrix factorization over algebraic structures known as dioids, which are characterized by the lack of additive inverse (“negative numbers”) and the idempotency of addition (a + a = a). Using dioids makes it easier to separate overlapping features, and, in particular, it allows to better deal with the above mentioned pattern breaking problem. We consider different types of dioids, that range from continuous (subtropical and tropical algebras) to discrete (Boolean algebra). Among these, the Boolean algebra is perhaps the most well known, and there exist methods that allow one to obtain high quality Boolean matrix factorizations in terms of the reconstruction error. In this work, however, a different objective function is used – the description length of the data, which enables us to obtain compact and highly interpretable results. The tropical and subtropical algebras, on the other hand, are much less known in the data mining field. While they find applications in areas such as job scheduling and discrete event systems, they are virtually unknown in the context of data analysis. We will use them to obtain idempotent nonnegative factorizations that are similar to NMF, but are better at separating the most prominent features of the data. %U https://publikationen.sulb.uni-saarland.de/handle/20.500.11880/27903
[47]
S. Karaev and P. Miettinen, “Algorithms for Approximate Subtropical Matrix Factorization,” Data Mining and Knowledge Discovery, vol. 33, no. 2, 2019.
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@article{Karaev_DMKD2018, TITLE = {Algorithms for Approximate Subtropical Matrix Factorization}, AUTHOR = {Karaev, Sanjar and Miettinen, Pauli}, LANGUAGE = {eng}, DOI = {10.1007/s10618-018-0599-1}, PUBLISHER = {Springer}, ADDRESS = {New York, NY}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, JOURNAL = {Data Mining and Knowledge Discovery}, VOLUME = {33}, NUMBER = {2}, PAGES = {526--576}, }
Endnote
%0 Journal Article %A Karaev, Sanjar %A Miettinen, Pauli %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Algorithms for Approximate Subtropical Matrix Factorization : %G eng %U http://hdl.handle.net/21.11116/0000-0002-9FD5-B %R 10.1007/s10618-018-0599-1 %7 2018 %D 2019 %J Data Mining and Knowledge Discovery %O DMKD %V 33 %N 2 %& 526 %P 526 - 576 %I Springer %C New York, NY
[48]
A. Konstantinidis, P. Irakleous, Z. Georgiou, D. Zeinalipour-Yazti, and P. K. Chrysanthis, “IoT Data Prefetching in Indoor Navigation SOAs,” ACM Transactions on Internet Technology, vol. 19, no. 1, 2019.
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@article{Konstantinidis:2018:IDP:3283809.3177777, TITLE = {{IoT} Data Prefetching in Indoor Navigation {SOAs}}, AUTHOR = {Konstantinidis, Andreas and Irakleous, Panagiotis and Georgiou, Zacharias and Zeinalipour-Yazti, Demetrios and Chrysanthis, Panos K.}, LANGUAGE = {eng}, ISSN = {1533-5399}, DOI = {10.1145/3177777}, PUBLISHER = {ACM}, ADDRESS = {New York, NY}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, JOURNAL = {ACM Transactions on Internet Technology}, VOLUME = {19}, NUMBER = {1}, EID = {10}, }
Endnote
%0 Journal Article %A Konstantinidis, Andreas %A Irakleous, Panagiotis %A Georgiou, Zacharias %A Zeinalipour-Yazti, Demetrios %A Chrysanthis, Panos K. %+ External Organizations External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T IoT Data Prefetching in Indoor Navigation SOAs : %G eng %U http://hdl.handle.net/21.11116/0000-0002-CA09-1 %R 10.1145/3177777 %7 2019 %D 2019 %J ACM Transactions on Internet Technology %O TOIT %V 19 %N 1 %Z sequence number: 10 %I ACM %C New York, NY %@ false
[49]
P. Lahoti, K. P. Gummadi, and G. Weikum, “Operationalizing Individual Fairness with Pairwise Fair Representations,” 2019. [Online]. Available: http://arxiv.org/abs/1907.01439. (arXiv: 1907.01439)
Abstract
We revisit the notion of individual fairness proposed by Dwork et al. A central challenge in operationalizing their approach is the difficulty in eliciting a human specification of a similarity metric. In this paper, we propose an operationalization of individual fairness that does not rely on a human specification of a distance metric. Instead, we propose novel approaches to elicit and leverage side-information on equally deserving individuals to counter subordination between social groups. We model this knowledge as a fairness graph, and learn a unified Pairwise Fair Representation(PFR) of the data that captures both data-driven similarity between individuals and the pairwise side-information in fairness graph. We elicit fairness judgments from a variety of sources, including humans judgments for two real-world datasets on recidivism prediction (COMPAS) and violent neighborhood prediction (Crime & Communities). Our experiments show that the PFR model for operationalizing individual fairness is practically viable.
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@online{Lahoti_arXiv1907.01439, TITLE = {Operationalizing Individual Fairness with Pairwise Fair Representations}, AUTHOR = {Lahoti, Preethi and Gummadi, Krishna P. and Weikum, Gerhard}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1907.01439}, EPRINT = {1907.01439}, EPRINTTYPE = {arXiv}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, ABSTRACT = {We revisit the notion of individual fairness proposed by Dwork et al. A central challenge in operationalizing their approach is the difficulty in eliciting a human specification of a similarity metric. In this paper, we propose an operationalization of individual fairness that does not rely on a human specification of a distance metric. Instead, we propose novel approaches to elicit and leverage side-information on equally deserving individuals to counter subordination between social groups. We model this knowledge as a fairness graph, and learn a unified Pairwise Fair Representation(PFR) of the data that captures both data-driven similarity between individuals and the pairwise side-information in fairness graph. We elicit fairness judgments from a variety of sources, including humans judgments for two real-world datasets on recidivism prediction (COMPAS) and violent neighborhood prediction (Crime & Communities). Our experiments show that the PFR model for operationalizing individual fairness is practically viable.}, }
Endnote
%0 Report %A Lahoti, Preethi %A Gummadi, Krishna P. %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Operationalizing Individual Fairness with Pairwise Fair Representations : %G eng %U http://hdl.handle.net/21.11116/0000-0003-FF17-5 %U http://arxiv.org/abs/1907.01439 %D 2019 %X We revisit the notion of individual fairness proposed by Dwork et al. A central challenge in operationalizing their approach is the difficulty in eliciting a human specification of a similarity metric. In this paper, we propose an operationalization of individual fairness that does not rely on a human specification of a distance metric. Instead, we propose novel approaches to elicit and leverage side-information on equally deserving individuals to counter subordination between social groups. We model this knowledge as a fairness graph, and learn a unified Pairwise Fair Representation(PFR) of the data that captures both data-driven similarity between individuals and the pairwise side-information in fairness graph. We elicit fairness judgments from a variety of sources, including humans judgments for two real-world datasets on recidivism prediction (COMPAS) and violent neighborhood prediction (Crime & Communities). Our experiments show that the PFR model for operationalizing individual fairness is practically viable. %K Computer Science, Learning, cs.LG,Statistics, Machine Learning, stat.ML
[50]
P. Lahoti, K. Gummadi, and G. Weikum, “Operationalizing Individual Fairness with Pairwise Fair Representations,” Proceedings of the VLDB Endowment (Proc. VLDB 2019), vol. 13, no. 4, 2019.
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@article{Lahoti2019_PVLDB, TITLE = {Operationalizing Individual Fairness with Pairwise Fair Representations}, AUTHOR = {Lahoti, Preethi and Gummadi, Krishna and Weikum, Gerhard}, LANGUAGE = {eng}, DOI = {10.14778/3372716.3372723}, PUBLISHER = {VLDB Endowment Inc.}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, JOURNAL = {Proceedings of the VLDB Endowment (Proc. VLDB)}, VOLUME = {13}, NUMBER = {4}, PAGES = {506--518}, BOOKTITLE = {Proceedings of the 45h International Conference on Very Large Data Bases (VLDB 2019)}, EDITOR = {Balazinska, Magdalena and Zhou, Xiaofang}, }
Endnote
%0 Journal Article %A Lahoti, Preethi %A Gummadi, Krishna %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Operationalizing Individual Fairness with Pairwise Fair Representations : %G eng %U http://hdl.handle.net/21.11116/0000-0005-8168-4 %R 10.14778/3372716.3372723 %7 2019 %D 2019 %J Proceedings of the VLDB Endowment %O PVLDB %V 13 %N 4 %& 506 %P 506 - 518 %I VLDB Endowment Inc. %B Proceedings of the 45h International Conference on Very Large Data Bases %O VLDB 2019 Los Angeles, CA, USA, 26-30 August 2019
[51]
P. Lahoti, K. Gummadi, and G. Weikum, “iFair: Learning Individually Fair Data Representations for Algorithmic Decision Making,” in ICDE 2019, 35th IEEE International Conference on Data Engineering, Macau, China, 2019.
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@inproceedings{Lahoti_ICDE2019, TITLE = {{iFair}: {L}earning Individually Fair Data Representations for Algorithmic Decision Making}, AUTHOR = {Lahoti, Preethi and Gummadi, Krishna and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-5386-7474-1}, DOI = {10.1109/ICDE.2019.00121}, PUBLISHER = {IEEE}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {ICDE 2019, 35th IEEE International Conference on Data Engineering}, PAGES = {1334--1345}, ADDRESS = {Macau, China}, }
Endnote
%0 Conference Proceedings %A Lahoti, Preethi %A Gummadi, Krishna %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T iFair: Learning Individually Fair Data Representations for Algorithmic Decision Making : %G eng %U http://hdl.handle.net/21.11116/0000-0003-F395-2 %R 10.1109/ICDE.2019.00121 %D 2019 %B 35th IEEE International Conference on Data Engineering %Z date of event: 2019-04-08 - 2019-04-12 %C Macau, China %B ICDE 2019 %P 1334 - 1345 %I IEEE %@ 978-1-5386-7474-1
[52]
X. Lu, S. Pramanik, R. Saha Roy, A. Abujabal, Y. Wang, and G. Weikum, “Answering Complex Questions by Joining Multi-Document Evidence with Quasi Knowledge Graphs,” in SIGIR’19, 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, Paris, France, 2019.
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@inproceedings{lu19answering, TITLE = {Answering Complex Questions by Joining Multi-Document Evidence with Quasi Knowledge Graphs}, AUTHOR = {Lu, Xiaolu and Pramanik, Soumajit and Saha Roy, Rishiraj and Abujabal, Abdalghani and Wang, Yafang and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-6172-9}, DOI = {10.1145/3331184.3331252}, PUBLISHER = {ACM}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {SIGIR'19, 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval}, EDITOR = {Piwowarski, Benjamin and Chevalier, Max and Gaussier, {\'E}ric}, PAGES = {105--114}, ADDRESS = {Paris, France}, }
Endnote
%0 Conference Proceedings %A Lu, Xiaolu %A Pramanik, Soumajit %A Saha Roy, Rishiraj %A Abujabal, Abdalghani %A Wang, Yafang %A Weikum, Gerhard %+ External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Answering Complex Questions by Joining Multi-Document Evidence with Quasi Knowledge Graphs : %G eng %U http://hdl.handle.net/21.11116/0000-0003-7085-8 %R 10.1145/3331184.3331252 %D 2019 %B 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval %Z date of event: 2019-07-21 - 2019-07-25 %C Paris, France %B SIGIR'19 %E Piwowarski, Benjamin; Chevalier, Max; Gaussier, Éric %P 105 - 114 %I ACM %@ 978-1-4503-6172-9
[53]
X. Lu, S. Pramanik, R. Saha Roy, A. Abujabal, Y. Wang, and G. Weikum, “Answering Complex Questions by Joining Multi-Document Evidence with Quasi Knowledge Graphs,” SIGIR 2019, 2019. (arXiv: 1908.00469)
Abstract
Direct answering of questions that involve multiple entities and relations is a challenge for text-based QA. This problem is most pronounced when answers can be found only by joining evidence from multiple documents. Curated knowledge graphs (KGs) may yield good answers, but are limited by their inherent incompleteness and potential staleness. This paper presents QUEST, a method that can answer complex questions directly from textual sources on-the-fly, by computing similarity joins over partial results from different documents. Our method is completely unsupervised, avoiding training-data bottlenecks and being able to cope with rapidly evolving ad hoc topics and formulation style in user questions. QUEST builds a noisy quasi KG with node and edge weights, consisting of dynamically retrieved entity names and relational phrases. It augments this graph with types and semantic alignments, and computes the best answers by an algorithm for Group Steiner Trees. We evaluate QUEST on benchmarks of complex questions, and show that it substantially outperforms state-of-the-art baselines.
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@article{Lu_arXiv1908.00469, TITLE = {Answering Complex Questions by Joining Multi-Document Evidence with Quasi Knowledge Graphs}, AUTHOR = {Lu, Xiaolu and Pramanik, Soumajit and Saha Roy, Rishiraj and Abujabal, Abdalghani and Wang, Yafang and Weikum, Gerhard}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1908.00469}, EPRINT = {1908.00469}, EPRINTTYPE = {arXiv}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Direct answering of questions that involve multiple entities and relations is a challenge for text-based QA. This problem is most pronounced when answers can be found only by joining evidence from multiple documents. Curated knowledge graphs (KGs) may yield good answers, but are limited by their inherent incompleteness and potential staleness. This paper presents QUEST, a method that can answer complex questions directly from textual sources on-the-fly, by computing similarity joins over partial results from different documents. Our method is completely unsupervised, avoiding training-data bottlenecks and being able to cope with rapidly evolving ad hoc topics and formulation style in user questions. QUEST builds a noisy quasi KG with node and edge weights, consisting of dynamically retrieved entity names and relational phrases. It augments this graph with types and semantic alignments, and computes the best answers by an algorithm for Group Steiner Trees. We evaluate QUEST on benchmarks of complex questions, and show that it substantially outperforms state-of-the-art baselines.}, JOURNAL = {SIGIR 2019}, }
Endnote
%0 Journal Article %A Lu, Xiaolu %A Pramanik, Soumajit %A Saha Roy, Rishiraj %A Abujabal, Abdalghani %A Wang, Yafang %A Weikum, Gerhard %+ External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Answering Complex Questions by Joining Multi-Document Evidence with Quasi Knowledge Graphs : %G eng %U http://hdl.handle.net/21.11116/0000-0005-83B3-C %U http://arxiv.org/abs/1908.00469 %7 2019 %D 2019 %X Direct answering of questions that involve multiple entities and relations is a challenge for text-based QA. This problem is most pronounced when answers can be found only by joining evidence from multiple documents. Curated knowledge graphs (KGs) may yield good answers, but are limited by their inherent incompleteness and potential staleness. This paper presents QUEST, a method that can answer complex questions directly from textual sources on-the-fly, by computing similarity joins over partial results from different documents. Our method is completely unsupervised, avoiding training-data bottlenecks and being able to cope with rapidly evolving ad hoc topics and formulation style in user questions. QUEST builds a noisy quasi KG with node and edge weights, consisting of dynamically retrieved entity names and relational phrases. It augments this graph with types and semantic alignments, and computes the best answers by an algorithm for Group Steiner Trees. We evaluate QUEST on benchmarks of complex questions, and show that it substantially outperforms state-of-the-art baselines. %K Computer Science, Information Retrieval, cs.IR %J SIGIR 2019
[54]
S. MacAvaney, A. Yates, A. Cohan, and N. Goharian, “CEDR: Contextualized Embeddings for Document Ranking,” in SIGIR’19, 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, Paris, France, 2019.
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@inproceedings{MacAvaney_SIGIR2019, TITLE = {{CEDR}: Contextualized Embeddings for Document Ranking}, AUTHOR = {MacAvaney, Sean and Yates, Andrew and Cohan, Arman and Goharian, Nazli}, LANGUAGE = {eng}, ISBN = {9781450361729}, DOI = {10.1145/3331184.3331317}, PUBLISHER = {ACM}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, BOOKTITLE = {SIGIR'19, 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval}, PAGES = {1101--1104}, ADDRESS = {Paris, France}, }
Endnote
%0 Conference Proceedings %A MacAvaney, Sean %A Yates, Andrew %A Cohan, Arman %A Goharian, Nazli %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T CEDR: Contextualized Embeddings for Document Ranking : %G eng %U http://hdl.handle.net/21.11116/0000-0004-02D3-B %R 10.1145/3331184.3331317 %D 2019 %B 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval %Z date of event: 2019-07-21 - 2019-07-25 %C Paris, France %B SIGIR'19 %P 1101 - 1104 %I ACM %@ 9781450361729
[55]
S. MacAvaney, A. Yates, A. Cohan, and N. Goharian, “CEDR: Contextualized Embeddings for Document Ranking,” 2019. [Online]. Available: http://arxiv.org/abs/1904.07094. (arXiv: 1904.07094)
Abstract
Although considerable attention has been given to neural ranking architectures recently, far less attention has been paid to the term representations that are used as input to these models. In this work, we investigate how two pretrained contextualized language modes (ELMo and BERT) can be utilized for ad-hoc document ranking. Through experiments on TREC benchmarks, we find that several existing neural ranking architectures can benefit from the additional context provided by contextualized language models. Furthermore, we propose a joint approach that incorporates BERT's classification vector into existing neural models and show that it outperforms state-of-the-art ad-hoc ranking baselines. We call this joint approach CEDR (Contextualized Embeddings for Document Ranking). We also address practical challenges in using these models for ranking, including the maximum input length imposed by BERT and runtime performance impacts of contextualized language models.
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@online{MacAvaney_arXiv1904.07094, TITLE = {{CEDR}: Contextualized Embeddings for Document Ranking}, AUTHOR = {MacAvaney, Sean and Yates, Andrew and Cohan, Arman and Goharian, Nazli}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1904.07094}, EPRINT = {1904.07094}, EPRINTTYPE = {arXiv}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Although considerable attention has been given to neural ranking architectures recently, far less attention has been paid to the term representations that are used as input to these models. In this work, we investigate how two pretrained contextualized language modes (ELMo and BERT) can be utilized for ad-hoc document ranking. Through experiments on TREC benchmarks, we find that several existing neural ranking architectures can benefit from the additional context provided by contextualized language models. Furthermore, we propose a joint approach that incorporates BERT's classification vector into existing neural models and show that it outperforms state-of-the-art ad-hoc ranking baselines. We call this joint approach CEDR (Contextualized Embeddings for Document Ranking). We also address practical challenges in using these models for ranking, including the maximum input length imposed by BERT and runtime performance impacts of contextualized language models.}, }
Endnote
%0 Report %A MacAvaney, Sean %A Yates, Andrew %A Cohan, Arman %A Goharian, Nazli %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T CEDR: Contextualized Embeddings for Document Ranking : %G eng %U http://hdl.handle.net/21.11116/0000-0004-02C7-9 %U http://arxiv.org/abs/1904.07094 %D 2019 %X Although considerable attention has been given to neural ranking architectures recently, far less attention has been paid to the term representations that are used as input to these models. In this work, we investigate how two pretrained contextualized language modes (ELMo and BERT) can be utilized for ad-hoc document ranking. Through experiments on TREC benchmarks, we find that several existing neural ranking architectures can benefit from the additional context provided by contextualized language models. Furthermore, we propose a joint approach that incorporates BERT's classification vector into existing neural models and show that it outperforms state-of-the-art ad-hoc ranking baselines. We call this joint approach CEDR (Contextualized Embeddings for Document Ranking). We also address practical challenges in using these models for ranking, including the maximum input length imposed by BERT and runtime performance impacts of contextualized language models. %K Computer Science, Information Retrieval, cs.IR,Computer Science, Computation and Language, cs.CL
[56]
S. MacAvaney, A. Yates, A. Cohan, L. Soldaini, K. Hui, N. Goharian, and O. Frieder, “Overcoming Low-Utility Facets for Complex Answer Retrieval,” Information Retrieval Journal, vol. 22, no. 3–4, 2019.
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@article{MacAvaney2019, TITLE = {Overcoming Low-Utility Facets for Complex Answer Retrieval}, AUTHOR = {MacAvaney, Sean and Yates, Andrew and Cohan, Arman and Soldaini, Luca and Hui, Kai and Goharian, Nazli and Frieder, Ophir}, LANGUAGE = {eng}, ISSN = {1386-4564}, DOI = {10.1007/s10791-018-9343-0}, PUBLISHER = {Springer}, ADDRESS = {New York, NY}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, JOURNAL = {Information Retrieval Journal}, VOLUME = {22}, NUMBER = {3-4}, PAGES = {395--418}, }
Endnote
%0 Journal Article %A MacAvaney, Sean %A Yates, Andrew %A Cohan, Arman %A Soldaini, Luca %A Hui, Kai %A Goharian, Nazli %A Frieder, Ophir %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations External Organizations External Organizations %T Overcoming Low-Utility Facets for Complex Answer Retrieval : %G eng %U http://hdl.handle.net/21.11116/0000-0003-C4A1-9 %R 10.1007/s10791-018-9343-0 %7 2019 %D 2019 %J Information Retrieval Journal %V 22 %N 3-4 %& 395 %P 395 - 418 %I Springer %C New York, NY %@ false
[57]
S. MacAvaney, A. Yates, K. Hui, and O. Frieder, “Content-Based Weak Supervision for Ad-Hoc Re-Ranking,” 2019. [Online]. Available: http://arxiv.org/abs/1707.00189. (arXiv: 1707.00189)
Abstract
One challenge with neural ranking is the need for a large amount of manually-labeled relevance judgments for training. In contrast with prior work, we examine the use of weak supervision sources for training that yield pseudo query-document pairs that already exhibit relevance (e.g., newswire headline-content pairs and encyclopedic heading-paragraph pairs). We also propose filtering techniques to eliminate training samples that are too far out of domain using two techniques: a heuristic-based approach and novel supervised filter that re-purposes a neural ranker. Using several leading neural ranking architectures and multiple weak supervision datasets, we show that these sources of training pairs are effective on their own (outperforming prior weak supervision techniques), and that filtering can further improve performance.
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@online{MacAvaney_arXiv1707.00189, TITLE = {Content-Based Weak Supervision for Ad-Hoc Re-Ranking}, AUTHOR = {MacAvaney, Sean and Yates, Andrew and Hui, Kai and Frieder, Ophir}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1707.00189}, EPRINT = {1707.00189}, EPRINTTYPE = {arXiv}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, ABSTRACT = {One challenge with neural ranking is the need for a large amount of manually-labeled relevance judgments for training. In contrast with prior work, we examine the use of weak supervision sources for training that yield pseudo query-document pairs that already exhibit relevance (e.g., newswire headline-content pairs and encyclopedic heading-paragraph pairs). We also propose filtering techniques to eliminate training samples that are too far out of domain using two techniques: a heuristic-based approach and novel supervised filter that re-purposes a neural ranker. Using several leading neural ranking architectures and multiple weak supervision datasets, we show that these sources of training pairs are effective on their own (outperforming prior weak supervision techniques), and that filtering can further improve performance.}, }
Endnote
%0 Report %A MacAvaney, Sean %A Yates, Andrew %A Hui, Kai %A Frieder, Ophir %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T Content-Based Weak Supervision for Ad-Hoc Re-Ranking : %G eng %U http://hdl.handle.net/21.11116/0000-0005-6B59-0 %U http://arxiv.org/abs/1707.00189 %D 2019 %X One challenge with neural ranking is the need for a large amount of manually-labeled relevance judgments for training. In contrast with prior work, we examine the use of weak supervision sources for training that yield pseudo query-document pairs that already exhibit relevance (e.g., newswire headline-content pairs and encyclopedic heading-paragraph pairs). We also propose filtering techniques to eliminate training samples that are too far out of domain using two techniques: a heuristic-based approach and novel supervised filter that re-purposes a neural ranker. Using several leading neural ranking architectures and multiple weak supervision datasets, we show that these sources of training pairs are effective on their own (outperforming prior weak supervision techniques), and that filtering can further improve performance. %K Computer Science, Information Retrieval, cs.IR,Computer Science, Computation and Language, cs.CL
[58]
S. MacAvaney, A. Yates, K. Hui, and O. Frieder, “Content-Based Weak Supervision for Ad-Hoc Re-Ranking,” in SIGIR’19, 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, Paris, France, 2019.
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@inproceedings{MacAvaney_SIGIR2019b, TITLE = {Content-Based Weak Supervision for Ad-Hoc Re-Ranking}, AUTHOR = {MacAvaney, Sean and Yates, Andrew and Hui, Kai and Frieder, Ophir}, LANGUAGE = {eng}, ISBN = {9781450361729}, DOI = {10.1145/3331184.3331316}, PUBLISHER = {ACM}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, BOOKTITLE = {SIGIR'19, 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval}, PAGES = {993--996}, ADDRESS = {Paris, France}, }
Endnote
%0 Conference Proceedings %A MacAvaney, Sean %A Yates, Andrew %A Hui, Kai %A Frieder, Ophir %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T Content-Based Weak Supervision for Ad-Hoc Re-Ranking : %G eng %U http://hdl.handle.net/21.11116/0000-0005-6B55-4 %R 10.1145/3331184.3331316 %D 2019 %B 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval %Z date of event: 2019-07-21 - 2019-07-25 %C Paris, France %B SIGIR'19 %P 993 - 996 %I ACM %@ 9781450361729
[59]
P. Mandros, M. Boley, and J. Vreeken, “Discovering Reliable Dependencies from Data: Hardness and Improved Algorithms,” in Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI 2019), Macao, 2019.
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@inproceedings{mandros_IJCAI2019, TITLE = {Discovering Reliable Dependencies from Data: {H}ardness and Improved Algorithms}, AUTHOR = {Mandros, Panagiotis and Boley, Mario and Vreeken, Jilles}, LANGUAGE = {eng}, ISBN = {978-0-9992411-4-1}, DOI = {10.24963/ijcai.2019/864}, PUBLISHER = {IJCAI}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI 2019)}, EDITOR = {Krais, Sarit}, PAGES = {6206--6210}, ADDRESS = {Macao}, }
Endnote
%0 Conference Proceedings %A Mandros, Panagiotis %A Boley, Mario %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T Discovering Reliable Dependencies from Data: Hardness and Improved Algorithms : %G eng %U http://hdl.handle.net/21.11116/0000-0005-848A-A %R 10.24963/ijcai.2019/864 %D 2019 %B Twenty-Eighth International Joint Conference on Artificial Intelligence %Z date of event: 2019-08-10 - 2019-08-16 %C Macao %B Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence %E Krais, Sarit %P 6206 - 6210 %I IJCAI %@ 978-0-9992411-4-1 %U https://www.ijcai.org/Proceedings/2019/0864.pdf
[60]
P. Mandros, M. Boley, and J. Vreeken, “Discovering Reliable Correlations in Categorical Data,” 2019. [Online]. Available: http://arxiv.org/abs/1908.11682. (arXiv: 1908.11682)
Abstract
In many scientific tasks we are interested in discovering whether there exist any correlations in our data. This raises many questions, such as how to reliably and interpretably measure correlation between a multivariate set of attributes, how to do so without having to make assumptions on distribution of the data or the type of correlation, and, how to efficiently discover the top-most reliably correlated attribute sets from data. In this paper we answer these questions for discovery tasks in categorical data. In particular, we propose a corrected-for-chance, consistent, and efficient estimator for normalized total correlation, by which we obtain a reliable, naturally interpretable, non-parametric measure for correlation over multivariate sets. For the discovery of the top-k correlated sets, we derive an effective algorithmic framework based on a tight bounding function. This framework offers exact, approximate, and heuristic search. Empirical evaluation shows that already for small sample sizes the estimator leads to low-regret optimization outcomes, while the algorithms are shown to be highly effective for both large and high-dimensional data. Through two case studies we confirm that our discovery framework identifies interesting and meaningful correlations.
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@online{Mandros_arXiv1908.11682, TITLE = {Discovering Reliable Correlations in Categorical Data}, AUTHOR = {Mandros, Panagiotis and Boley, Mario and Vreeken, Jilles}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1908.11682}, EPRINT = {1908.11682}, EPRINTTYPE = {arXiv}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, ABSTRACT = {In many scientific tasks we are interested in discovering whether there exist any correlations in our data. This raises many questions, such as how to reliably and interpretably measure correlation between a multivariate set of attributes, how to do so without having to make assumptions on distribution of the data or the type of correlation, and, how to efficiently discover the top-most reliably correlated attribute sets from data. In this paper we answer these questions for discovery tasks in categorical data. In particular, we propose a corrected-for-chance, consistent, and efficient estimator for normalized total correlation, by which we obtain a reliable, naturally interpretable, non-parametric measure for correlation over multivariate sets. For the discovery of the top-k correlated sets, we derive an effective algorithmic framework based on a tight bounding function. This framework offers exact, approximate, and heuristic search. Empirical evaluation shows that already for small sample sizes the estimator leads to low-regret optimization outcomes, while the algorithms are shown to be highly effective for both large and high-dimensional data. Through two case studies we confirm that our discovery framework identifies interesting and meaningful correlations.}, }
Endnote
%0 Report %A Mandros, Panagiotis %A Boley, Mario %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T Discovering Reliable Correlations in Categorical Data : %G eng %U http://hdl.handle.net/21.11116/0000-0005-8491-1 %U http://arxiv.org/abs/1908.11682 %D 2019 %X In many scientific tasks we are interested in discovering whether there exist any correlations in our data. This raises many questions, such as how to reliably and interpretably measure correlation between a multivariate set of attributes, how to do so without having to make assumptions on distribution of the data or the type of correlation, and, how to efficiently discover the top-most reliably correlated attribute sets from data. In this paper we answer these questions for discovery tasks in categorical data. In particular, we propose a corrected-for-chance, consistent, and efficient estimator for normalized total correlation, by which we obtain a reliable, naturally interpretable, non-parametric measure for correlation over multivariate sets. For the discovery of the top-k correlated sets, we derive an effective algorithmic framework based on a tight bounding function. This framework offers exact, approximate, and heuristic search. Empirical evaluation shows that already for small sample sizes the estimator leads to low-regret optimization outcomes, while the algorithms are shown to be highly effective for both large and high-dimensional data. Through two case studies we confirm that our discovery framework identifies interesting and meaningful correlations. %K Computer Science, Learning, cs.LG,Computer Science, Databases, cs.DB,Computer Science, Information Theory, cs.IT,Mathematics, Information Theory, math.IT,Statistics, Machine Learning, stat.ML
[61]
A. Marx and J. Vreeken, “Testing Conditional Independence on Discrete Data using Stochastic Complexity,” in Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS 2019), Naha, Okinawa, Japan, 2019.
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@inproceedings{Marx_AISTATS2019, TITLE = {Testing Conditional Independence on Discrete Data using Stochastic Complexity}, AUTHOR = {Marx, Alexander and Vreeken, Jilles}, LANGUAGE = {eng}, PUBLISHER = {PMLR}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS 2019)}, EDITOR = {Chaudhuri, Kamalika and Sugiyama, Masashi}, PAGES = {496--505}, SERIES = {Proceedings of the Machine Learning Research}, VOLUME = {89}, ADDRESS = {Naha, Okinawa, Japan}, }
Endnote
%0 Conference Proceedings %A Marx, Alexander %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Testing Conditional Independence on Discrete Data using Stochastic Complexity : %G eng %U http://hdl.handle.net/21.11116/0000-0003-0D3C-D %D 2019 %B 22nd International Conference on Artificial Intelligence and Statistics %Z date of event: 2019-04-16 - 2019-04-18 %C Naha, Okinawa, Japan %B Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics %E Chaudhuri, Kamalika; Sugiyama, Masashi %P 496 - 505 %I PMLR %B Proceedings of the Machine Learning Research %N 89 %U http://proceedings.mlr.press/v89/marx19a/marx19a.pdf
[62]
A. Marx and J. Vreeken, “Testing Conditional Independence on Discrete Data using Stochastic Complexity,” 2019. [Online]. Available: http://arxiv.org/abs/1903.04829. (arXiv: 1903.04829)
Abstract
Testing for conditional independence is a core aspect of constraint-based causal discovery. Although commonly used tests are perfect in theory, they often fail to reject independence in practice, especially when conditioning on multiple variables. We focus on discrete data and propose a new test based on the notion of algorithmic independence that we instantiate using stochastic complexity. Amongst others, we show that our proposed test, SCI, is an asymptotically unbiased as well as $L_2$ consistent estimator for conditional mutual information (CMI). Further, we show that SCI can be reformulated to find a sensible threshold for CMI that works well on limited samples. Empirical evaluation shows that SCI has a lower type II error than commonly used tests. As a result, we obtain a higher recall when we use SCI in causal discovery algorithms, without compromising the precision.
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@online{Marx_arXiv1903.04829, TITLE = {Testing Conditional Independence on Discrete Data using Stochastic Complexity}, AUTHOR = {Marx, Alexander and Vreeken, Jilles}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1903.04829}, EPRINT = {1903.04829}, EPRINTTYPE = {arXiv}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Testing for conditional independence is a core aspect of constraint-based causal discovery. Although commonly used tests are perfect in theory, they often fail to reject independence in practice, especially when conditioning on multiple variables. We focus on discrete data and propose a new test based on the notion of algorithmic independence that we instantiate using stochastic complexity. Amongst others, we show that our proposed test, SCI, is an asymptotically unbiased as well as $L_2$ consistent estimator for conditional mutual information (CMI). Further, we show that SCI can be reformulated to find a sensible threshold for CMI that works well on limited samples. Empirical evaluation shows that SCI has a lower type II error than commonly used tests. As a result, we obtain a higher recall when we use SCI in causal discovery algorithms, without compromising the precision.}, }
Endnote
%0 Report %A Marx, Alexander %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Testing Conditional Independence on Discrete Data using Stochastic Complexity : %G eng %U http://hdl.handle.net/21.11116/0000-0004-027A-1 %U http://arxiv.org/abs/1903.04829 %D 2019 %X Testing for conditional independence is a core aspect of constraint-based causal discovery. Although commonly used tests are perfect in theory, they often fail to reject independence in practice, especially when conditioning on multiple variables. We focus on discrete data and propose a new test based on the notion of algorithmic independence that we instantiate using stochastic complexity. Amongst others, we show that our proposed test, SCI, is an asymptotically unbiased as well as $L_2$ consistent estimator for conditional mutual information (CMI). Further, we show that SCI can be reformulated to find a sensible threshold for CMI that works well on limited samples. Empirical evaluation shows that SCI has a lower type II error than commonly used tests. As a result, we obtain a higher recall when we use SCI in causal discovery algorithms, without compromising the precision. %K Statistics, Machine Learning, stat.ML,Computer Science, Learning, cs.LG
[63]
A. Marx and J. Vreeken, “Telling Cause from Effect by Local and Global Regression,” Knowledge and Information Systems, vol. 60, no. 3, 2019.
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@article{marx:19:crack, TITLE = {Telling Cause from Effect by Local and Global Regression}, AUTHOR = {Marx, Alexander and Vreeken, Jilles}, LANGUAGE = {eng}, ISSN = {0219-1377}, DOI = {10.1007/s10115-018-1286-7}, PUBLISHER = {Springer}, ADDRESS = {New York, NY}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, JOURNAL = {Knowledge and Information Systems}, VOLUME = {60}, NUMBER = {3}, PAGES = {1277--1305}, }
Endnote
%0 Journal Article %A Marx, Alexander %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Telling Cause from Effect by Local and Global Regression : %G eng %U http://hdl.handle.net/21.11116/0000-0002-9EAD-A %R 10.1007/s10115-018-1286-7 %7 2018-12-07 %D 2019 %J Knowledge and Information Systems %V 60 %N 3 %& 1277 %P 1277 - 1305 %I Springer %C New York, NY %@ false
[64]
A. Marx and J. Vreeken, “Identifiability of Cause and Effect using Regularized Regression,” in KDD’19, 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, AK, USA, 2019.
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@inproceedings{Marx_KDD2019, TITLE = {Identifiability of Cause and Effect using Regularized Regression}, AUTHOR = {Marx, Alexander and Vreeken, Jilles}, LANGUAGE = {eng}, ISBN = {978-1-4503-6201-6}, DOI = {10.1145/3292500.3330854}, PUBLISHER = {ACM}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, BOOKTITLE = {KDD'19, 25th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining}, PAGES = {852--861}, ADDRESS = {Anchorage, AK, USA}, }
Endnote
%0 Conference Proceedings %A Marx, Alexander %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Identifiability of Cause and Effect using Regularized Regression : %G eng %U http://hdl.handle.net/21.11116/0000-0004-858C-8 %R 10.1145/3292500.3330854 %D 2019 %B 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining %Z date of event: 2019-08-04 - 2019-08-08 %C Anchorage, AK, USA %B KDD'19 %P 852 - 861 %I ACM %@ 978-1-4503-6201-6
[65]
A. Marx and J. Vreeken, “Causal Inference on Multivariate and Mixed-Type Data,” in Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2018), Dublin, Ireland, 2019.
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@inproceedings{marx:18:crack, TITLE = {Causal Inference on Multivariate and Mixed-Type Data}, AUTHOR = {Marx, Alexander and Vreeken, Jilles}, LANGUAGE = {eng}, ISBN = {978-3-030-10927-1}, DOI = {10.1007/978-3-030-10928-8_39}, PUBLISHER = {Springer}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2019}, BOOKTITLE = {Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2018)}, EDITOR = {Berlingerio, Michele and Bonchi, Francesco and G{\"a}rtner, Thomas and Hurley, Neil and Ifrim, Georgiana}, PAGES = {655--671}, SERIES = {Lecture Notes in Artificial Intelligence}, VOLUME = {11052}, ADDRESS = {Dublin, Ireland}, }
Endnote
%0 Conference Proceedings %A Marx, Alexander %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Causal Inference on Multivariate and Mixed-Type Data : %G eng %U http://hdl.handle.net/21.11116/0000-0002-9E86-5 %R 10.1007/978-3-030-10928-8_39 %D 2019 %B European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases %Z date of event: 2018-09-10 - 2018-09-14 %C Dublin, Ireland %B Machine Learning and Knowledge Discovery in Databases %E Berlingerio, Michele; Bonchi, Francesco; Gärtner, Thomas; Hurley, Neil; Ifrim, Georgiana %P 655 - 671 %I Springer %@ 978-3-030-10927-1 %B Lecture Notes in Artificial Intelligence %N 11052
[66]
A. Marx and J. Vreeken, “Approximating Algorithmic Conditional Independence for Discrete Data,” in Proceedings of the First AAAI Spring Symposium Beyond Curve Fitting: Causation, Counterfactuals, and Imagination-based AI, Stanford, CA, USA. (Accepted/in press)
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@inproceedings{Marx_AAAISpringSymp2019, TITLE = {Approximating Algorithmic Conditional Independence for Discrete Data}, AUTHOR = {Marx, Alexander and Vreeken, Jilles}, LANGUAGE = {eng}, YEAR = {2019}, PUBLREMARK = {Accepted}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of the First AAAI Spring Symposium Beyond Curve Fitting: Causation, Counterfactuals, and Imagination-based AI}, ADDRESS = {Stanford, CA, USA}, }
Endnote
%0 Conference Proceedings %A Marx, Alexander %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Approximating Algorithmic Conditional Independence for Discrete Data : %G eng %U http://hdl.handle.net/21.11116/0000-0003-0D4C-B %D 2019 %B First AAAI Spring Symposium Beyond Curve Fitting: Causation, Counterfactuals, and Imagination-based AI %Z date of event: 2019-05-25 - 2019-05-27 %C Stanford, CA, USA %B Proceedings of the First AAAI Spring Symposium Beyond Curve Fitting: Causation, Counterfactuals, and Imagination-based AI
[67]
S. Metzler, S. Günnemann, and P. Miettinen, “Stability and Dynamics of Communities on Online Question-Answer Sites,” Social Networks, vol. 58, 2019.
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@article{Metzler2019, TITLE = {Stability and Dynamics of Communities on Online Question-Answer Sites}, AUTHOR = {Metzler, Saskia and G{\"u}nnemann, Stephan and Miettinen, Pauli}, LANGUAGE = {eng}, ISSN = {0378-8733}, DOI = {10.1016/j.socnet.2018.12.004}, PUBLISHER = {Elsevier}, ADDRESS = {Amsterdam}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, JOURNAL = {Social Networks}, VOLUME = {58}, PAGES = {50--58}, }
Endnote
%0 Journal Article %A Metzler, Saskia %A Günnemann, Stephan %A Miettinen, Pauli %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Stability and Dynamics of Communities on Online Question-Answer Sites : %G eng %U http://hdl.handle.net/21.11116/0000-0002-BCC1-0 %R 10.1016/j.socnet.2018.12.004 %7 2019 %D 2019 %J Social Networks %V 58 %& 50 %P 50 - 58 %I Elsevier %C Amsterdam %@ false
[68]
S. Metzler and P. Miettinen, “HyGen: Generating Random Graphs with Hyperbolic Communities,” Applied Network Science, vol. 4, 2019.
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@article{Metzler_Miettienen19, TITLE = {{HyGen}: {G}enerating Random Graphs with Hyperbolic Communities}, AUTHOR = {Metzler, Saskia and Miettinen, Pauli}, LANGUAGE = {eng}, ISSN = {2364-8228}, DOI = {10.1007/s41109-019-0166-8}, PUBLISHER = {Springer}, ADDRESS = {New York, NY}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, JOURNAL = {Applied Network Science}, VOLUME = {4}, EID = {53}, }
Endnote
%0 Journal Article %A Metzler, Saskia %A Miettinen, Pauli %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T HyGen: Generating Random Graphs with Hyperbolic Communities : %G eng %U http://hdl.handle.net/21.11116/0000-0005-8E5E-3 %R 10.1007/s41109-019-0166-8 %7 2019 %D 2019 %J Applied Network Science %O ANS Appl Netw Sci %V 4 %Z sequence number: 53 %I Springer %C New York, NY %@ false
[69]
M. Mohanty, M. Ramanath, M. Yahya, and G. Weikum, “Spec-QP: Speculative Query Planning for Joins over Knowledge Graphs,” in Advances in Database Technology (EDBT 2019), Lisbon, Portugal, 2019.
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@inproceedings{Mohanty:EDBT2019, TITLE = {{Spec-QP}: {S}peculative Query Planning for Joins over Knowledge Graphs}, AUTHOR = {Mohanty, Madhulika and Ramanath, Maya and Yahya, Mohamed and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-3-89318-081-3}, DOI = {10.5441/002/edbt.2019.07}, PUBLISHER = {OpenProceedings.org}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Advances in Database Technology (EDBT 2019)}, EDITOR = {Herschel, Melanie and Galhardas, Helena and Reinwald, Berthold and Fundlaki, Irini and Binning, Carsten and Kaoudi, Zoi}, PAGES = {61--72}, ADDRESS = {Lisbon, Portugal}, }
Endnote
%0 Conference Proceedings %A Mohanty, Madhulika %A Ramanath, Maya %A Yahya, Mohamed %A Weikum, Gerhard %+ External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Spec-QP: Speculative Query Planning for Joins over Knowledge Graphs : %G eng %U http://hdl.handle.net/21.11116/0000-0003-3A7D-1 %R 10.5441/002/edbt.2019.07 %D 2019 %B 22nd International Conference on Extending Database Technology %Z date of event: 2019-03-26 - 2019-03-29 %C Lisbon, Portugal %B Advances in Database Technology %E Herschel, Melanie; Galhardas, Helena; Reinwald, Berthold; Fundlaki, Irini; Binning, Carsten; Kaoudi, Zoi %P 61 - 72 %I OpenProceedings.org %@ 978-3-89318-081-3
[70]
S. Nag Chowdhury, N. Tandon, H. Ferhatosmanoglu, and G. Weikum, “VISIR: Visual and Semantic Image Label Refinement,” 2019. [Online]. Available: http://arxiv.org/abs/1909.00741. (arXiv: 1909.00741)
Abstract
The social media explosion has populated the Internet with a wealth of images. There are two existing paradigms for image retrieval: 1) content-based image retrieval (CBIR), which has traditionally used visual features for similarity search (e.g., SIFT features), and 2) tag-based image retrieval (TBIR), which has relied on user tagging (e.g., Flickr tags). CBIR now gains semantic expressiveness by advances in deep-learning-based detection of visual labels. TBIR benefits from query-and-click logs to automatically infer more informative labels. However, learning-based tagging still yields noisy labels and is restricted to concrete objects, missing out on generalizations and abstractions. Click-based tagging is limited to terms that appear in the textual context of an image or in queries that lead to a click. This paper addresses the above limitations by semantically refining and expanding the labels suggested by learning-based object detection. We consider the semantic coherence between the labels for different objects, leverage lexical and commonsense knowledge, and cast the label assignment into a constrained optimization problem solved by an integer linear program. Experiments show that our method, called VISIR, improves the quality of the state-of-the-art visual labeling tools like LSDA and YOLO.
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@online{Nag_arXiv1909.00741, TITLE = {{VISIR}: Visual and Semantic Image Label Refinement}, AUTHOR = {Nag Chowdhury, Sreyasi and Tandon, Niket and Ferhatosmanoglu, Hakan and Weikum, Gerhard}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1909.00741}, EPRINT = {1909.00741}, EPRINTTYPE = {arXiv}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, ABSTRACT = {The social media explosion has populated the Internet with a wealth of images. There are two existing paradigms for image retrieval: 1) content-based image retrieval (CBIR), which has traditionally used visual features for similarity search (e.g., SIFT features), and 2) tag-based image retrieval (TBIR), which has relied on user tagging (e.g., Flickr tags). CBIR now gains semantic expressiveness by advances in deep-learning-based detection of visual labels. TBIR benefits from query-and-click logs to automatically infer more informative labels. However, learning-based tagging still yields noisy labels and is restricted to concrete objects, missing out on generalizations and abstractions. Click-based tagging is limited to terms that appear in the textual context of an image or in queries that lead to a click. This paper addresses the above limitations by semantically refining and expanding the labels suggested by learning-based object detection. We consider the semantic coherence between the labels for different objects, leverage lexical and commonsense knowledge, and cast the label assignment into a constrained optimization problem solved by an integer linear program. Experiments show that our method, called VISIR, improves the quality of the state-of-the-art visual labeling tools like LSDA and YOLO.}, }
Endnote
%0 Report %A Nag Chowdhury, Sreyasi %A Tandon, Niket %A Ferhatosmanoglu, Hakan %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T VISIR: Visual and Semantic Image Label Refinement : %G eng %U http://hdl.handle.net/21.11116/0000-0005-83CE-F %U http://arxiv.org/abs/1909.00741 %D 2019 %X The social media explosion has populated the Internet with a wealth of images. There are two existing paradigms for image retrieval: 1) content-based image retrieval (CBIR), which has traditionally used visual features for similarity search (e.g., SIFT features), and 2) tag-based image retrieval (TBIR), which has relied on user tagging (e.g., Flickr tags). CBIR now gains semantic expressiveness by advances in deep-learning-based detection of visual labels. TBIR benefits from query-and-click logs to automatically infer more informative labels. However, learning-based tagging still yields noisy labels and is restricted to concrete objects, missing out on generalizations and abstractions. Click-based tagging is limited to terms that appear in the textual context of an image or in queries that lead to a click. This paper addresses the above limitations by semantically refining and expanding the labels suggested by learning-based object detection. We consider the semantic coherence between the labels for different objects, leverage lexical and commonsense knowledge, and cast the label assignment into a constrained optimization problem solved by an integer linear program. Experiments show that our method, called VISIR, improves the quality of the state-of-the-art visual labeling tools like LSDA and YOLO. %K Computer Science, Multimedia, cs.MM,Computer Science, Computer Vision and Pattern Recognition, cs.CV,Computer Science, Information Retrieval, cs.IR
[71]
S. Nag Chowdhury, S. Razniewski, and G. Weikum, “Story-oriented Image Selection and Placement,” 2019. [Online]. Available: http://arxiv.org/abs/1909.00692. (arXiv: 1909.00692)
Abstract
Multimodal contents have become commonplace on the Internet today, manifested as news articles, social media posts, and personal or business blog posts. Among the various kinds of media (images, videos, graphics, icons, audio) used in such multimodal stories, images are the most popular. The selection of images from a collection - either author's personal photo album, or web repositories - and their meticulous placement within a text, builds a succinct multimodal commentary for digital consumption. In this paper we present a system that automates the process of selecting relevant images for a story and placing them at contextual paragraphs within the story for a multimodal narration. We leverage automatic object recognition, user-provided tags, and commonsense knowledge, and use an unsupervised combinatorial optimization to solve the selection and placement problems seamlessly as a single unit.
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@online{Nag_arXiv1909.00692, TITLE = {Story-oriented Image Selection and Placement}, AUTHOR = {Nag Chowdhury, Sreyasi and Razniewski, Simon and Weikum, Gerhard}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1909.00692}, EPRINT = {1909.00692}, EPRINTTYPE = {arXiv}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Multimodal contents have become commonplace on the Internet today, manifested as news articles, social media posts, and personal or business blog posts. Among the various kinds of media (images, videos, graphics, icons, audio) used in such multimodal stories, images are the most popular. The selection of images from a collection -- either author's personal photo album, or web repositories -- and their meticulous placement within a text, builds a succinct multimodal commentary for digital consumption. In this paper we present a system that automates the process of selecting relevant images for a story and placing them at contextual paragraphs within the story for a multimodal narration. We leverage automatic object recognition, user-provided tags, and commonsense knowledge, and use an unsupervised combinatorial optimization to solve the selection and placement problems seamlessly as a single unit.}, }
Endnote
%0 Report %A Nag Chowdhury, Sreyasi %A Razniewski, Simon %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Story-oriented Image Selection and Placement : %G eng %U http://hdl.handle.net/21.11116/0000-0005-83C9-4 %U http://arxiv.org/abs/1909.00692 %D 2019 %X Multimodal contents have become commonplace on the Internet today, manifested as news articles, social media posts, and personal or business blog posts. Among the various kinds of media (images, videos, graphics, icons, audio) used in such multimodal stories, images are the most popular. The selection of images from a collection - either author's personal photo album, or web repositories - and their meticulous placement within a text, builds a succinct multimodal commentary for digital consumption. In this paper we present a system that automates the process of selecting relevant images for a story and placing them at contextual paragraphs within the story for a multimodal narration. We leverage automatic object recognition, user-provided tags, and commonsense knowledge, and use an unsupervised combinatorial optimization to solve the selection and placement problems seamlessly as a single unit. %K Computer Science, Computation and Language, cs.CL
[72]
S. Nag Chowdhury, N. Tandon, and G. Weikum, “Know2Look: Commonsense Knowledge for Visual Search,” 2019. [Online]. Available: http://arxiv.org/abs/1909.00749. (arXiv: 1909.00749)
Abstract
With the rise in popularity of social media, images accompanied by contextual text form a huge section of the web. However, search and retrieval of documents are still largely dependent on solely textual cues. Although visual cues have started to gain focus, the imperfection in object/scene detection do not lead to significantly improved results. We hypothesize that the use of background commonsense knowledge on query terms can significantly aid in retrieval of documents with associated images. To this end we deploy three different modalities - text, visual cues, and commonsense knowledge pertaining to the query - as a recipe for efficient search and retrieval.
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@online{Nag_arXiv1909.00749, TITLE = {{Know2Look}: Commonsense Knowledge for Visual Search}, AUTHOR = {Nag Chowdhury, Sreyasi and Tandon, Niket and Weikum, Gerhard}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1909.00749}, EPRINT = {1909.00749}, EPRINTTYPE = {arXiv}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, ABSTRACT = {With the rise in popularity of social media, images accompanied by contextual text form a huge section of the web. However, search and retrieval of documents are still largely dependent on solely textual cues. Although visual cues have started to gain focus, the imperfection in object/scene detection do not lead to significantly improved results. We hypothesize that the use of background commonsense knowledge on query terms can significantly aid in retrieval of documents with associated images. To this end we deploy three different modalities -- text, visual cues, and commonsense knowledge pertaining to the query -- as a recipe for efficient search and retrieval.}, }
Endnote
%0 Report %A Nag Chowdhury, Sreyasi %A Tandon, Niket %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Know2Look: Commonsense Knowledge for Visual Search : %G eng %U http://hdl.handle.net/21.11116/0000-0005-83D2-9 %U http://arxiv.org/abs/1909.00749 %D 2019 %X With the rise in popularity of social media, images accompanied by contextual text form a huge section of the web. However, search and retrieval of documents are still largely dependent on solely textual cues. Although visual cues have started to gain focus, the imperfection in object/scene detection do not lead to significantly improved results. We hypothesize that the use of background commonsense knowledge on query terms can significantly aid in retrieval of documents with associated images. To this end we deploy three different modalities - text, visual cues, and commonsense knowledge pertaining to the query - as a recipe for efficient search and retrieval. %K Computer Science, Information Retrieval, cs.IR
[73]
S. Paramonov, D. Stepanova, and P. Miettinen, “Hybrid ASP-based Approach to Pattern Mining,” Theory and Practice of Logic Programming, vol. 19, no. 4, 2019.
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@article{ParamonovTPLP, TITLE = {Hybrid {ASP}-based Approach to Pattern Mining}, AUTHOR = {Paramonov, Sergey and Stepanova, Daria and Miettinen, Pauli}, LANGUAGE = {eng}, ISSN = {1471-0684}, DOI = {10.1017/S1471068418000467}, PUBLISHER = {Cambridge University Press}, ADDRESS = {Cambridge}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, JOURNAL = {Theory and Practice of Logic Programming}, VOLUME = {19}, NUMBER = {4}, PAGES = {505--535}, }
Endnote
%0 Journal Article %A Paramonov, Sergey %A Stepanova, Daria %A Miettinen, Pauli %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Hybrid ASP-based Approach to Pattern Mining : %G eng %U http://hdl.handle.net/21.11116/0000-0003-0CC4-3 %R 10.1017/S1471068418000467 %7 2019 %D 2019 %J Theory and Practice of Logic Programming %O TPLP %V 19 %N 4 %& 505 %P 505 - 535 %I Cambridge University Press %C Cambridge %@ false
[74]
K. Popat, “Credibility Analysis of Textual Claimswith Explainable Evidence,” Universität des Saarlandes, Saarbrücken, 2019.
Abstract
Despite being a vast resource of valuable information, the Web has been polluted by the spread of false claims. Increasing hoaxes, fake news, and misleading information on the Web have given rise to many fact-checking websites that manually assess these doubtful claims. However, the rapid speed and large scale of misinformation spread have become the bottleneck for manual verification. This calls for credibility assessment tools that can automate this verification process. Prior works in this domain make strong assumptions about the structure of the claims and the communities where they are made. Most importantly, black-box techniques proposed in prior works lack the ability to explain why a certain statement is deemed credible or not. To address these limitations, this dissertation proposes a general framework for automated credibility assessment that does not make any assumption about the structure or origin of the claims. Specifically, we propose a feature-based model, which automatically retrieves relevant articles about the given claim and assesses its credibility by capturing the mutual interaction between the language style of the relevant articles, their stance towards the claim, and the trustworthiness of the underlying web sources. We further enhance our credibility assessment approach and propose a neural-network-based model. Unlike the feature-based model, this model does not rely on feature engineering and external lexicons. Both our models make their assessments interpretable by extracting explainable evidence from judiciously selected web sources. We utilize our models and develop a Web interface, CredEye, which enables users to automatically assess the credibility of a textual claim and dissect into the assessment by browsing through judiciously and automatically selected evidence snippets. In addition, we study the problem of stance classification and propose a neural-network-based model for predicting the stance of diverse user perspectives regarding the controversial claims. Given a controversial claim and a user comment, our stance classification model predicts whether the user comment is supporting or opposing the claim.
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@phdthesis{Popatphd2019, TITLE = {Credibility Analysis of Textual Claimswith Explainable Evidence}, AUTHOR = {Popat, Kashyap}, LANGUAGE = {eng}, DOI = {10.22028/D291-30005}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, ABSTRACT = {Despite being a vast resource of valuable information, the Web has been polluted by the spread of false claims. Increasing hoaxes, fake news, and misleading information on the Web have given rise to many fact-checking websites that manually assess these doubtful claims. However, the rapid speed and large scale of misinformation spread have become the bottleneck for manual verification. This calls for credibility assessment tools that can automate this verification process. Prior works in this domain make strong assumptions about the structure of the claims and the communities where they are made. Most importantly, black-box techniques proposed in prior works lack the ability to explain why a certain statement is deemed credible or not. To address these limitations, this dissertation proposes a general framework for automated credibility assessment that does not make any assumption about the structure or origin of the claims. Specifically, we propose a feature-based model, which automatically retrieves relevant articles about the given claim and assesses its credibility by capturing the mutual interaction between the language style of the relevant articles, their stance towards the claim, and the trustworthiness of the underlying web sources. We further enhance our credibility assessment approach and propose a neural-network-based model. Unlike the feature-based model, this model does not rely on feature engineering and external lexicons. Both our models make their assessments interpretable by extracting explainable evidence from judiciously selected web sources. We utilize our models and develop a Web interface, CredEye, which enables users to automatically assess the credibility of a textual claim and dissect into the assessment by browsing through judiciously and automatically selected evidence snippets. In addition, we study the problem of stance classification and propose a neural-network-based model for predicting the stance of diverse user perspectives regarding the controversial claims. Given a controversial claim and a user comment, our stance classification model predicts whether the user comment is supporting or opposing the claim.}, }
Endnote
%0 Thesis %A Popat, Kashyap %Y Weikum, Gerhard %A referee: Naumann, Felix %A referee: Yates, Andrew %+ Databases and Information Systems, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Credibility Analysis of Textual Claimswith Explainable Evidence : %G eng %U http://hdl.handle.net/21.11116/0000-0005-654D-4 %R 10.22028/D291-30005 %I Universität des Saarlandes %C Saarbrücken %D 2019 %P 134 p. %V phd %9 phd %X Despite being a vast resource of valuable information, the Web has been polluted by the spread of false claims. Increasing hoaxes, fake news, and misleading information on the Web have given rise to many fact-checking websites that manually assess these doubtful claims. However, the rapid speed and large scale of misinformation spread have become the bottleneck for manual verification. This calls for credibility assessment tools that can automate this verification process. Prior works in this domain make strong assumptions about the structure of the claims and the communities where they are made. Most importantly, black-box techniques proposed in prior works lack the ability to explain why a certain statement is deemed credible or not. To address these limitations, this dissertation proposes a general framework for automated credibility assessment that does not make any assumption about the structure or origin of the claims. Specifically, we propose a feature-based model, which automatically retrieves relevant articles about the given claim and assesses its credibility by capturing the mutual interaction between the language style of the relevant articles, their stance towards the claim, and the trustworthiness of the underlying web sources. We further enhance our credibility assessment approach and propose a neural-network-based model. Unlike the feature-based model, this model does not rely on feature engineering and external lexicons. Both our models make their assessments interpretable by extracting explainable evidence from judiciously selected web sources. We utilize our models and develop a Web interface, CredEye, which enables users to automatically assess the credibility of a textual claim and dissect into the assessment by browsing through judiciously and automatically selected evidence snippets. In addition, we study the problem of stance classification and propose a neural-network-based model for predicting the stance of diverse user perspectives regarding the controversial claims. Given a controversial claim and a user comment, our stance classification model predicts whether the user comment is supporting or opposing the claim. %U https://publikationen.sulb.uni-saarland.de/handle/20.500.11880/28481
[75]
K. Popat, S. Mukherjee, A. Yates, and G. Weikum, “STANCY: Stance Classification Based on Consistency Cues,” 2019. [Online]. Available: http://arxiv.org/abs/1910.06048. (arXiv: 1910.06048)
Abstract
Controversial claims are abundant in online media and discussion forums. A better understanding of such claims requires analyzing them from different perspectives. Stance classification is a necessary step for inferring these perspectives in terms of supporting or opposing the claim. In this work, we present a neural network model for stance classification leveraging BERT representations and augmenting them with a novel consistency constraint. Experiments on the Perspectrum dataset, consisting of claims and users' perspectives from various debate websites, demonstrate the effectiveness of our approach over state-of-the-art baselines.
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@online{Popat_arXiv1910.06048, TITLE = {{STANCY}: Stance Classification Based on Consistency Cues}, AUTHOR = {Popat, Kashyap and Mukherjee, Subhabrata and Yates, Andrew and Weikum, Gerhard}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1910.06048}, EPRINT = {1910.06048}, EPRINTTYPE = {arXiv}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Controversial claims are abundant in online media and discussion forums. A better understanding of such claims requires analyzing them from different perspectives. Stance classification is a necessary step for inferring these perspectives in terms of supporting or opposing the claim. In this work, we present a neural network model for stance classification leveraging BERT representations and augmenting them with a novel consistency constraint. Experiments on the Perspectrum dataset, consisting of claims and users' perspectives from various debate websites, demonstrate the effectiveness of our approach over state-of-the-art baselines.}, }
Endnote
%0 Report %A Popat, Kashyap %A Mukherjee, Subhabrata %A Yates, Andrew %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T STANCY: Stance Classification Based on Consistency Cues : %G eng %U http://hdl.handle.net/21.11116/0000-0005-83E2-7 %U http://arxiv.org/abs/1910.06048 %D 2019 %X Controversial claims are abundant in online media and discussion forums. A better understanding of such claims requires analyzing them from different perspectives. Stance classification is a necessary step for inferring these perspectives in terms of supporting or opposing the claim. In this work, we present a neural network model for stance classification leveraging BERT representations and augmenting them with a novel consistency constraint. Experiments on the Perspectrum dataset, consisting of claims and users' perspectives from various debate websites, demonstrate the effectiveness of our approach over state-of-the-art baselines. %K Computer Science, Computation and Language, cs.CL,Computer Science, Artificial Intelligence, cs.AI,Computer Science, Learning, cs.LG
[76]
K. Popat, S. Mukherjee, A. Yates, and G. Weikum, “STANCY: Stance Classification Based on Consistency Cues,” in 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP 2019), Hong Kong, China, 2019.
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@inproceedings{D19-1675, TITLE = {STANCY: {S}tance Classification Based on Consistency Cues}, AUTHOR = {Popat, Kashyap and Mukherjee, Subhabrata and Yates, Andrew and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-950737-90-1}, URL = {https://www.aclweb.org/anthology/D19-1675/}, DOI = {10.18653/v1/D19-1675}, PUBLISHER = {ACM}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, BOOKTITLE = {2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP 2019)}, EDITOR = {Inui, Kentaro and JIng, Jiang and Ng, Vincent and Wan, Xiaojun}, PAGES = {6412--6417}, ADDRESS = {Hong Kong, China}, }
Endnote
%0 Conference Proceedings %A Popat, Kashyap %A Mukherjee, Subhabrata %A Yates, Andrew %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T STANCY: Stance Classification Based on Consistency Cues : %G eng %U http://hdl.handle.net/21.11116/0000-0005-827A-F %U https://www.aclweb.org/anthology/D19-1675/ %R 10.18653/v1/D19-1675 %D 2019 %B Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing %Z date of event: 2019-11-03 - 2019-11-07 %C Hong Kong, China %B 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing %E Inui, Kentaro; JIng, Jiang; Ng, Vincent; Wan, Xiaojun %P 6412 - 6417 %I ACM %@ 978-1-950737-90-1
[77]
S. Razniewski, N. Jain, P. Mirza, and G. Weikum, “Coverage of Information Extraction from Sentences and Paragraphs,” in 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP 2019), Hong Kong, China, 2019.
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@inproceedings{D19-1000, TITLE = {Coverage of Information Extraction from Sentences and Paragraphs}, AUTHOR = {Razniewski, Simon and Jain, Nitisha and Mirza, Paramita and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-950737-90-1}, URL = {https://www.aclweb.org/anthology/D19-1000}, PUBLISHER = {ACM}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, BOOKTITLE = {2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP 2019)}, EDITOR = {Inui, Kentaro and JIng, Jiang and Ng, Vincent and Wan, Xiaojun}, PAGES = {5770--5775}, ADDRESS = {Hong Kong, China}, }
Endnote
%0 Conference Proceedings %A Razniewski, Simon %A Jain, Nitisha %A Mirza, Paramita %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Coverage of Information Extraction from Sentences and Paragraphs : %G eng %U http://hdl.handle.net/21.11116/0000-0005-8265-6 %U https://www.aclweb.org/anthology/D19-1000 %D 2019 %B Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing %Z date of event: 2019-11-03 - 2019-11-07 %C Hong Kong, China %B 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing %E Inui, Kentaro; JIng, Jiang; Ng, Vincent; Wan, Xiaojun %P 5770 - 5775 %I ACM %@ 978-1-950737-90-1
[78]
J. Romero, S. Razniewski, K. Pal, J. Z. Pan, A. Sakhadeo, and G. Weikum, “Commonsense Properties from Query Logs and Question Answering Forums,” in CIKM’19, 28th ACM International Conference on Information and Knowledge Management, Beijing China, 2019.
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@inproceedings{Romero_CIKM2019, TITLE = {Commonsense Properties from Query Logs and Question Answering Forums}, AUTHOR = {Romero, Julien and Razniewski, Simon and Pal, Koninika and Pan, Jeff Z. and Sakhadeo, Archit and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {9781450369763}, DOI = {10.1145/3357384.3357955}, PUBLISHER = {ACM}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, BOOKTITLE = {CIKM'19, 28th ACM International Conference on Information and Knowledge Management}, EDITOR = {Zhu, Wenwu and Tao, Dacheng}, PAGES = {1411--1420}, ADDRESS = {Beijing China}, }
Endnote
%0 Conference Proceedings %A Romero, Julien %A Razniewski, Simon %A Pal, Koninika %A Pan, Jeff Z. %A Sakhadeo, Archit %A Weikum, Gerhard %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Commonsense Properties from Query Logs and Question Answering Forums : %G eng %U http://hdl.handle.net/21.11116/0000-0005-8255-8 %R 10.1145/3357384.3357955 %D 2019 %B 28th ACM International Conference on Information and Knowledge Management %Z date of event: 2019-11-03 - 2019-11-07 %C Beijing China %B CIKM'19 %E Zhu, Wenwu; Tao, Dacheng %P 1411 - 1420 %I ACM %@ 9781450369763
[79]
J. Romero, S. Razniewski, K. Pal, J. Z. Pan, A. Sakhadeo, and G. Weikum, “Commonsense Properties from Query Logs and Question Answering Forums,” 2019. [Online]. Available: http://arxiv.org/abs/1905.10989. (arXiv: 1905.10989)
Abstract
Commonsense knowledge about object properties, human behavior and general concepts is crucial for robust AI applications. However, automatic acquisition of this knowledge is challenging because of sparseness and bias in online sources. This paper presents Quasimodo, a methodology and tool suite for distilling commonsense properties from non-standard web sources. We devise novel ways of tapping into search-engine query logs and QA forums, and combining the resulting candidate assertions with statistical cues from encyclopedias, books and image tags in a corroboration step. Unlike prior work on commonsense knowledge bases, Quasimodo focuses on salient properties that are typically associated with certain objects or concepts. Extensive evaluations, including extrinsic use-case studies, show that Quasimodo provides better coverage than state-of-the-art baselines with comparable quality.
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@online{Romero_arXiv1905.10989, TITLE = {Commonsense Properties from Query Logs and Question Answering Forums}, AUTHOR = {Romero, Julien and Razniewski, Simon and Pal, Koninika and Pan, Jeff Z. and Sakhadeo, Archit and Weikum, Gerhard}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1905.10989}, EPRINT = {1905.10989}, EPRINTTYPE = {arXiv}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Commonsense knowledge about object properties, human behavior and general concepts is crucial for robust AI applications. However, automatic acquisition of this knowledge is challenging because of sparseness and bias in online sources. This paper presents Quasimodo, a methodology and tool suite for distilling commonsense properties from non-standard web sources. We devise novel ways of tapping into search-engine query logs and QA forums, and combining the resulting candidate assertions with statistical cues from encyclopedias, books and image tags in a corroboration step. Unlike prior work on commonsense knowledge bases, Quasimodo focuses on salient properties that are typically associated with certain objects or concepts. Extensive evaluations, including extrinsic use-case studies, show that Quasimodo provides better coverage than state-of-the-art baselines with comparable quality.}, }
Endnote
%0 Report %A Romero, Julien %A Razniewski, Simon %A Pal, Koninika %A Pan, Jeff Z. %A Sakhadeo, Archit %A Weikum, Gerhard %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Commonsense Properties from Query Logs and Question Answering Forums : %G eng %U http://hdl.handle.net/21.11116/0000-0003-FEEE-4 %U http://arxiv.org/abs/1905.10989 %D 2019 %X Commonsense knowledge about object properties, human behavior and general concepts is crucial for robust AI applications. However, automatic acquisition of this knowledge is challenging because of sparseness and bias in online sources. This paper presents Quasimodo, a methodology and tool suite for distilling commonsense properties from non-standard web sources. We devise novel ways of tapping into search-engine query logs and QA forums, and combining the resulting candidate assertions with statistical cues from encyclopedias, books and image tags in a corroboration step. Unlike prior work on commonsense knowledge bases, Quasimodo focuses on salient properties that are typically associated with certain objects or concepts. Extensive evaluations, including extrinsic use-case studies, show that Quasimodo provides better coverage than state-of-the-art baselines with comparable quality. %K Computer Science, Computation and Language, cs.CL,Computer Science, Artificial Intelligence, cs.AI,Computer Science, Databases, cs.DB
[80]
F. M. Suchanek, J. Lajus, A. Boschin, and G. Weikum, “Knowledge Representation and Rule Mining in Entity-Centric Knowledge Bases,” in Reasoning Web -- Explainable Artificial Intelligence, Berlin: Springer, 2019.
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@incollection{Suchanek_LNCS11810, TITLE = {Knowledge Representation and Rule Mining in Entity-Centric Knowledge Bases}, AUTHOR = {Suchanek, Fabian M. and Lajus, Jonathan and Boschin, Armand and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-3-030-31422-4}, DOI = {10.1007/978-3-030-31423-1_4}, PUBLISHER = {Springer}, ADDRESS = {Berlin}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Reasoning Web -- Explainable Artificial Intelligence}, DEBUG = {author: Krötzsch, Markus; author: Stpanova, Daria}, PAGES = {110--152}, SERIES = {Lecture Notes in Computer Science}, VOLUME = {11810}, }
Endnote
%0 Book Section %A Suchanek, Fabian M. %A Lajus, Jonathan %A Boschin, Armand %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Knowledge Representation and Rule Mining in Entity-Centric Knowledge Bases : %G eng %U http://hdl.handle.net/21.11116/0000-0005-8298-C %R 10.1007/978-3-030-31423-1_4 %D 2019 %B Reasoning Web -- Explainable Artificial Intelligence %E Krötzsch, Markus; Stpanova, Daria %P 110 - 152 %I Springer %C Berlin %@ 978-3-030-31422-4 %S Lecture Notes in Computer Science %N 11810
[81]
H. Su, X. Shen, R. Zhang, F. Sun, P. Hu, C. Niu, and J. Zhou, “Improving Multi-turn Dialogue Modelling with Utterance ReWriter,” in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL), Florence, Italy, 2019.
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@inproceedings{Su_2019, TITLE = {Improving Multi-turn Dialogue Modelling with Utterance {ReWriter}}, AUTHOR = {Su, Hui and Shen, Xiaoyu and Zhang, Rongzhi and Sun, Fei and Hu, Pengwei and Niu, Cheng and Zhou, Jie}, LANGUAGE = {eng}, URL = {https://www.aclweb.org/anthology/P19-1003}, DOI = {10.18653/v1/P19-1003}, PUBLISHER = {Association for Computational Linguistics}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, BOOKTITLE = {Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL)}, EDITOR = {Korhonen, Anna and Traum, David and M{\`a}rquez, Llu{\'i}s}, PAGES = {22--31}, ADDRESS = {Florence, Italy}, }
Endnote
%0 Conference Proceedings %A Su, Hui %A Shen, Xiaoyu %A Zhang, Rongzhi %A Sun, Fei %A Hu, Pengwei %A Niu, Cheng %A Zhou, Jie %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations External Organizations External Organizations %T Improving Multi-turn Dialogue Modelling with Utterance ReWriter : %G eng %U http://hdl.handle.net/21.11116/0000-0005-6982-2 %U https://www.aclweb.org/anthology/P19-1003 %R 10.18653/v1/P19-1003 %D 2019 %B 57th Annual Meeting of the Association for Computational Linguistics %Z date of event: 2019-07-28 - 2019-08-02 %C Florence, Italy %B Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics %E Korhonen, Anna; Traum, David; Màrquez, Lluís %P 22 - 31 %I Association for Computational Linguistics
[82]
N. Tatti and P. Miettinen, “Boolean Matrix Factorization Meets Consecutive Ones Property,” in Proceedings of the 2019 SIAM International Conference on Data Mining (SDM 2019), Calgary, Canada, 2019.
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@inproceedings{Tatti_SDM2019, TITLE = {Boolean Matrix Factorization Meets Consecutive Ones Property}, AUTHOR = {Tatti, Nikolaj and Miettinen, Pauli}, LANGUAGE = {eng}, ISBN = {978-1-61197-567-3}, DOI = {10.1137/1.9781611975673.82}, PUBLISHER = {SIAM}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of the 2019 SIAM International Conference on Data Mining (SDM 2019)}, EDITOR = {Berger-Wolf, Tanya and Chawla, Nitesh}, PAGES = {729--737}, ADDRESS = {Calgary, Canada}, }
Endnote
%0 Conference Proceedings %A Tatti, Nikolaj %A Miettinen, Pauli %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Boolean Matrix Factorization Meets Consecutive Ones Property : %G eng %U http://hdl.handle.net/21.11116/0000-0004-030A-E %R 10.1137/1.9781611975673.82 %D 2019 %B SIAM International Conference on Data Mining %Z date of event: 2019-05-02 - 2019-05-04 %C Calgary, Canada %B Proceedings of the 2019 SIAM International Conference on Data Mining %E Berger-Wolf, Tanya; Chawla, Nitesh %P 729 - 737 %I SIAM %@ 978-1-61197-567-3
[83]
N. Tatti and P. Miettinen, “Boolean Matrix Factorization Meets Consecutive Ones Property,” 2019. [Online]. Available: http://arxiv.org/abs/1901.05797. (arXiv: 1901.05797)
Abstract
Boolean matrix factorization is a natural and a popular technique for summarizing binary matrices. In this paper, we study a problem of Boolean matrix factorization where we additionally require that the factor matrices have consecutive ones property (OBMF). A major application of this optimization problem comes from graph visualization: standard techniques for visualizing graphs are circular or linear layout, where nodes are ordered in circle or on a line. A common problem with visualizing graphs is clutter due to too many edges. The standard approach to deal with this is to bundle edges together and represent them as ribbon. We also show that we can use OBMF for edge bundling combined with circular or linear layout techniques. We demonstrate that not only this problem is NP-hard but we cannot have a polynomial-time algorithm that yields a multiplicative approximation guarantee (unless P = NP). On the positive side, we develop a greedy algorithm where at each step we look for the best 1-rank factorization. Since even obtaining 1-rank factorization is NP-hard, we propose an iterative algorithm where we fix one side and and find the other, reverse the roles, and repeat. We show that this step can be done in linear time using pq-trees. We also extend the problem to cyclic ones property and symmetric factorizations. Our experiments show that our algorithms find high-quality factorizations and scale well.
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@online{Tatti_arXiv1901.05797, TITLE = {Boolean Matrix Factorization Meets Consecutive Ones Property}, AUTHOR = {Tatti, Nikolaj and Miettinen, Pauli}, URL = {http://arxiv.org/abs/1901.05797}, EPRINT = {1901.05797}, EPRINTTYPE = {arXiv}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Boolean matrix factorization is a natural and a popular technique for summarizing binary matrices. In this paper, we study a problem of Boolean matrix factorization where we additionally require that the factor matrices have consecutive ones property (OBMF). A major application of this optimization problem comes from graph visualization: standard techniques for visualizing graphs are circular or linear layout, where nodes are ordered in circle or on a line. A common problem with visualizing graphs is clutter due to too many edges. The standard approach to deal with this is to bundle edges together and represent them as ribbon. We also show that we can use OBMF for edge bundling combined with circular or linear layout techniques. We demonstrate that not only this problem is NP-hard but we cannot have a polynomial-time algorithm that yields a multiplicative approximation guarantee (unless P = NP). On the positive side, we develop a greedy algorithm where at each step we look for the best 1-rank factorization. Since even obtaining 1-rank factorization is NP-hard, we propose an iterative algorithm where we fix one side and and find the other, reverse the roles, and repeat. We show that this step can be done in linear time using pq-trees. We also extend the problem to cyclic ones property and symmetric factorizations. Our experiments show that our algorithms find high-quality factorizations and scale well.}, }
Endnote
%0 Report %A Tatti, Nikolaj %A Miettinen, Pauli %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Boolean Matrix Factorization Meets Consecutive Ones Property : %U http://hdl.handle.net/21.11116/0000-0004-02F0-A %U http://arxiv.org/abs/1901.05797 %D 2019 %X Boolean matrix factorization is a natural and a popular technique for summarizing binary matrices. In this paper, we study a problem of Boolean matrix factorization where we additionally require that the factor matrices have consecutive ones property (OBMF). A major application of this optimization problem comes from graph visualization: standard techniques for visualizing graphs are circular or linear layout, where nodes are ordered in circle or on a line. A common problem with visualizing graphs is clutter due to too many edges. The standard approach to deal with this is to bundle edges together and represent them as ribbon. We also show that we can use OBMF for edge bundling combined with circular or linear layout techniques. We demonstrate that not only this problem is NP-hard but we cannot have a polynomial-time algorithm that yields a multiplicative approximation guarantee (unless P = NP). On the positive side, we develop a greedy algorithm where at each step we look for the best 1-rank factorization. Since even obtaining 1-rank factorization is NP-hard, we propose an iterative algorithm where we fix one side and and find the other, reverse the roles, and repeat. We show that this step can be done in linear time using pq-trees. We also extend the problem to cyclic ones property and symmetric factorizations. Our experiments show that our algorithms find high-quality factorizations and scale well. %K Computer Science, Data Structures and Algorithms, cs.DS,Computer Science, Discrete Mathematics, cs.DM,Computer Science, Learning, cs.LG
[84]
A. Tigunova, A. Yates, P. Mirza, and G. Weikum, “Listening between the Lines: Learning Personal Attributes from Conversations,” 2019. [Online]. Available: http://arxiv.org/abs/1904.10887. (arXiv: 1904.10887)
Abstract
Open-domain dialogue agents must be able to converse about many topics while incorporating knowledge about the user into the conversation. In this work we address the acquisition of such knowledge, for personalization in downstream Web applications, by extracting personal attributes from conversations. This problem is more challenging than the established task of information extraction from scientific publications or Wikipedia articles, because dialogues often give merely implicit cues about the speaker. We propose methods for inferring personal attributes, such as profession, age or family status, from conversations using deep learning. Specifically, we propose several Hidden Attribute Models, which are neural networks leveraging attention mechanisms and embeddings. Our methods are trained on a per-predicate basis to output rankings of object values for a given subject-predicate combination (e.g., ranking the doctor and nurse professions high when speakers talk about patients, emergency rooms, etc). Experiments with various conversational texts including Reddit discussions, movie scripts and a collection of crowdsourced personal dialogues demonstrate the viability of our methods and their superior performance compared to state-of-the-art baselines.
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@online{Tigunova_arXiv1904.10887, TITLE = {Listening between the Lines: Learning Personal Attributes from Conversations}, AUTHOR = {Tigunova, Anna and Yates, Andrew and Mirza, Paramita and Weikum, Gerhard}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1904.10887}, EPRINT = {1904.10887}, EPRINTTYPE = {arXiv}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Open-domain dialogue agents must be able to converse about many topics while incorporating knowledge about the user into the conversation. In this work we address the acquisition of such knowledge, for personalization in downstream Web applications, by extracting personal attributes from conversations. This problem is more challenging than the established task of information extraction from scientific publications or Wikipedia articles, because dialogues often give merely implicit cues about the speaker. We propose methods for inferring personal attributes, such as profession, age or family status, from conversations using deep learning. Specifically, we propose several Hidden Attribute Models, which are neural networks leveraging attention mechanisms and embeddings. Our methods are trained on a per-predicate basis to output rankings of object values for a given subject-predicate combination (e.g., ranking the doctor and nurse professions high when speakers talk about patients, emergency rooms, etc). Experiments with various conversational texts including Reddit discussions, movie scripts and a collection of crowdsourced personal dialogues demonstrate the viability of our methods and their superior performance compared to state-of-the-art baselines.}, }
Endnote
%0 Report %A Tigunova, Anna %A Yates, Andrew %A Mirza, Paramita %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Listening between the Lines: Learning Personal Attributes from Conversations : %G eng %U http://hdl.handle.net/21.11116/0000-0003-FE7F-2 %U http://arxiv.org/abs/1904.10887 %D 2019 %X Open-domain dialogue agents must be able to converse about many topics while incorporating knowledge about the user into the conversation. In this work we address the acquisition of such knowledge, for personalization in downstream Web applications, by extracting personal attributes from conversations. This problem is more challenging than the established task of information extraction from scientific publications or Wikipedia articles, because dialogues often give merely implicit cues about the speaker. We propose methods for inferring personal attributes, such as profession, age or family status, from conversations using deep learning. Specifically, we propose several Hidden Attribute Models, which are neural networks leveraging attention mechanisms and embeddings. Our methods are trained on a per-predicate basis to output rankings of object values for a given subject-predicate combination (e.g., ranking the doctor and nurse professions high when speakers talk about patients, emergency rooms, etc). Experiments with various conversational texts including Reddit discussions, movie scripts and a collection of crowdsourced personal dialogues demonstrate the viability of our methods and their superior performance compared to state-of-the-art baselines. %K Computer Science, Computation and Language, cs.CL
[85]
A. Tigunova, A. Yates, P. Mirza, and G. Weikum, “Listening between the Lines: Learning Personal Attributes from Conversations,” in Proceedings of The World Wide Web Conference (WWW 2019), San Francisco, CA, USA, 2019.
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@inproceedings{tigunova2019listening, TITLE = {Listening between the Lines: {L}earning Personal Attributes from Conversations}, AUTHOR = {Tigunova, Anna and Yates, Andrew and Mirza, Paramita and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-6674-8}, DOI = {10.1145/3308558.3313498}, PUBLISHER = {ACM}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of The World Wide Web Conference (WWW 2019)}, EDITOR = {McAuley, Julian}, PAGES = {1818--1828}, ADDRESS = {San Francisco, CA, USA}, }
Endnote
%0 Conference Proceedings %A Tigunova, Anna %A Yates, Andrew %A Mirza, Paramita %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Listening between the Lines: Learning Personal Attributes from Conversations : %G eng %U http://hdl.handle.net/21.11116/0000-0003-1460-A %R 10.1145/3308558.3313498 %D 2019 %B The Web Conference %Z date of event: 2019-05-13 - 2019-05-17 %C San Francisco, CA, USA %B Proceedings of The World Wide Web Conference %E McAuley, Julian %P 1818 - 1828 %I ACM %@ 978-1-4503-6674-8
[86]
B. D. Trisedya, G. Weikum, J. Qi, and R. Zhang, “Neural Relation Extraction for Knowledge Base Enrichment,” in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL), Florence, Italy, 2019.
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@inproceedings{Trisedya_ACL2019, TITLE = {Neural Relation Extraction for Knowledge Base Enrichment}, AUTHOR = {Trisedya, Bayu Distiawan and Weikum, Gerhard and Qi, Jianzhong and Zhang, Rui}, LANGUAGE = {eng}, URL = {https://www.aclweb.org/anthology/P19-1023}, DOI = {10.18653/v1/P19-1023}, PUBLISHER = {Association for Computational Linguistics}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, BOOKTITLE = {Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL)}, EDITOR = {Korhonen, Anna and Traum, David and M{\`a}rguez, Llu{\'i}s}, PAGES = {229--240}, ADDRESS = {Florence, Italy}, }
Endnote
%0 Conference Proceedings %A Trisedya, Bayu Distiawan %A Weikum, Gerhard %A Qi, Jianzhong %A Zhang, Rui %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T Neural Relation Extraction for Knowledge Base Enrichment : %G eng %U http://hdl.handle.net/21.11116/0000-0005-6B08-B %U https://www.aclweb.org/anthology/P19-1023 %R 10.18653/v1/P19-1023 %D 2019 %B 57th Annual Meeting of the Association for Computational Linguistics %Z date of event: 2019-07-28 - 2019-08-02 %C Florence, Italy %B Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics %E Korhonen, Anna; Traum, David; Màrguez, Lluís %P 229 - 240 %I Association for Computational Linguistics
[87]
M. Unterkalmsteiner and A. Yates, “Expert-sourcing Domain-specific Knowledge: The Case of Synonym Validation,” in Joint Proceedings of REFSQ-2019 Workshops, Doctoral Symposium, Live Studies Track, and Poster Track co-located with the 25th International Conference on Requirements Engineering: Foundation for Software Quality (REFSQ 2019) (NLP4RE 2019), Essen, Germany, 2019.
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@inproceedings{Unterkalmsteiner_NLP4RE2019, TITLE = {Expert-sourcing Domain-specific Knowledge: {The} Case of Synonym Validation}, AUTHOR = {Unterkalmsteiner, Michael and Yates, Andrew}, LANGUAGE = {eng}, ISSN = {1613-0073}, URL = {urn:nbn:de:0074-2376-8}, PUBLISHER = {CEUR-WS}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Joint Proceedings of REFSQ-2019 Workshops, Doctoral Symposium, Live Studies Track, and Poster Track co-located with the 25th International Conference on Requirements Engineering: Foundation for Software Quality (REFSQ 2019) (NLP4RE 2019)}, EDITOR = {Dalpiaz, Fabiano and Ferrari, Alessio and Franch, Xavier and Gregory, Sarah and Houdek, Frank and Palomares, Cristina}, EID = {8}, SERIES = {CEUR Workshop Proceedings}, VOLUME = {2376}, ADDRESS = {Essen, Germany}, }
Endnote
%0 Conference Proceedings %A Unterkalmsteiner, Michael %A Yates, Andrew %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Expert-sourcing Domain-specific Knowledge: The Case of Synonym Validation : %G eng %U http://hdl.handle.net/21.11116/0000-0004-02AE-6 %D 2019 %B 2nd Workshop on Natural Language Processing for Requirements Engineering and NLP Tool Showcase %Z date of event: 2019-03-18 - 2019-03-18 %C Essen, Germany %B Joint Proceedings of REFSQ-2019 Workshops, Doctoral Symposium, Live Studies Track, and Poster Track co-located with the 25th International Conference on Requirements Engineering: Foundation for Software Quality (REFSQ 2019) %E Dalpiaz, Fabiano; Ferrari, Alessio; Franch, Xavier; Gregory, Sarah; Houdek, Frank; Palomares, Cristina %Z sequence number: 8 %I CEUR-WS %B CEUR Workshop Proceedings %N 2376 %@ false %U http://ceur-ws.org/Vol-2376/NLP4RE19_paper08.pdf
[88]
M. van Leeuwen, P. Chau, J. Vreeken, D. Shahaf, and C. Faloutsos, “Addendum to the Special Issue on Interactive Data Exploration and Analytics (TKDD, Vol. 12, Iss. 1): Introduction by the Guest Editors,” ACM Transactions on Knowledge Discovery from Data, vol. 13, no. 1, 2019.
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@article{vanLeeuwen2019, TITLE = {Addendum to the Special Issue on Interactive Data Exploration and Analytics ({TKDD}, Vol. 12, Iss. 1): Introduction by the Guest Editors}, AUTHOR = {van Leeuwen, Matthijs and Chau, Polo and Vreeken, Jilles and Shahaf, Dafna and Faloutsos, Christos}, LANGUAGE = {eng}, ISSN = {1556-4681}, DOI = {10.1145/3298786}, PUBLISHER = {ACM}, ADDRESS = {New York, NY}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, JOURNAL = {ACM Transactions on Knowledge Discovery from Data}, VOLUME = {13}, NUMBER = {1}, EID = {13}, }
Endnote
%0 Journal Article %A van Leeuwen, Matthijs %A Chau, Polo %A Vreeken, Jilles %A Shahaf, Dafna %A Faloutsos, Christos %+ External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T Addendum to the Special Issue on Interactive Data Exploration and Analytics (TKDD, Vol. 12, Iss. 1): Introduction by the Guest Editors : %G eng %U http://hdl.handle.net/21.11116/0000-0003-FFD5-E %R 10.1145/3298786 %7 2019 %D 2019 %J ACM Transactions on Knowledge Discovery from Data %V 13 %N 1 %Z sequence number: 13 %I ACM %C New York, NY %@ false
[89]
L. Wang, Y. Wang, G. de Melo, and G. Weikum, “Understanding Archetypes of Fake News via Fine-grained Classification,” Social Network Analysis and Mining, vol. 9, no. 1, 2019.
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@article{Wang2019_Understanding, TITLE = {Understanding Archetypes of Fake News via Fine-grained Classification}, AUTHOR = {Wang, Liqiang and Wang, Yafang and de Melo, Gerard and Weikum, Gerhard}, LANGUAGE = {eng}, ISSN = {1869-5450}, DOI = {10.1007/s13278-019-0580-z}, PUBLISHER = {Springer}, ADDRESS = {Cham}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, JOURNAL = {Social Network Analysis and Mining}, VOLUME = {9}, NUMBER = {1}, EID = {37}, }
Endnote
%0 Journal Article %A Wang, Liqiang %A Wang, Yafang %A de Melo, Gerard %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Understanding Archetypes of Fake News via Fine-grained Classification : %G eng %U http://hdl.handle.net/21.11116/0000-0005-789A-7 %R 10.1007/s13278-019-0580-z %7 2019 %D 2019 %J Social Network Analysis and Mining %V 9 %N 1 %Z sequence number: 37 %I Springer %C Cham %@ false
[90]
G. Weikum, J. Hoffart, and F. Suchanek, “Knowledge Harvesting: Achievements and Challenges,” in Computing and Software Science, Berlin: Springer, 2019.
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@incollection{Weikum_KnowHarv2019, TITLE = {Knowledge Harvesting: Achievements and Challenges}, AUTHOR = {Weikum, Gerhard and Hoffart, Johannes and Suchanek, Fabian}, LANGUAGE = {eng}, ISBN = {978-3-319-91907-2}, DOI = {10.1007/978-3-319-91908-9_13}, PUBLISHER = {Springer}, ADDRESS = {Berlin}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, BOOKTITLE = {Computing and Software Science}, EDITOR = {Steffen, Bernhard and Woeginger, Gerhard}, PAGES = {217--235}, SERIES = {Lecture Notes in Computer Science}, VOLUME = {10000}, }
Endnote
%0 Book Section %A Weikum, Gerhard %A Hoffart, Johannes %A Suchanek, Fabian %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T Knowledge Harvesting: Achievements and Challenges : %G eng %U http://hdl.handle.net/21.11116/0000-0005-83B1-E %R 10.1007/978-3-319-91908-9_13 %D 2019 %B Computing and Software Science %E Steffen, Bernhard; Woeginger, Gerhard %P 217 - 235 %I Springer %C Berlin %@ 978-3-319-91907-2 %S Lecture Notes in Computer Science %N 10000
[91]
A. Wisesa, F. Darari, A. Krisnadhi, W. Nutt, and S. Razniewski, “Wikidata Completeness Profiling Using ProWD,” in K-CAP’19, 10th International Conference on Knowledge Capture, Marina del Rey, CA, USA, 2019.
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@inproceedings{Wisesa_K-CAP2019, TITLE = {Wikidata Completeness Profiling Using {ProWD}}, AUTHOR = {Wisesa, Avicenna and Darari, Fariz and Krisnadhi, Adila and Nutt, Werner and Razniewski, Simon}, LANGUAGE = {eng}, ISBN = {978-1-4503-7008-0}, DOI = {10.1145/3360901.3364425}, PUBLISHER = {ACM}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {K-CAP'19, 10th International Conference on Knowledge Capture}, EDITOR = {Kejriwal, Maynak and Szekely, Pedro}, PAGES = {123--130}, ADDRESS = {Marina del Rey, CA, USA}, }
Endnote
%0 Conference Proceedings %A Wisesa, Avicenna %A Darari, Fariz %A Krisnadhi, Adila %A Nutt, Werner %A Razniewski, Simon %+ External Organizations External Organizations External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Wikidata Completeness Profiling Using ProWD : %G eng %U http://hdl.handle.net/21.11116/0000-0005-849B-7 %R 10.1145/3360901.3364425 %D 2019 %B 10th International Conference on Knowledge Capture %Z date of event: 2019-11-19 - 2019-11-21 %C Marina del Rey, CA, USA %B K-CAP'19 %E Kejriwal, Maynak; Szekely, Pedro %P 123 - 130 %I ACM %@ 978-1-4503-7008-0
[92]
A. Yates and M. Unterkalmsteiner, “Replicating Relevance-Ranked Synonym Discovery in a New Language and Domain,” in Advances in Information Retrieval (ECIR 2019), Cologne, Germany, 2019.
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@inproceedings{Yates_ECIR2019, TITLE = {Replicating Relevance-Ranked Synonym Discovery in a New Language and Domain}, AUTHOR = {Yates, Andrew and Unterkalmsteiner, Michael}, LANGUAGE = {eng}, ISBN = {978-3-030-15711-1}, DOI = {10.1007/978-3-030-15712-8_28}, PUBLISHER = {Springer}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, BOOKTITLE = {Advances in Information Retrieval (ECIR 2019)}, EDITOR = {Azzopardi, Leif and Stein, Benno and Fuhr, Norbert and Mayr, Philipp and Hauff, Claudia and Hiemstra, Djoerd}, PAGES = {429--442}, SERIES = {Lecture Notes in Computer Science}, VOLUME = {11437}, ADDRESS = {Cologne, Germany}, }
Endnote
%0 Conference Proceedings %A Yates, Andrew %A Unterkalmsteiner, Michael %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Replicating Relevance-Ranked Synonym Discovery in a New Language and Domain : %G eng %U http://hdl.handle.net/21.11116/0000-0004-029B-B %R 10.1007/978-3-030-15712-8_28 %D 2019 %B 41st European Conference on IR Research %Z date of event: 2019-04-14 - 2019-04-18 %C Cologne, Germany %B Advances in Information Retrieval %E Azzopardi, Leif; Stein, Benno; Fuhr, Norbert; Mayr, Philipp; Hauff, Claudia; Hiemstra, Djoerd %P 429 - 442 %I Springer %@ 978-3-030-15711-1 %B Lecture Notes in Computer Science %N 11437
2018
[93]
A. Abujabal, R. Saha Roy, M. Yahya, and G. Weikum, “Never-Ending Learning for Open-Domain Question Answering over Knowledge Bases,” in Proceedings of the World Wide Web Conference (WWW 2018), Lyon, France, 2018.
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@inproceedings{AbujabalWWW_2018, TITLE = {Never-Ending Learning for Open-Domain Question Answering over Knowledge Bases}, AUTHOR = {Abujabal, Abdalghani and Saha Roy, Rishiraj and Yahya, Mohamed and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-5639-8}, DOI = {10.1145/3178876.3186004}, PUBLISHER = {ACM}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {Proceedings of the World Wide Web Conference (WWW 2018)}, EDITOR = {Champin, Pierre-Antoine and Gandon, Fabien and M{\'e}dini, Lionel and Lalmas, Mounia and Ipeirotis, Panagiotis G.}, PAGES = {1053--1062}, ADDRESS = {Lyon, France}, }
Endnote
%0 Conference Proceedings %A Abujabal, Abdalghani %A Saha Roy, Rishiraj %A Yahya, Mohamed %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Never-Ending Learning for Open-Domain Question Answering over Knowledge Bases : %G eng %U http://hdl.handle.net/21.11116/0000-0001-3C91-8 %R 10.1145/3178876.3186004 %D 2018 %B The Web Conference %Z date of event: 2018-04-23 - 2018-04-27 %C Lyon, France %B Proceedings of the World Wide Web Conference %E Champin, Pierre-Antoine; Gandon, Fabien; Médini, Lionel; Lalmas, Mounia; Ipeirotis, Panagiotis G. %P 1053 - 1062 %I ACM %@ 978-1-4503-5639-8
[94]
A. Abujabal, R. Saha Roy, M. Yahya, and G. Weikum, “ComQA: A Community-sourced Dataset for Complex Factoid Question Answering with Paraphrase Clusters,” 2018. [Online]. Available: http://arxiv.org/abs/1809.09528. (arXiv: 1809.09528)
Abstract
To bridge the gap between the capabilities of the state-of-the-art in factoid question answering (QA) and what real users ask, we need large datasets of real user questions that capture the various question phenomena users are interested in, and the diverse ways in which these questions are formulated. We introduce ComQA, a large dataset of real user questions that exhibit different challenging aspects such as temporal reasoning, compositionality, etc. ComQA questions come from the WikiAnswers community QA platform. Through a large crowdsourcing effort, we clean the question dataset, group questions into paraphrase clusters, and annotate clusters with their answers. ComQA contains 11,214 questions grouped into 4,834 paraphrase clusters. We detail the process of constructing ComQA, including the measures taken to ensure its high quality while making effective use of crowdsourcing. We also present an extensive analysis of the dataset and the results achieved by state-of-the-art systems on ComQA, demonstrating that our dataset can be a driver of future research on QA.
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@online{Abujabal_arXiv1809.09528, TITLE = {{ComQA}: {A} Community-sourced Dataset for Complex Factoid Question Answering with Paraphrase Clusters}, AUTHOR = {Abujabal, Abdalghani and Saha Roy, Rishiraj and Yahya, Mohamed and Weikum, Gerhard}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1809.09528}, EPRINT = {1809.09528}, EPRINTTYPE = {arXiv}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, ABSTRACT = {To bridge the gap between the capabilities of the state-of-the-art in factoid question answering (QA) and what real users ask, we need large datasets of real user questions that capture the various question phenomena users are interested in, and the diverse ways in which these questions are formulated. We introduce ComQA, a large dataset of real user questions that exhibit different challenging aspects such as temporal reasoning, compositionality, etc. ComQA questions come from the WikiAnswers community QA platform. Through a large crowdsourcing effort, we clean the question dataset, group questions into paraphrase clusters, and annotate clusters with their answers. ComQA contains 11,214 questions grouped into 4,834 paraphrase clusters. We detail the process of constructing ComQA, including the measures taken to ensure its high quality while making effective use of crowdsourcing. We also present an extensive analysis of the dataset and the results achieved by state-of-the-art systems on ComQA, demonstrating that our dataset can be a driver of future research on QA.}, }
Endnote
%0 Report %A Abujabal, Abdalghani %A Saha Roy, Rishiraj %A Yahya, Mohamed %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T ComQA: A Community-sourced Dataset for Complex Factoid Question Answering with Paraphrase Clusters : %G eng %U http://hdl.handle.net/21.11116/0000-0002-A0FE-B %U http://arxiv.org/abs/1809.09528 %D 2018 %X To bridge the gap between the capabilities of the state-of-the-art in factoid question answering (QA) and what real users ask, we need large datasets of real user questions that capture the various question phenomena users are interested in, and the diverse ways in which these questions are formulated. We introduce ComQA, a large dataset of real user questions that exhibit different challenging aspects such as temporal reasoning, compositionality, etc. ComQA questions come from the WikiAnswers community QA platform. Through a large crowdsourcing effort, we clean the question dataset, group questions into paraphrase clusters, and annotate clusters with their answers. ComQA contains 11,214 questions grouped into 4,834 paraphrase clusters. We detail the process of constructing ComQA, including the measures taken to ensure its high quality while making effective use of crowdsourcing. We also present an extensive analysis of the dataset and the results achieved by state-of-the-art systems on ComQA, demonstrating that our dataset can be a driver of future research on QA. %K Computer Science, Computation and Language, cs.CL
[95]
P. Agarwal, J. Strötgen, L. Del Corro, J. Hoffart, and G. Weikum, “diaNED: Time-Aware Named Entity Disambiguation for Diachronic Corpora,” in The 56th Annual Meeting of the Association for Computational Linguistics (ACL 2018), Melbourne, Australia, 2018.
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@inproceedings{AgrawalACL2018a, TITLE = {{diaNED}: {T}ime-Aware Named Entity Disambiguation for Diachronic Corpora}, AUTHOR = {Agarwal, Prabal and Str{\"o}tgen, Jannik and Del Corro, Luciano and Hoffart, Johannes and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-948087-34-6}, URL = {https://aclanthology.coli.uni-saarland.de/volumes/proceedings-of-the-56th-annual-meeting-of-the-association-for-computational-linguistics-volume-2-short-papers}, PUBLISHER = {ACL}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {The 56th Annual Meeting of the Association for Computational Linguistics (ACL 2018)}, EDITOR = {Gurevych, Iryna and Miyao, Yusuke}, PAGES = {686--693}, EID = {602}, ADDRESS = {Melbourne, Australia}, }
Endnote
%0 Conference Proceedings %A Agarwal, Prabal %A Strötgen, Jannik %A Del Corro, Luciano %A Hoffart, Johannes %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T diaNED: Time-Aware Named Entity Disambiguation for Diachronic Corpora : %G eng %U http://hdl.handle.net/21.11116/0000-0001-9055-C %D 2018 %B The 56th Annual Meeting of the Association for Computational Linguistics %Z date of event: 2018-07-15 - 2018-07-20 %C Melbourne, Australia %B The 56th Annual Meeting of the Association for Computational Linguistics %E Gurevych, Iryna; Miyao, Yusuke %P 686 - 693 %Z sequence number: 602 %I ACL %@ 978-1-948087-34-6 %U http://aclweb.org/anthology/P18-2109
[96]
M. Antenore, G. Leone, A. Panconesi, and E. Terolli, “Together We Buy, Alone I Quit: Some Experimental Studies of Online Persuaders,” in DTUC’18 Digital Tools & Uses Congres, Paris, France, 2018.
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@inproceedings{Antenore:2018:TWB:3240117.3240119, TITLE = {Together We Buy, Alone {I} Quit: {S}ome Experimental Studies of Online Persuaders}, AUTHOR = {Antenore, Marzia and Leone, Giovanna and Panconesi, Alessandro and Terolli, Erisa}, LANGUAGE = {eng}, ISBN = {978-1-4503-6451-5}, DOI = {10.1145/3240117.3240119}, PUBLISHER = {ACM}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {DTUC'18 Digital Tools \& Uses Congres}, EDITOR = {Reyes, E. and Szoniecky, S. and Mkadmi, A. and Kembellec, G. and Fournier-S'niehotta, R. and Siala-Kallel, F. and Ammi, M. and Labelle, S.}, EID = {2}, ADDRESS = {Paris, France}, }
Endnote
%0 Conference Proceedings %A Antenore, Marzia %A Leone, Giovanna %A Panconesi, Alessandro %A Terolli, Erisa %+ External Organizations External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Together We Buy, Alone I Quit: Some Experimental Studies of Online Persuaders : %G eng %U http://hdl.handle.net/21.11116/0000-0002-A89D-0 %R 10.1145/3240117.3240119 %D 2018 %B First International Digital Tools & Uses Congress %Z date of event: 2018-10-03 - 2018-10-05 %C Paris, France %B DTUC'18 Digital Tools & Uses Congres %E Reyes, E.; Szoniecky, S.; Mkadmi, A.; Kembellec, G.; Fournier-S'niehotta, R.; Siala-Kallel, F.; Ammi, M.; Labelle, S. %Z sequence number: 2 %I ACM %@ 978-1-4503-6451-5
[97]
O. Balalau, C. Castillo, and M. Sozio, “EviDense: A Graph-Based Method for Finding Unique High-Impact Events with Succinct Keyword-Based Descriptions,” in Proceedings of the Twelfth International AAAI Conference on Web and Social Media (ICWSM 2018), Stanford, CA, USA, 2018.
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@inproceedings{Balalau_ICWSM2018, TITLE = {{EviDense}: {A} Graph-Based Method for Finding Unique High-Impact Events with Succinct Keyword-Based Descriptions}, AUTHOR = {Balalau, Oana and Castillo, Carlos and Sozio, Mauro}, LANGUAGE = {eng}, ISBN = {978-1-57735-798-8}, PUBLISHER = {AAAI}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of the Twelfth International AAAI Conference on Web and Social Media (ICWSM 2018)}, PAGES = {560--563}, ADDRESS = {Stanford, CA, USA}, }
Endnote
%0 Conference Proceedings %A Balalau, Oana %A Castillo, Carlos %A Sozio, Mauro %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T EviDense: A Graph-Based Method for Finding Unique High-Impact Events with Succinct Keyword-Based Descriptions : %G eng %U http://hdl.handle.net/21.11116/0000-0002-9CE8-9 %D 2018 %B 12th International AAAI Conference on Web and Social Media %Z date of event: 2018-06-25 - 2018-06-28 %C Stanford, CA, USA %B Proceedings of the Twelfth International AAAI Conference on Web and Social Media %P 560 - 563 %I AAAI %@ 978-1-57735-798-8 %U https://aaai.org/ocs/index.php/ICWSM/ICWSM18/paper/view/17889
[98]
V. Balaraman, S. Razniewski, and W. Nutt, “Recoin: Relative Completeness in Wikidata,” in Companion of the World Wide Web Conference (WWW 2018), Lyon, France, 2018.
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@inproceedings{BalaramanWWW2017, TITLE = {Recoin: {R}elative Completeness in {W}ikidata}, AUTHOR = {Balaraman, Vevake and Razniewski, Simon and Nutt, Werner}, LANGUAGE = {eng}, ISBN = {978-1-4503-5640-4}, DOI = {10.1145/3184558.3191641}, PUBLISHER = {ACM}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {Companion of the World Wide Web Conference (WWW 2018)}, EDITOR = {Champin, Pierre-Antoine and Gandon, Fabien and M{\'e}dini, Lionel}, PAGES = {1787--1792}, ADDRESS = {Lyon, France}, }
Endnote
%0 Conference Proceedings %A Balaraman, Vevake %A Razniewski, Simon %A Nutt, Werner %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Recoin: Relative Completeness in Wikidata : %G eng %U http://hdl.handle.net/21.11116/0000-0001-414A-3 %R 10.1145/3184558.3191641 %D 2018 %B The Web Conference %Z date of event: 2018-04-23 - 2018-04-27 %C Lyon, France %B Companion of the World Wide Web Conference %E Champin, Pierre-Antoine; Gandon, Fabien; Médini, Lionel %P 1787 - 1792 %I ACM %@ 978-1-4503-5640-4
[99]
A. J. Biega, K. P. Gummadi, and G. Weikum, “Equity of Attention: Amortizing Individual Fairness in Rankings,” in SIGIR’18, 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, Ann Arbor, MI, USA, 2018.
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@inproceedings{BiegaSIGIR2018, TITLE = {Equity of Attention: {A}mortizing Individual Fairness in Rankings}, AUTHOR = {Biega, Asia J. and Gummadi, Krishna P. and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-5022-8}, DOI = {10.1145/3209978.3210063}, PUBLISHER = {ACM}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {SIGIR'18, 41st International ACM SIGIR Conference on Research and Development in Information Retrieval}, PAGES = {405--414}, ADDRESS = {Ann Arbor, MI, USA}, }
Endnote
%0 Conference Proceedings %A Biega, Asia J. %A Gummadi, Krishna P. %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Equity of Attention: Amortizing Individual Fairness in Rankings : %G eng %U http://hdl.handle.net/21.11116/0000-0002-0D8A-5 %R 10.1145/3209978.3210063 %D 2018 %B 41st International ACM SIGIR Conference on Research and Development in Information Retrieval %Z date of event: 2018-07-08 - 2018-07-12 %C Ann Arbor, MI, USA %B SIGIR'18 %P 405 - 414 %I ACM %@ 978-1-4503-5022-8
[100]
A. J. Biega, K. P. Gummadi, and G. Weikum, “Equity of Attention: Amortizing Individual Fairness in Rankings,” 2018. [Online]. Available: http://arxiv.org/abs/1805.01788. (arXiv: 1805.01788)
Abstract
Rankings of people and items are at the heart of selection-making, match-making, and recommender systems, ranging from employment sites to sharing economy platforms. As ranking positions influence the amount of attention the ranked subjects receive, biases in rankings can lead to unfair distribution of opportunities and resources, such as jobs or income. This paper proposes new measures and mechanisms to quantify and mitigate unfairness from a bias inherent to all rankings, namely, the position bias, which leads to disproportionately less attention being paid to low-ranked subjects. Our approach differs from recent fair ranking approaches in two important ways. First, existing works measure unfairness at the level of subject groups while our measures capture unfairness at the level of individual subjects, and as such subsume group unfairness. Second, as no single ranking can achieve individual attention fairness, we propose a novel mechanism that achieves amortized fairness, where attention accumulated across a series of rankings is proportional to accumulated relevance. We formulate the challenge of achieving amortized individual fairness subject to constraints on ranking quality as an online optimization problem and show that it can be solved as an integer linear program. Our experimental evaluation reveals that unfair attention distribution in rankings can be substantial, and demonstrates that our method can improve individual fairness while retaining high ranking quality.
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@online{Biega_arXiv1805.01788, TITLE = {Equity of Attention: Amortizing Individual Fairness in Rankings}, AUTHOR = {Biega, Asia J. and Gummadi, Krishna P. and Weikum, Gerhard}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1805.01788}, EPRINT = {1805.01788}, EPRINTTYPE = {arXiv}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Rankings of people and items are at the heart of selection-making, match-making, and recommender systems, ranging from employment sites to sharing economy platforms. As ranking positions influence the amount of attention the ranked subjects receive, biases in rankings can lead to unfair distribution of opportunities and resources, such as jobs or income. This paper proposes new measures and mechanisms to quantify and mitigate unfairness from a bias inherent to all rankings, namely, the position bias, which leads to disproportionately less attention being paid to low-ranked subjects. Our approach differs from recent fair ranking approaches in two important ways. First, existing works measure unfairness at the level of subject groups while our measures capture unfairness at the level of individual subjects, and as such subsume group unfairness. Second, as no single ranking can achieve individual attention fairness, we propose a novel mechanism that achieves amortized fairness, where attention accumulated across a series of rankings is proportional to accumulated relevance. We formulate the challenge of achieving amortized individual fairness subject to constraints on ranking quality as an online optimization problem and show that it can be solved as an integer linear program. Our experimental evaluation reveals that unfair attention distribution in rankings can be substantial, and demonstrates that our method can improve individual fairness while retaining high ranking quality.}, }
Endnote
%0 Report %A Biega, Asia J. %A Gummadi, Krishna P. %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Equity of Attention: Amortizing Individual Fairness in Rankings : %G eng %U http://hdl.handle.net/21.11116/0000-0002-1563-7 %U http://arxiv.org/abs/1805.01788 %D 2018 %X Rankings of people and items are at the heart of selection-making, match-making, and recommender systems, ranging from employment sites to sharing economy platforms. As ranking positions influence the amount of attention the ranked subjects receive, biases in rankings can lead to unfair distribution of opportunities and resources, such as jobs or income. This paper proposes new measures and mechanisms to quantify and mitigate unfairness from a bias inherent to all rankings, namely, the position bias, which leads to disproportionately less attention being paid to low-ranked subjects. Our approach differs from recent fair ranking approaches in two important ways. First, existing works measure unfairness at the level of subject groups while our measures capture unfairness at the level of individual subjects, and as such subsume group unfairness. Second, as no single ranking can achieve individual attention fairness, we propose a novel mechanism that achieves amortized fairness, where attention accumulated across a series of rankings is proportional to accumulated relevance. We formulate the challenge of achieving amortized individual fairness subject to constraints on ranking quality as an online optimization problem and show that it can be solved as an integer linear program. Our experimental evaluation reveals that unfair attention distribution in rankings can be substantial, and demonstrates that our method can improve individual fairness while retaining high ranking quality. %K Computer Science, Information Retrieval, cs.IR,Computer Science, Computers and Society, cs.CY
[101]
N. Boldyrev, M. Spaniol, and G. Weikum, “Multi-Cultural Interlinking of Web Taxonomies with ACROSS,” The Journal of Web Science, vol. 4, no. 2, 2018.
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@article{Boldyrev2018, TITLE = {Multi-Cultural Interlinking of Web Taxonomies with {ACROSS}}, AUTHOR = {Boldyrev, Natalia and Spaniol, Marc and Weikum, Gerhard}, LANGUAGE = {eng}, DOI = {10.1561/106.00000012}, PUBLISHER = {Now Publishers}, ADDRESS = {Boston}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, JOURNAL = {The Journal of Web Science}, VOLUME = {4}, NUMBER = {2}, PAGES = {20--33}, }
Endnote
%0 Journal Article %A Boldyrev, Natalia %A Spaniol, Marc %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Multi-Cultural Interlinking of Web Taxonomies with ACROSS : %G eng %U http://hdl.handle.net/21.11116/0000-0001-3CA4-3 %R 10.1561/106.00000012 %7 2018 %D 2018 %J The Journal of Web Science %O Web Science %V 4 %N 2 %& 20 %P 20 - 33 %I Now Publishers %C Boston
[102]
K. Budhathoki, M. Boley, and J. Vreeken, “Rule Discovery for Exploratory Causal Reasoning,” in Proceedings of the NeurIPS 2018 workshop on Causal Learning (NeurIPS CL 2018), Montréal, Canada, 2018.
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@inproceedings{budhathoki:18:dice, TITLE = {Rule Discovery for Exploratory Causal Reasoning}, AUTHOR = {Budhathoki, Kailash and Boley, Mario and Vreeken, Jilles}, LANGUAGE = {eng}, URL = {https://drive.google.com/file/d/1r-KTsok3VLIz-wUh0YtsK5YaEu53DcTf/view}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of the NeurIPS 2018 workshop on Causal Learning (NeurIPS CL 2018)}, EID = {14}, ADDRESS = {Montr{\'e}al, Canada}, }
Endnote
%0 Conference Proceedings %A Budhathoki, Kailash %A Boley, Mario %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Rule Discovery for Exploratory Causal Reasoning : %G eng %U http://hdl.handle.net/21.11116/0000-0002-9EBC-9 %U https://drive.google.com/file/d/1r-KTsok3VLIz-wUh0YtsK5YaEu53DcTf/view %D 2018 %B NeurIPS 2018 Workshop on Causal Learning %Z date of event: 2018-12-07 - 2018-12-07 %C Montréal, Canada %B Proceedings of the NeurIPS 2018 workshop on Causal Learning %Z sequence number: 14
[103]
K. Budhathoki and J. Vreeken, “Origo: Causal Inference by Compression,” Knowledge and Information Systems, vol. 56, no. 2, 2018.
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@article{Budhathoki2018, TITLE = {Origo: {C}ausal Inference by Compression}, AUTHOR = {Budhathoki, Kailash and Vreeken, Jilles}, LANGUAGE = {eng}, ISSN = {0219-1377}, DOI = {10.1007/s10115-017-1130-5}, PUBLISHER = {Springer}, ADDRESS = {New York, NY}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, JOURNAL = {Knowledge and Information Systems}, VOLUME = {56}, NUMBER = {2}, PAGES = {285--307}, }
Endnote
%0 Journal Article %A Budhathoki, Kailash %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Origo: Causal Inference by Compression : %G eng %U http://hdl.handle.net/21.11116/0000-0001-AF2B-B %R 10.1007/s10115-017-1130-5 %7 2018 %D 2018 %J Knowledge and Information Systems %V 56 %N 2 %& 285 %P 285 - 307 %I Springer %C New York, NY %@ false
[104]
K. Budhathoki and J. Vreeken, “Accurate Causal Inference on Discrete Data,” in IEEE International Conference on Data Mining (ICDM 2018), Singapore, Singapore, 2018.
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@inproceedings{budhathoki:18:acid, TITLE = {Accurate Causal Inference on Discrete Data}, AUTHOR = {Budhathoki, Kailash and Vreeken, Jilles}, LANGUAGE = {eng}, ISBN = {978-1-5386-9159-5}, DOI = {10.1109/ICDM.2018.00105}, PUBLISHER = {IEEE}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {IEEE International Conference on Data Mining (ICDM 2018)}, PAGES = {881--886}, ADDRESS = {Singapore, Singapore}, }
Endnote
%0 Conference Proceedings %A Budhathoki, Kailash %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Accurate Causal Inference on Discrete Data : %G eng %U http://hdl.handle.net/21.11116/0000-0002-9E96-3 %R 10.1109/ICDM.2018.00105 %D 2018 %B IEEE International Conference on Data Mining %Z date of event: 2018-11-17 - 2018-11-20 %C Singapore, Singapore %B IEEE International Conference on Data Mining %P 881 - 886 %I IEEE %@ 978-1-5386-9159-5
[105]
K. Budhathoki and J. Vreeken, “Causal Inference on Event Sequences,” in Proceedings of the 2018 SIAM International Conference on Data Mining (SDM 2018), San Diego, CA, USA, 2018.
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@inproceedings{budhathoki_SDM2018, TITLE = {Causal Inference on Event Sequences}, AUTHOR = {Budhathoki, Kailash and Vreeken, Jilles}, LANGUAGE = {eng}, ISBN = {978-1-61197-532-1}, DOI = {10.1137/1.9781611975321.7}, PUBLISHER = {SIAM}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {Proceedings of the 2018 SIAM International Conference on Data Mining (SDM 2018)}, EDITOR = {Ester, Martin and Pedreschi, Dino}, PAGES = {55--63}, ADDRESS = {San Diego, CA, USA}, }
Endnote
%0 Conference Proceedings %A Budhathoki, Kailash %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Causal Inference on Event Sequences : %G eng %U http://hdl.handle.net/21.11116/0000-0002-5F34-A %R 10.1137/1.9781611975321.7 %D 2018 %B SIAM International Conference on Data Mining %Z date of event: 2018-05-03 - 2018-05-05 %C San Diego, CA, USA %B Proceedings of the 2018 SIAM International Conference on Data Mining %E Ester, Martin; Pedreschi, Dino %P 55 - 63 %I SIAM %@ 978-1-61197-532-1
[106]
A. Cohan, B. Desmet, A. Yates, L. Soldaini, S. MacAvaney, and N. Goharian, “SMHD: A Large-Scale Resource for Exploring Online Language Usage for Multiple Mental Health Conditions,” 2018. [Online]. Available: http://arxiv.org/abs/1806.05258. (arXiv: 1806.05258)
Abstract
Mental health is a significant and growing public health concern. As language usage can be leveraged to obtain crucial insights into mental health conditions, there is a need for large-scale, labeled, mental health-related datasets of users who have been diagnosed with one or more of such conditions. In this paper, we investigate the creation of high-precision patterns to identify self-reported diagnoses of nine different mental health conditions, and obtain high-quality labeled data without the need for manual labelling. We introduce the SMHD (Self-reported Mental Health Diagnoses) dataset and make it available. SMHD is a novel large dataset of social media posts from users with one or multiple mental health conditions along with matched control users. We examine distinctions in users' language, as measured by linguistic and psychological variables. We further explore text classification methods to identify individuals with mental conditions through their language.
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@online{cohan_arXiv1806.05258, TITLE = {{SMHD}: A Large-Scale Resource for Exploring Online Language Usage for Multiple Mental Health Conditions}, AUTHOR = {Cohan, Arman and Desmet, Bart and Yates, Andrew and Soldaini, Luca and MacAvaney, Sean and Goharian, Nazli}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1806.05258}, EPRINT = {1806.05258}, EPRINTTYPE = {arXiv}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Mental health is a significant and growing public health concern. As language usage can be leveraged to obtain crucial insights into mental health conditions, there is a need for large-scale, labeled, mental health-related datasets of users who have been diagnosed with one or more of such conditions. In this paper, we investigate the creation of high-precision patterns to identify self-reported diagnoses of nine different mental health conditions, and obtain high-quality labeled data without the need for manual labelling. We introduce the SMHD (Self-reported Mental Health Diagnoses) dataset and make it available. SMHD is a novel large dataset of social media posts from users with one or multiple mental health conditions along with matched control users. We examine distinctions in users' language, as measured by linguistic and psychological variables. We further explore text classification methods to identify individuals with mental conditions through their language.}, }
Endnote
%0 Report %A Cohan, Arman %A Desmet, Bart %A Yates, Andrew %A Soldaini, Luca %A MacAvaney, Sean %A Goharian, Nazli %+ External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations %T SMHD: A Large-Scale Resource for Exploring Online Language Usage for Multiple Mental Health Conditions : %G eng %U http://hdl.handle.net/21.11116/0000-0002-5ED4-6 %U http://arxiv.org/abs/1806.05258 %D 2018 %X Mental health is a significant and growing public health concern. As language usage can be leveraged to obtain crucial insights into mental health conditions, there is a need for large-scale, labeled, mental health-related datasets of users who have been diagnosed with one or more of such conditions. In this paper, we investigate the creation of high-precision patterns to identify self-reported diagnoses of nine different mental health conditions, and obtain high-quality labeled data without the need for manual labelling. We introduce the SMHD (Self-reported Mental Health Diagnoses) dataset and make it available. SMHD is a novel large dataset of social media posts from users with one or multiple mental health conditions along with matched control users. We examine distinctions in users' language, as measured by linguistic and psychological variables. We further explore text classification methods to identify individuals with mental conditions through their language. %K Computer Science, Computation and Language, cs.CL
[107]
A. Cohan, B. Desmet, A. Yates, L. Soldaini, S. MacAvaney, and N. Goharian, “SMHD: a Large-Scale Resource for Exploring Online Language Usage for Multiple Mental Health Conditions,” in The 27th International Conference on Computational Linguistics (COLING 2018), Santa Fe, NM, USA, 2018.
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@inproceedings{Cohan_COLING2018, TITLE = {{SMHD}: {A} Large-Scale Resource for Exploring Online Language Usage for Multiple Mental Health Conditions}, AUTHOR = {Cohan, Arman and Desmet, Bart and Yates, Andrew and Soldaini, Luca and MacAvaney, Sean and Goharian, Nazli}, LANGUAGE = {eng}, ISBN = {978-1-948087-50-6}, URL = {http://aclweb.org/anthology/C18-1126}, PUBLISHER = {ACL}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {The 27th International Conference on Computational Linguistics (COLING 2018)}, EDITOR = {Bender, Emily M. and Derczynski, Leon and Isabelle, Pierre}, PAGES = {1485--1497}, ADDRESS = {Santa Fe, NM, USA}, }
Endnote
%0 Conference Proceedings %A Cohan, Arman %A Desmet, Bart %A Yates, Andrew %A Soldaini, Luca %A MacAvaney, Sean %A Goharian, Nazli %+ External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations %T SMHD: a Large-Scale Resource for Exploring Online Language Usage for Multiple Mental Health Conditions : %G eng %U http://hdl.handle.net/21.11116/0000-0002-5E91-1 %U http://aclweb.org/anthology/C18-1126 %D 2018 %B 27th International Conference on Computational Linguistics %Z date of event: 2018-08-20 - 2018-08-26 %C Santa Fe, NM, USA %B The 27th International Conference on Computational Linguistics %E Bender, Emily M.; Derczynski, Leon; Isabelle, Pierre %P 1485 - 1497 %I ACL %@ 978-1-948087-50-6
[108]
M. Danisch, O. Balalau, and M. Sozio, “Listing k-cliques in Sparse Real-World Graphs,” in Companion of the World Wide Web Conference (WWW 2018), Lyon, France, 2018.
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@inproceedings{Danisch_WWW2018, TITLE = {Listing k-cliques in Sparse Real-World Graphs}, AUTHOR = {Danisch, Maximilien and Balalau, Oana and Sozio, Mauro}, LANGUAGE = {eng}, ISBN = {978-1-4503-5640-4}, DOI = {10.1145/3178876.3186125}, PUBLISHER = {ACM}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {Companion of the World Wide Web Conference (WWW 2018)}, EDITOR = {Champin, Pierre-Antoine and Gandon, Fabien and M{\'e}dini, Lionel}, PAGES = {589--598}, ADDRESS = {Lyon, France}, }
Endnote
%0 Conference Proceedings %A Danisch, Maximilien %A Balalau, Oana %A Sozio, Mauro %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Listing k-cliques in Sparse Real-World Graphs : %G eng %U http://hdl.handle.net/21.11116/0000-0002-9CDE-5 %R 10.1145/3178876.3186125 %D 2018 %B The Web Conference %Z date of event: 2018-04-23 - 2018-04-27 %C Lyon, France %B Companion of the World Wide Web Conference %E Champin, Pierre-Antoine; Gandon, Fabien; Médini, Lionel %P 589 - 598 %I ACM %@ 978-1-4503-5640-4
[109]
F. Darari, W. Nutt, G. Pirrò, and S. Razniewski, “Completeness Management for RDF Data Sources,” ACM Transactions on the Web, vol. 12, no. 3, 2018.
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@article{Darari2018, TITLE = {Completeness Management for {RDF} Data Sources}, AUTHOR = {Darari, Fariz and Nutt, Werner and Pirr{\`o}, Giuseppe and Razniewski, Simon}, LANGUAGE = {eng}, DOI = {10.1145/3196248}, PUBLISHER = {ACM}, ADDRESS = {New York, NY}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, JOURNAL = {ACM Transactions on the Web}, VOLUME = {12}, NUMBER = {3}, EID = {18}, }
Endnote
%0 Journal Article %A Darari, Fariz %A Nutt, Werner %A Pirrò, Giuseppe %A Razniewski, Simon %+ External Organizations External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Completeness Management for RDF Data Sources : %G eng %U http://hdl.handle.net/21.11116/0000-0001-E17F-3 %R 10.1145/3196248 %7 2018 %D 2018 %J ACM Transactions on the Web %V 12 %N 3 %Z sequence number: 18 %I ACM %C New York, NY
[110]
F. Darari, W. Nutt, and S. Razniewski, “Comparing Index Structures for Completeness Reasoning,” in IWBIS 2018, International Workshop on Big Data and Information Security, Jakarta, Indonesia, 2018.
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@inproceedings{DarariIWBIS2018, TITLE = {Comparing Index Structures for Completeness Reasoning}, AUTHOR = {Darari, Fariz and Nutt, Werner and Razniewski, Simon}, LANGUAGE = {eng}, ISBN = {978-1-5386-5525-2}, DOI = {10.1109/IWBIS.2018.8471712}, PUBLISHER = {IEEE}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {IWBIS 2018, International Workshop on Big Data and Information Security}, PAGES = {49--56}, ADDRESS = {Jakarta, Indonesia}, }
Endnote
%0 Conference Proceedings %A Darari, Fariz %A Nutt, Werner %A Razniewski, Simon %+ External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Comparing Index Structures for Completeness Reasoning : %G eng %U http://hdl.handle.net/21.11116/0000-0001-E193-A %R 10.1109/IWBIS.2018.8471712 %D 2018 %B International Workshop on Big Data and Information Security %Z date of event: 2018-05-12 - 2018-05-13 %C Jakarta, Indonesia %B IWBIS 2018 %P 49 - 56 %I IEEE %@ 978-1-5386-5525-2
[111]
S. Degaetano-Ortlieb and J. Strötgen, “Diachronic Variation of Temporal Expressions in Scientific Writing through the Lens of Relative Entropy,” in Language Technologies for the Challenges of the Digital Age (GSCL 2017), Berlin, Germany, 2018.
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@inproceedings{DegaetanoortliebStroetgen2017, TITLE = {Diachronic Variation of Temporal Expressions in Scientific Writing through the Lens of Relative Entropy}, AUTHOR = {Degaetano-Ortlieb, Stefania and Str{\"o}tgen, Jannik}, LANGUAGE = {eng}, ISBN = {978-3-319-73705-8}, DOI = {10.1007/978-3-319-73706-5_22}, PUBLISHER = {Springer}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {Language Technologies for the Challenges of the Digital Age (GSCL 2017)}, EDITOR = {Rehm, Georg and Declerck, Thierry}, PAGES = {259--275}, SERIES = {Lecture Notes in Artificial Intelligence}, VOLUME = {10713}, ADDRESS = {Berlin, Germany}, }
Endnote
%0 Conference Proceedings %A Degaetano-Ortlieb, Stefania %A Strötgen, Jannik %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Diachronic Variation of Temporal Expressions in Scientific Writing through the Lens of Relative Entropy : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-A8E8-5 %R 10.1007/978-3-319-73706-5_22 %D 2018 %B Conference of the German Society for Computational Linguistics and Language Technology %Z date of event: 2017-09-13 - 2017-09-14 %C Berlin, Germany %B Language Technologies for the Challenges of the Digital Age %E Rehm, Georg; Declerck, Thierry %P 259 - 275 %I Springer %@ 978-3-319-73705-8 %B Lecture Notes in Artificial Intelligence %N 10713
[112]
P. Ernst, A. Siu, and G. Weikum, “HighLife: Higher-arity Fact Harvesting,” in Proceedings of the World Wide Web Conference (WWW 2018), Lyon, France, 2018.
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@inproceedings{ErnstlWWW_2018, TITLE = {{HighLife}: Higher-arity Fact Harvesting}, AUTHOR = {Ernst, Patrick and Siu, Amy and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-5639-8}, DOI = {10.1145/3178876.3186000}, PUBLISHER = {ACM}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {Proceedings of the World Wide Web Conference (WWW 2018)}, EDITOR = {Champin, Pierre-Antoine and Gandon, Fabien and M{\'e}dini, Lionel and Lalmas, Mounia and Ipeirotis, Panagiotis G.}, PAGES = {1013--1022}, ADDRESS = {Lyon, France}, }
Endnote
%0 Conference Proceedings %A Ernst, Patrick %A Siu, Amy %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T HighLife: Higher-arity Fact Harvesting : %G eng %U http://hdl.handle.net/21.11116/0000-0001-3C96-3 %R 10.1145/3178876.3186000 %D 2018 %B The Web Conference %Z date of event: 2018-04-23 - 2018-04-27 %C Lyon, France %B Proceedings of the World Wide Web Conference %E Champin, Pierre-Antoine; Gandon, Fabien; Médini, Lionel; Lalmas, Mounia; Ipeirotis, Panagiotis G. %P 1013 - 1022 %I ACM %@ 978-1-4503-5639-8
[113]
P. Ernst, “Biomedical Knowledge Base Construction from Text and its Applications in Knowledge-based Systems,” Universität des Saarlandes, Saarbrücken, 2018.
Abstract
While general-purpose Knowledge Bases (KBs) have gone a long way in compiling comprehensive knowledgee about people, events, places, etc., domain-specific KBs, such as on health, are equally important, but are less explored. Consequently, a comprehensive and expressive health KB that spans all aspects of biomedical knowledge is still missing. The main goal of this thesis is to develop principled methods for building such a KB and enabling knowledge-centric applications. We address several challenges and make the following contributions: - To construct a health KB, we devise a largely automated and scalable pattern-based knowledge extraction method covering a spectrum of different text genres and distilling a wide variety of facts from different biomedical areas. - To consider higher-arity relations, crucial for proper knowledge representation in advanced domain such as health, we generalize the fact-pattern duality paradigm of previous methods. A key novelty is the integration of facts with missing arguments by extending our framework to partial patterns and facts by reasoning over the composability of partial facts. - To demonstrate the benefits of a health KB, we devise systems for entity-aware search and analytics and for entity-relationship-oriented exploration. Extensive experiments and use-case studies demonstrate the viability of the proposed approaches.
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@phdthesis{Ernstphd2017, TITLE = {Biomedical Knowledge Base Construction from Text and its Applications in Knowledge-based Systems}, AUTHOR = {Ernst, Patrick}, LANGUAGE = {eng}, URL = {urn:nbn:de:bsz:291-scidok-ds-271051}, DOI = {10.22028/D291-27105}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, ABSTRACT = {While general-purpose Knowledge Bases (KBs) have gone a long way in compiling comprehensive knowledgee about people, events, places, etc., domain-specific KBs, such as on health, are equally important, but are less explored. Consequently, a comprehensive and expressive health KB that spans all aspects of biomedical knowledge is still missing. The main goal of this thesis is to develop principled methods for building such a KB and enabling knowledge-centric applications. We address several challenges and make the following contributions: -- To construct a health KB, we devise a largely automated and scalable pattern-based knowledge extraction method covering a spectrum of different text genres and distilling a wide variety of facts from different biomedical areas. -- To consider higher-arity relations, crucial for proper knowledge representation in advanced domain such as health, we generalize the fact-pattern duality paradigm of previous methods. A key novelty is the integration of facts with missing arguments by extending our framework to partial patterns and facts by reasoning over the composability of partial facts. -- To demonstrate the benefits of a health KB, we devise systems for entity-aware search and analytics and for entity-relationship-oriented exploration. Extensive experiments and use-case studies demonstrate the viability of the proposed approaches.}, }
Endnote
%0 Thesis %A Ernst, Patrick %Y Weikum, Gerhard %A referee: Verspoor, Karin %A referee: Berberich, Klaus %+ Databases and Information Systems, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Biomedical Knowledge Base Construction from Text and its Applications in Knowledge-based Systems : %G eng %U http://hdl.handle.net/21.11116/0000-0001-1864-4 %U urn:nbn:de:bsz:291-scidok-ds-271051 %R 10.22028/D291-27105 %I Universität des Saarlandes %C Saarbrücken %D 2018 %8 20.02.2018 %P 147 p. %V phd %9 phd %X While general-purpose Knowledge Bases (KBs) have gone a long way in compiling comprehensive knowledgee about people, events, places, etc., domain-specific KBs, such as on health, are equally important, but are less explored. Consequently, a comprehensive and expressive health KB that spans all aspects of biomedical knowledge is still missing. The main goal of this thesis is to develop principled methods for building such a KB and enabling knowledge-centric applications. We address several challenges and make the following contributions: - To construct a health KB, we devise a largely automated and scalable pattern-based knowledge extraction method covering a spectrum of different text genres and distilling a wide variety of facts from different biomedical areas. - To consider higher-arity relations, crucial for proper knowledge representation in advanced domain such as health, we generalize the fact-pattern duality paradigm of previous methods. A key novelty is the integration of facts with missing arguments by extending our framework to partial patterns and facts by reasoning over the composability of partial facts. - To demonstrate the benefits of a health KB, we devise systems for entity-aware search and analytics and for entity-relationship-oriented exploration. Extensive experiments and use-case studies demonstrate the viability of the proposed approaches. %U https://publikationen.sulb.uni-saarland.de/handle/20.500.11880/26987
[114]
A. K. Fischer, J. Vreeken, and D. Klakov, “Beyond Pairwise Similarity: Quantifying and Characterizing Linguistic Similarity between Groups of Languages by MDL,” Computación y Sistemas, vol. 21, no. 4, 2018.
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@article{Fischer2018, TITLE = {Beyond Pairwise Similarity: Quantifying and Characterizing Linguistic Similarity between Groups of Languages by {MDL}}, AUTHOR = {Fischer, Andrea K. and Vreeken, Jilles and Klakov, Dietrich}, LANGUAGE = {eng}, DOI = {10.13053/CyS-21-4-2865}, PUBLISHER = {Instituto Polit{\'e}cnico Nacional}, ADDRESS = {M{\'e}xico}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, JOURNAL = {Computaci{\'o}n y Sistemas}, VOLUME = {21}, NUMBER = {4}, PAGES = {829--839}, }
Endnote
%0 Journal Article %A Fischer, Andrea K. %A Vreeken, Jilles %A Klakov, Dietrich %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Beyond Pairwise Similarity: Quantifying and Characterizing Linguistic Similarity between Groups of Languages by MDL : %G eng %U http://hdl.handle.net/21.11116/0000-0001-4156-5 %R 10.13053/CyS-21-4-2865 %7 2018 %D 2018 %J Computación y Sistemas %V 21 %N 4 %& 829 %P 829 - 839 %I Instituto Politécnico Nacional %C México %U http://www.redalyc.org/articulo.oa?id=61553900023
[115]
E. Galbrun and P. Miettinen, “Mining Redescriptions with Siren,” ACM Transactions on Knowledge Discovery from Data, vol. 12, no. 1, 2018.
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@article{galbrun17mining, TITLE = {Mining Redescriptions with {Siren}}, AUTHOR = {Galbrun, Esther and Miettinen, Pauli}, LANGUAGE = {eng}, DOI = {10.1145/3007212}, PUBLISHER = {ACM}, ADDRESS = {New York, NY}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, JOURNAL = {ACM Transactions on Knowledge Discovery from Data}, VOLUME = {12}, NUMBER = {1}, EID = {6}, }
Endnote
%0 Journal Article %A Galbrun, Esther %A Miettinen, Pauli %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Mining Redescriptions with Siren : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-227B-F %R 10.1145/3007212 %7 2018 %D 2018 %J ACM Transactions on Knowledge Discovery from Data %V 12 %N 1 %Z sequence number: 6 %I ACM %C New York, NY
[116]
E. Gius, N. Reiter, J. Strötgen, and M. Willand, “SANTA: Systematische Analyse Narrativer Texte durch Annotation,” in DHd 2018, 5. Tagung des Verbands Digital Humanities im deutschsprachigen Raum e.V., Köln, Germany, 2018.
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@inproceedings{GiusDHd2018, TITLE = {{{SANTA}: {Systematische Analyse Narrativer Texte durch Annotation}}}, AUTHOR = {Gius, Evelyn and Reiter, Nils and Str{\"o}tgen, Jannik and Willand, Marcus}, LANGUAGE = {deu}, ISBN = {978-3-946275-02-2}, URL = {http://dhd2018.uni-koeln.de/}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {DHd 2018, 5. Tagung des Verbands Digital Humanities im deutschsprachigen Raum e.V.}, PAGES = {302--305}, ADDRESS = {K{\"o}ln, Germany}, }
Endnote
%0 Conference Proceedings %A Gius, Evelyn %A Reiter, Nils %A Strötgen, Jannik %A Willand, Marcus %+ External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T SANTA: Systematische Analyse Narrativer Texte durch Annotation : %G deu %U http://hdl.handle.net/11858/00-001M-0000-002E-73EC-4 %D 2018 %B 5. Tagung des Verbands Digital Humanities im deutschsprachigen Raum e.V. %Z date of event: 2018-02-26 - 2018-03-02 %C Köln, Germany %B DHd 2018 %P 302 - 305 %@ 978-3-946275-02-2
[117]
D. Gupta and K. Berberich, “GYANI: An Indexing Infrastructure for Knowledge-Centric Tasks,” in CIKM’18, 27th ACM International Conference on Information and Knowledge Management, Torino, Italy, 2018.
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@inproceedings{Gupta_CIKM2018, TITLE = {{GYANI}: {A}n Indexing Infrastructure for Knowledge-Centric Tasks}, AUTHOR = {Gupta, Dhruv and Berberich, Klaus}, LANGUAGE = {eng}, ISBN = {978-1-4503-6014-2}, DOI = {10.1145/3269206.3271745}, PUBLISHER = {ACM}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {CIKM'18, 27th ACM International Conference on Information and Knowledge Management}, EDITOR = {Cuzzocrea, Alfredo and Allan, James and Paton, Norman and Srivastava, Divesh and Agrawal, Rakesh and Broder, Andrei and Zaki, Mohamed and Candan, Selcuk and Labrinidis, Alexandros and Schuster, Assaf and Wang, Haixun}, PAGES = {487--496}, ADDRESS = {Torino, Italy}, }
Endnote
%0 Conference Proceedings %A Gupta, Dhruv %A Berberich, Klaus %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T GYANI: An Indexing Infrastructure for Knowledge-Centric Tasks : %G eng %U http://hdl.handle.net/21.11116/0000-0002-A8B7-2 %R 10.1145/3269206.3271745 %D 2018 %B 27th ACM International Conference on Information and Knowledge Management %Z date of event: 2018-10-22 - 2018-10-26 %C Torino, Italy %B CIKM'18 %E Cuzzocrea, Alfredo; Allan, James; Paton, Norman; Srivastava, Divesh; Agrawal, Rakesh; Broder, Andrei; Zaki, Mohamed; Candan, Selcuk; Labrinidis, Alexandros; Schuster, Assaf; Wang, Haixun %P 487 - 496 %I ACM %@ 978-1-4503-6014-2
[118]
D. Gupta and K. Berberich, “Identifying Time Intervals for Knowledge Graph Facts,” in Companion of the World Wide Web Conference (WWW 2018), Lyon, France, 2018.
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@inproceedings{GuptaWWW2017, TITLE = {Identifying Time Intervals for Knowledge Graph Facts}, AUTHOR = {Gupta, Dhruv and Berberich, Klaus}, LANGUAGE = {eng}, ISBN = {978-1-4503-5640-4}, DOI = {10.1145/3184558.3186917}, PUBLISHER = {ACM}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {Companion of the World Wide Web Conference (WWW 2018)}, EDITOR = {Champin, Pierre-Antoine and Gandon, Fabien and M{\'e}dini, Lionel}, PAGES = {37--38}, ADDRESS = {Lyon, France}, }
Endnote
%0 Conference Proceedings %A Gupta, Dhruv %A Berberich, Klaus %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Identifying Time Intervals for Knowledge Graph Facts : %G eng %U http://hdl.handle.net/21.11116/0000-0001-411F-4 %R 10.1145/3184558.3186917 %D 2018 %B The Web Conference %Z date of event: 2018-04-23 - 2018-04-27 %C Lyon, France %B Companion of the World Wide Web Conference %E Champin, Pierre-Antoine; Gandon, Fabien; Médini, Lionel %P 37 - 38 %I ACM %@ 978-1-4503-5640-4
[119]
D. Gupta, K. Berberich, J. Strötgen, and D. Zeinalipour-Yazti, “Generating Semantic Aspects for Queries,” in JCDL’18, Joint Conference on Digital Libraries, Fort Worth, TX, USA, 2018.
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@inproceedings{GuptaJCDL2018, TITLE = {Generating Semantic Aspects for Queries}, AUTHOR = {Gupta, Dhruv and Berberich, Klaus and Str{\"o}tgen, Jannik and Zeinalipour-Yazti, Demetrios}, LANGUAGE = {eng}, ISBN = {978-1-4503-5178-2}, DOI = {10.1145/3197026.3203900}, PUBLISHER = {ACM}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {JCDL'18, Joint Conference on Digital Libraries}, PAGES = {335--336}, ADDRESS = {Fort Worth, TX, USA}, }
Endnote
%0 Conference Proceedings %A Gupta, Dhruv %A Berberich, Klaus %A Strötgen, Jannik %A Zeinalipour-Yazti, Demetrios %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Generating Semantic Aspects for Queries : %G eng %U http://hdl.handle.net/21.11116/0000-0001-904D-6 %R 10.1145/3197026.3203900 %D 2018 %B Joint Conference on Digital Libraries %Z date of event: 2018-06-03 - 2018-06-07 %C Fort Worth, TX, USA %B JCDL'18 %P 335 - 336 %I ACM %@ 978-1-4503-5178-2
[120]
G. Haratinezhad Torbati, “Joint Disambiguation of Named Entities and Concepts,” Universität des Saarlandes, Saarbrücken, 2018.
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@mastersthesis{torbati2018concept, TITLE = {Joint Disambiguation of Named Entities and Concepts}, AUTHOR = {Haratinezhad Torbati, Ghazaleh}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, }
Endnote
%0 Thesis %A Haratinezhad Torbati, Ghazaleh %Y Del Corro, Luciano %A referee: Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Joint Disambiguation of Named Entities and Concepts : %G eng %U http://hdl.handle.net/21.11116/0000-0003-38D0-3 %I Universität des Saarlandes %C Saarbrücken %D 2018 %P XIII, 70 p. %V master %9 master
[121]
A. Horňáková, M. List, J. Vreeken, and M. H. Schulz, “JAMI: Fast Computation of Conditional Mutual Information for ceRNA Network Analysis,” Bioinformatics, vol. 34, no. 17, 2018.
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@article{Hornakova_Bioinformatics2018, TITLE = {{JAMI}: {F}ast Computation of Conditional Mutual Information for {ceRNA} Network Analysis}, AUTHOR = {Hor{\v n}{\'a}kov{\'a}, Andrea and List, Markus and Vreeken, Jilles and Schulz, Marcel H.}, LANGUAGE = {eng}, ISSN = {1367-4803}, DOI = {10.1093/bioinformatics/bty221}, PUBLISHER = {Oxford University Press}, ADDRESS = {Oxford}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, JOURNAL = {Bioinformatics}, VOLUME = {34}, NUMBER = {17}, PAGES = {3050--3051}, }
Endnote
%0 Journal Article %A Horňáková, Andrea %A List, Markus %A Vreeken, Jilles %A Schulz, Marcel H. %+ Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society %T JAMI: Fast Computation of Conditional Mutual Information for ceRNA Network Analysis : %G eng %U http://hdl.handle.net/21.11116/0000-0002-573A-C %R 10.1093/bioinformatics/bty221 %7 2018 %D 2018 %J Bioinformatics %V 34 %N 17 %& 3050 %P 3050 - 3051 %I Oxford University Press %C Oxford %@ false
[122]
V. T. Ho, “An Embedding-based Approach to Rule Learning from Knowledge Graphs,” Universität des Saarlandes, Saarbrücken, 2018.
Abstract
Knowledge Graphs (KGs) play an important role in various information systems and have application in many fields such as Semantic Web Search, Question Answering and Information Retrieval. KGs present information in the form of entities and relationships between them. Modern KGs could contain up to millions of entities and billions of facts, and they are usually built using automatic construction methods. As a result, despite the huge size of KGs, a large number of facts between their entities are still missing. That is the reason why we see the importance of the task of Knowledge Graph Completion (a.k.a. Link Prediction), which concerns the prediction of those missing facts. Rules over a Knowledge Graph capture interpretable patterns in data and various methods for rule learning have been proposed. Since KGs are inherently incomplete, rules can be used to deduce missing facts. Statistical measures for learned rules such as confidence reflect rule quality well when the KG is reasonably complete; however, these measures might be misleading otherwise. So, it is difficult to learn high-quality rules from the KG alone, and scalability dictates that only a small set of candidate rules is generated. Therefore, the ranking and pruning of candidate rules are major problems. To address this issue, we propose a rule learning method that utilizes probabilistic representations of missing facts. In particular, we iteratively extend rules induced from a KG by relying on feedback from a precomputed embedding model over the KG and optionally external information sources including text corpora. The contributions of this thesis are as follows: • We introduce a framework for rule learning guided by external sources. • We propose a concrete instantiation of our framework to show how to learn high- quality rules by utilizing feedback from a pretrained embedding model. • We conducted experiments on real-world KGs that demonstrate the effectiveness of our novel approach with respect to both the quality of the learned rules and fact predictions that they produce.
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@mastersthesis{HoMaster2018, TITLE = {An Embedding-based Approach to Rule Learning from Knowledge Graphs}, AUTHOR = {Ho, Vinh Thinh}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, ABSTRACT = {Knowledge Graphs (KGs) play an important role in various information systems and have application in many {fi}elds such as Semantic Web Search, Question Answering and Information Retrieval. KGs present information in the form of entities and relationships between them. Modern KGs could contain up to millions of entities and billions of facts, and they are usually built using automatic construction methods. As a result, despite the huge size of KGs, a large number of facts between their entities are still missing. That is the reason why we see the importance of the task of Knowledge Graph Completion (a.k.a. Link Prediction), which concerns the prediction of those missing facts. Rules over a Knowledge Graph capture interpretable patterns in data and various methods for rule learning have been proposed. Since KGs are inherently incomplete, rules can be used to deduce missing facts. Statistical measures for learned rules such as con{fi}dence re{fl}ect rule quality well when the KG is reasonably complete; however, these measures might be misleading otherwise. So, it is difficult to learn high-quality rules from the KG alone, and scalability dictates that only a small set of candidate rules is generated. Therefore, the ranking and pruning of candidate rules are major problems. To address this issue, we propose a rule learning method that utilizes probabilistic representations of missing facts. In particular, we iteratively extend rules induced from a KG by relying on feedback from a precomputed embedding model over the KG and optionally external information sources including text corpora. The contributions of this thesis are as follows: \mbox{$\bullet$} We introduce a framework for rule learning guided by external sources. \mbox{$\bullet$} We propose a concrete instantiation of our framework to show how to learn high- quality rules by utilizing feedback from a pretrained embedding model. \mbox{$\bullet$} We conducted experiments on real-world KGs that demonstrate the effectiveness of our novel approach with respect to both the quality of the learned rules and fact predictions that they produce.}, }
Endnote
%0 Thesis %A Ho, Vinh Thinh %A referee: Weikum, Gerhard %Y Stepanova, Daria %+ Databases and Information Systems, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T An Embedding-based Approach to Rule Learning from Knowledge Graphs : %G eng %U http://hdl.handle.net/21.11116/0000-0001-DE06-F %I Universität des Saarlandes %C Saarbrücken %D 2018 %P 60 %V master %9 master %X Knowledge Graphs (KGs) play an important role in various information systems and have application in many fields such as Semantic Web Search, Question Answering and Information Retrieval. KGs present information in the form of entities and relationships between them. Modern KGs could contain up to millions of entities and billions of facts, and they are usually built using automatic construction methods. As a result, despite the huge size of KGs, a large number of facts between their entities are still missing. That is the reason why we see the importance of the task of Knowledge Graph Completion (a.k.a. Link Prediction), which concerns the prediction of those missing facts. Rules over a Knowledge Graph capture interpretable patterns in data and various methods for rule learning have been proposed. Since KGs are inherently incomplete, rules can be used to deduce missing facts. Statistical measures for learned rules such as confidence reflect rule quality well when the KG is reasonably complete; however, these measures might be misleading otherwise. So, it is difficult to learn high-quality rules from the KG alone, and scalability dictates that only a small set of candidate rules is generated. Therefore, the ranking and pruning of candidate rules are major problems. To address this issue, we propose a rule learning method that utilizes probabilistic representations of missing facts. In particular, we iteratively extend rules induced from a KG by relying on feedback from a precomputed embedding model over the KG and optionally external information sources including text corpora. The contributions of this thesis are as follows: • We introduce a framework for rule learning guided by external sources. • We propose a concrete instantiation of our framework to show how to learn high- quality rules by utilizing feedback from a pretrained embedding model. • We conducted experiments on real-world KGs that demonstrate the effectiveness of our novel approach with respect to both the quality of the learned rules and fact predictions that they produce.
[123]
V. T. Ho, D. Stepanova, M. H. Gad-Elrab, E. Kharlamov, and G. Weikum, “Rule Learning from Knowledge Graphs Guided by Embedding Models,” in The Semantic Web -- ISWC 2018, Monterey, CA, USA, 2018.
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@inproceedings{StepanovaISWC2018, TITLE = {Rule Learning from Knowledge Graphs Guided by Embedding Models}, AUTHOR = {Ho, Vinh Thinh and Stepanova, Daria and Gad-Elrab, Mohamed Hassan and Kharlamov, Evgeny and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-3-030-00670-9}, DOI = {10.1007/978-3-030-00671-6_5}, PUBLISHER = {Springer}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {The Semantic Web -- ISWC 2018}, EDITOR = {Vrande{\v c}i{\'c}, Denny and Bontcheva, Kalina and Su{\'a}rez-Figueroa, Mari Carmen and Presutti, Valentina and Celino, Irene and Sabou, Marta and Kaffee, Lucie-Aim{\'e}e and Simperl, Elena}, PAGES = {72--90}, SERIES = {Lecture Notes in Computer Science}, VOLUME = {11136}, ADDRESS = {Monterey, CA, USA}, }
Endnote
%0 Conference Proceedings %A Ho, Vinh Thinh %A Stepanova, Daria %A Gad-Elrab, Mohamed Hassan %A Kharlamov, Evgeny %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Rule Learning from Knowledge Graphs Guided by Embedding Models : %G eng %U http://hdl.handle.net/21.11116/0000-0001-9058-9 %R 10.1007/978-3-030-00671-6_5 %D 2018 %B The 17th International Semantic Web Conference %Z date of event: 2018-10-08 - 2018-10-12 %C Monterey, CA, USA %B The Semantic Web -- ISWC 2018 %E Vrandečić, Denny; Bontcheva, Kalina; Suárez-Figueroa, Mari Carmen; Presutti, Valentina; Celino, Irene; Sabou, Marta; Kaffee, Lucie-Aimée; Simperl, Elena %P 72 - 90 %I Springer %@ 978-3-030-00670-9 %B Lecture Notes in Computer Science %N 11136
[124]
V. T. Ho, D. Stepanova, M. H. Gad-Elrab, E. Kharlamov, and G. Weikum, “Learning Rules from Incomplete KGs using Embeddings,” in ISWC 2018 Posters & Demonstrations, Industry and Blue Sky Ideas Tracks (ISWC-P&D-Industry-BlueSky 2018), Monterey, CA, USA, 2018.
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@inproceedings{StepanovaISWC2018b, TITLE = {Learning Rules from Incomplete {KGs} using Embeddings}, AUTHOR = {Ho, Vinh Thinh and Stepanova, Daria and Gad-Elrab, Mohamed Hassan and Kharlamov, Evgeny and Weikum, Gerhard}, LANGUAGE = {eng}, URL = {http://ceur-ws.org/Vol-2180/paper-25.pdf; urn:nbn:de:0074-2180-3}, PUBLISHER = {ceur.ws.org}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {ISWC 2018 Posters \& Demonstrations, Industry and Blue Sky Ideas Tracks (ISWC-P\&D-Industry-BlueSky 2018)}, EDITOR = {van Erp, Marieke and Atre, Medha and Lopez, Vanessa and Srinivas, Kavitha and Fortuna, Carolina}, EID = {25}, SERIES = {CEUR Workshop Proceedings}, VOLUME = {2180}, ADDRESS = {Monterey, CA, USA}, }
Endnote
%0 Conference Proceedings %A Ho, Vinh Thinh %A Stepanova, Daria %A Gad-Elrab, Mohamed Hassan %A Kharlamov, Evgeny %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Learning Rules from Incomplete KGs using Embeddings : %G eng %U http://hdl.handle.net/21.11116/0000-0001-905B-6 %U http://ceur-ws.org/Vol-2180/paper-25.pdf %D 2018 %B The 17th International Semantic Web Conference %Z date of event: 2018-10-08 - 2018-10-12 %C Monterey, CA, USA %B ISWC 2018 Posters & Demonstrations, Industry and Blue Sky Ideas Tracks %E van Erp, Marieke; Atre, Medha; Lopez, Vanessa; Srinivas, Kavitha; Fortuna, Carolina %Z sequence number: 25 %I ceur.ws.org %B CEUR Workshop Proceedings %N 2180
[125]
K. Hui, A. Yates, K. Berberich, and G. de Melo, “Co-PACRR: A Context-Aware Neural IR Model for Ad-hoc Retrieval,” in WSDM’18, 11th ACM International Conference on Web Search and Data Mining, Marina Del Rey, CA, USA, 2018.
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@inproceedings{Hui_WSDM2018, TITLE = {Co-{PACRR}: {A} Context-Aware Neural {IR} Model for Ad-hoc Retrieval}, AUTHOR = {Hui, Kai and Yates, Andrew and Berberich, Klaus and de Melo, Gerard}, LANGUAGE = {eng}, ISBN = {978-1-4503-5581-0}, DOI = {10.1145/3159652.3159689}, PUBLISHER = {ACM}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {WSDM'18, 11th ACM International Conference on Web Search and Data Mining}, PAGES = {279--287}, ADDRESS = {Marina Del Rey, CA, USA}, }
Endnote
%0 Conference Proceedings %A Hui, Kai %A Yates, Andrew %A Berberich, Klaus %A de Melo, Gerard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Co-PACRR: A Context-Aware Neural IR Model for Ad-hoc Retrieval : %G eng %U http://hdl.handle.net/21.11116/0000-0000-6367-D %R 10.1145/3159652.3159689 %D 2018 %B 11th ACM International Conference on Web Search and Data Mining %Z date of event: 2018-02-05 - 2018-02-09 %C Marina Del Rey, CA, USA %B WSDM'18 %P 279 - 287 %I ACM %@ 978-1-4503-5581-0
[126]
M. Humble, “Redescription Mining on Financial Time Series Data,” Universität des Saarlandes, Saarbrücken, 2018.
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@mastersthesis{Humble_BSc2017, TITLE = {Redescription Mining on Financial Time Series Data}, AUTHOR = {Humble, Megan}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, TYPE = {Bachelor's thesis}, }
Endnote
%0 Thesis %A Humble, Megan %Y Miettinen, Pauli %A referee: Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Redescription Mining on Financial Time Series Data : %G eng %U http://hdl.handle.net/21.11116/0000-0002-F042-4 %I Universität des Saarlandes %C Saarbrücken %D 2018 %P XV, 100 p. %V bachelor %9 bachelor
[127]
H. Jhavar and P. Mirza, “EMOFIEL: Mapping Emotions of Relationships in a Story,” in Companion of the World Wide Web Conference (WWW 2018), Lyon, France, 2018.
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@inproceedings{JhavarWWW2018, TITLE = {{EMOFIEL}: {M}apping Emotions of Relationships in a Story}, AUTHOR = {Jhavar, Harshita and Mirza, Paramita}, LANGUAGE = {eng}, ISBN = {978-1-4503-5640-4}, DOI = {10.1145/3184558.3186989}, PUBLISHER = {ACM}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {Companion of the World Wide Web Conference (WWW 2018)}, EDITOR = {Champin, Pierre-Antoine and Gandon, Fabien and M{\'e}dini, Lionel}, PAGES = {243--246}, ADDRESS = {Lyon, France}, }
Endnote
%0 Conference Proceedings %A Jhavar, Harshita %A Mirza, Paramita %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T EMOFIEL: Mapping Emotions of Relationships in a Story : %G eng %U http://hdl.handle.net/21.11116/0000-0001-4B96-2 %R 10.1145/3184558.3186989 %D 2018 %B The Web Conference %Z date of event: 2018-04-23 - 2018-04-27 %C Lyon, France %B Companion of the World Wide Web Conference %E Champin, Pierre-Antoine; Gandon, Fabien; Médini, Lionel %P 243 - 246 %I ACM %@ 978-1-4503-5640-4
[128]
Z. Jia, A. Abujabal, R. Saha Roy, J. Strötgen, and G. Weikum, “TEQUILA: Temporal Question Answering over Knowledge Bases,” in CIKM’18, 27th ACM International Conference on Information and Knowledge Management, Torino, Italy, 2018.
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@inproceedings{Jia_CIKM2018, TITLE = {{TEQUILA}: {T}emporal Question Answering over Knowledge Bases}, AUTHOR = {Jia, Zhen and Abujabal, Abdalghani and Saha Roy, Rishiraj and Str{\"o}tgen, Jannik and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-6014-2}, DOI = {10.1145/3269206.3269247}, PUBLISHER = {ACM}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {CIKM'18, 27th ACM International Conference on Information and Knowledge Management}, EDITOR = {Cuzzocrea, Alfredo and Allan, James and Paton, Norman and Srivastava, Divesh and Agrawal, Rakesh and Broder, Andrei and Zaki, Mohamed and Candan, Selcuk and Labrinidis, Alexandros and Schuster, Assaf and Wang, Haixun}, PAGES = {1807--1810}, ADDRESS = {Torino, Italy}, }
Endnote
%0 Conference Proceedings %A Jia, Zhen %A Abujabal, Abdalghani %A Saha Roy, Rishiraj %A Strötgen, Jannik %A Weikum, Gerhard %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T TEQUILA: Temporal Question Answering over Knowledge Bases : %G eng %U http://hdl.handle.net/21.11116/0000-0002-A106-1 %R 10.1145/3269206.3269247 %D 2018 %B 27th ACM International Conference on Information and Knowledge Management %Z date of event: 2018-10-22 - 2018-10-26 %C Torino, Italy %B CIKM'18 %E Cuzzocrea, Alfredo; Allan, James; Paton, Norman; Srivastava, Divesh; Agrawal, Rakesh; Broder, Andrei; Zaki, Mohamed; Candan, Selcuk; Labrinidis, Alexandros; Schuster, Assaf; Wang, Haixun %P 1807 - 1810 %I ACM %@ 978-1-4503-6014-2
[129]
Z. Jia, A. Abujabal, R. Saha Roy, J. Strötgen, and G. Weikum, “TempQuestions: A Benchmark for Temporal Question Answering,” in Companion of the World Wide Web Conference (WWW 2018), Lyon, France, 2018.
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@inproceedings{JiaWWW2017, TITLE = {{TempQuestions}: {A} Benchmark for Temporal Question Answering}, AUTHOR = {Jia, Zhen and Abujabal, Abdalghani and Saha Roy, Rishiraj and Str{\"o}tgen, Jannik and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-5640-4}, DOI = {10.1145/3184558.3191536}, PUBLISHER = {ACM}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {Companion of the World Wide Web Conference (WWW 2018)}, EDITOR = {Champin, Pierre-Antoine and Gandon, Fabien and M{\'e}dini, Lionel}, PAGES = {1057--1062}, ADDRESS = {Lyon, France}, }
Endnote
%0 Conference Proceedings %A Jia, Zhen %A Abujabal, Abdalghani %A Saha Roy, Rishiraj %A Strötgen, Jannik %A Weikum, Gerhard %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T TempQuestions: A Benchmark for Temporal Question Answering : %G eng %U http://hdl.handle.net/21.11116/0000-0001-3C80-B %R 10.1145/3184558.3191536 %D 2018 %B The Web Conference %Z date of event: 2018-04-23 - 2018-04-27 %C Lyon, France %B Companion of the World Wide Web Conference %E Champin, Pierre-Antoine; Gandon, Fabien; Médini, Lionel %P 1057 - 1062 %I ACM %@ 978-1-4503-5640-4
[130]
J. Kalofolias, E. Galbrun, and P. Miettinen, “From Sets of Good Redescriptions to Good Sets of Redescriptions,” Knowledge and Information Systems, vol. 57, no. 1, 2018.
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@article{kalofolias18from, TITLE = {From Sets of Good Redescriptions to Good Sets of Redescriptions}, AUTHOR = {Kalofolias, Janis and Galbrun, Esther and Miettinen, Pauli}, LANGUAGE = {eng}, ISSN = {0219-1377}, DOI = {10.1007/s10115-017-1149-7}, PUBLISHER = {Springer}, ADDRESS = {New York, NY}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, JOURNAL = {Knowledge and Information Systems}, VOLUME = {57}, NUMBER = {1}, PAGES = {21--54}, }
Endnote
%0 Journal Article %A Kalofolias, Janis %A Galbrun, Esther %A Miettinen, Pauli %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T From Sets of Good Redescriptions to Good Sets of Redescriptions : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002E-90D1-5 %R 10.1007/s10115-017-1149-7 %7 2018-01-19 %D 2018 %J Knowledge and Information Systems %V 57 %N 1 %& 21 %P 21 - 54 %I Springer %C New York, NY %@ false
[131]
S. Karaev, S. Metzler, and P. Miettinen, “Logistic-Tropical Decompositions and Nested Subgraphs,” in Proceedings of the 14th International Workshop on Mining and Learning with Graphs (MLG 2018), London, UK, 2018.
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@inproceedings{Karaev_MLG2018, TITLE = {Logistic-Tropical Decompositions and Nested Subgraphs}, AUTHOR = {Karaev, Sanjar and Metzler, Saskia and Miettinen, Pauli}, LANGUAGE = {eng}, PUBLISHER = {MLG Workshop}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of the 14th International Workshop on Mining and Learning with Graphs (MLG 2018)}, EID = {35}, ADDRESS = {London, UK}, }
Endnote
%0 Conference Proceedings %A Karaev, Sanjar %A Metzler, Saskia %A Miettinen, Pauli %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Logistic-Tropical Decompositions and Nested Subgraphs : %G eng %U http://hdl.handle.net/21.11116/0000-0002-A91F-E %D 2018 %B 14th International Workshop on Mining and Learning with Graphs %Z date of event: 2018-08-20 - 2018-08-20 %C London, UK %B Proceedings of the 14th International Workshop on Mining and Learning with Graphs %Z sequence number: 35 %I MLG Workshop %U http://www.mlgworkshop.org/2018/papers/MLG2018_paper_35.pdf
[132]
S. Karaev, J. Hook, and P. Miettinen, “Latitude: A Model for Mixed Linear-Tropical Matrix Factorization,” 2018. [Online]. Available: http://arxiv.org/abs/1801.06136. (arXiv: 1801.06136)
Abstract
Nonnegative matrix factorization (NMF) is one of the most frequently-used matrix factorization models in data analysis. A significant reason to the popularity of NMF is its interpretability and the `parts of whole' interpretation of its components. Recently, max-times, or subtropical, matrix factorization (SMF) has been introduced as an alternative model with equally interpretable `winner takes it all' interpretation. In this paper we propose a new mixed linear--tropical model, and a new algorithm, called Latitude, that combines NMF and SMF, being able to smoothly alternate between the two. In our model, the data is modeled using the latent factors and latent parameters that control whether the factors are interpreted as NMF or SMF features, or their mixtures. We present an algorithm for our novel matrix factorization. Our experiments show that our algorithm improves over both baselines, and can yield interpretable results that reveal more of the latent structure than either NMF or SMF alone.
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@online{Karaev2018, TITLE = {Latitude: A Model for Mixed Linear-Tropical Matrix Factorization}, AUTHOR = {Karaev, Sanjar and Hook, James and Miettinen, Pauli}, URL = {http://arxiv.org/abs/1801.06136}, EPRINT = {1801.06136}, EPRINTTYPE = {arXiv}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Nonnegative matrix factorization (NMF) is one of the most frequently-used matrix factorization models in data analysis. A significant reason to the popularity of NMF is its interpretability and the `parts of whole' interpretation of its components. Recently, max-times, or subtropical, matrix factorization (SMF) has been introduced as an alternative model with equally interpretable `winner takes it all' interpretation. In this paper we propose a new mixed linear--tropical model, and a new algorithm, called Latitude, that combines NMF and SMF, being able to smoothly alternate between the two. In our model, the data is modeled using the latent factors and latent parameters that control whether the factors are interpreted as NMF or SMF features, or their mixtures. We present an algorithm for our novel matrix factorization. Our experiments show that our algorithm improves over both baselines, and can yield interpretable results that reveal more of the latent structure than either NMF or SMF alone.}, }
Endnote
%0 Report %A Karaev, Sanjar %A Hook, James %A Miettinen, Pauli %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Latitude: A Model for Mixed Linear-Tropical Matrix Factorization : %U http://hdl.handle.net/21.11116/0000-0000-636B-9 %U http://arxiv.org/abs/1801.06136 %D 2018 %X Nonnegative matrix factorization (NMF) is one of the most frequently-used matrix factorization models in data analysis. A significant reason to the popularity of NMF is its interpretability and the `parts of whole' interpretation of its components. Recently, max-times, or subtropical, matrix factorization (SMF) has been introduced as an alternative model with equally interpretable `winner takes it all' interpretation. In this paper we propose a new mixed linear--tropical model, and a new algorithm, called Latitude, that combines NMF and SMF, being able to smoothly alternate between the two. In our model, the data is modeled using the latent factors and latent parameters that control whether the factors are interpreted as NMF or SMF features, or their mixtures. We present an algorithm for our novel matrix factorization. Our experiments show that our algorithm improves over both baselines, and can yield interpretable results that reveal more of the latent structure than either NMF or SMF alone. %K Computer Science, Learning, cs.LG
[133]
S. Karaev, J. Hook, and P. Miettinen, “Latitude: A Model for Mixed Linear-Tropical Matrix Factorization,” in Proceedings of the 2018 SIAM International Conference on Data Mining (SDM 2018), San Diego, CA, USA, 2018.
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@inproceedings{Karaev_SDM2018, TITLE = {Latitude: A Model for Mixed Linear-Tropical Matrix Factorization}, AUTHOR = {Karaev, Sanjar and Hook, James and Miettinen, Pauli}, LANGUAGE = {eng}, ISBN = {978-1-61197-532-1}, DOI = {10.1137/1.9781611975321.41}, PUBLISHER = {SIAM}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {Proceedings of the 2018 SIAM International Conference on Data Mining (SDM 2018)}, EDITOR = {Ester, Martin and Pedreschi, Dino}, PAGES = {360--368}, ADDRESS = {San Diego, CA, USA}, }
Endnote
%0 Conference Proceedings %A Karaev, Sanjar %A Hook, James %A Miettinen, Pauli %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Latitude: A Model for Mixed Linear-Tropical Matrix Factorization : %G eng %U http://hdl.handle.net/21.11116/0000-0002-5E2D-4 %R 10.1137/1.9781611975321.41 %D 2018 %B SIAM International Conference on Data Mining %Z date of event: 2018-05-03 - 2018-05-05 %C San Diego, CA, USA %B Proceedings of the 2018 SIAM International Conference on Data Mining %E Ester, Martin; Pedreschi, Dino %P 360 - 368 %I SIAM %@ 978-1-61197-532-1
[134]
P. Lahoti, K. Garimella, and A. Gionis, “Joint Non-negative Matrix Factorization for Learning Ideological Leaning on Twitter,” in WSDM’18, 11th ACM International Conference on Web Search and Data Mining, Marina Del Rey, CA, USA, 2018.
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@inproceedings{Lahoti_WSDM2018, TITLE = {Joint Non-negative Matrix Factorization for Learning Ideological Leaning on {T}witter}, AUTHOR = {Lahoti, Preethi and Garimella, Kiran and Gionis, Aristides}, LANGUAGE = {eng}, ISBN = {978-1-4503-5581-0}, DOI = {10.1145/3159652.3159669}, PUBLISHER = {ACM}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {WSDM'18, 11th ACM International Conference on Web Search and Data Mining}, PAGES = {351--359}, ADDRESS = {Marina Del Rey, CA, USA}, }
Endnote
%0 Conference Proceedings %A Lahoti, Preethi %A Garimella, Kiran %A Gionis, Aristides %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T Joint Non-negative Matrix Factorization for Learning Ideological Leaning on Twitter : %G eng %U http://hdl.handle.net/21.11116/0000-0002-9C4F-7 %R 10.1145/3159652.3159669 %D 2018 %B 11th ACM International Conference on Web Search and Data Mining %Z date of event: 2018-02-05 - 2018-02-09 %C Marina Del Rey, CA, USA %B WSDM'18 %P 351 - 359 %I ACM %@ 978-1-4503-5581-0
[135]
P. Lahoti, G. Weikum, and K. P. Gummadi, “iFair: Learning Individually Fair Data Representations for Algorithmic Decision Making,” 2018. [Online]. Available: http://arxiv.org/abs/1806.01059. (arXiv: 1806.01059)
Abstract
People are rated and ranked, towards algorithmic decision making in an increasing number of applications, typically based on machine learning. Research on how to incorporate fairness into such tasks has prevalently pursued the paradigm of group fairness: ensuring that each ethnic or social group receives its fair share in the outcome of classifiers and rankings. In contrast, the alternative paradigm of individual fairness has received relatively little attention. This paper introduces a method for probabilistically clustering user records into a low-rank representation that captures individual fairness yet also achieves high accuracy in classification and regression models. Our notion of individual fairness requires that users who are similar in all task-relevant attributes such as job qualification, and disregarding all potentially discriminating attributes such as gender, should have similar outcomes. Since the case for fairness is ubiquitous across many tasks, we aim to learn general representations that can be applied to arbitrary downstream use-cases. We demonstrate the versatility of our method by applying it to classification and learning-to-rank tasks on two real-world datasets. Our experiments show substantial improvements over the best prior work for this setting.
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@online{Lahoti_arXiv1806.01059, TITLE = {{iFair}: {L}earning Individually Fair Data Representations for Algorithmic Decision Making}, AUTHOR = {Lahoti, Preethi and Weikum, Gerhard and Gummadi, Krishna P.}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1806.01059}, EPRINT = {1806.01059}, EPRINTTYPE = {arXiv}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, ABSTRACT = {People are rated and ranked, towards algorithmic decision making in an increasing number of applications, typically based on machine learning. Research on how to incorporate fairness into such tasks has prevalently pursued the paradigm of group fairness: ensuring that each ethnic or social group receives its fair share in the outcome of classifiers and rankings. In contrast, the alternative paradigm of individual fairness has received relatively little attention. This paper introduces a method for probabilistically clustering user records into a low-rank representation that captures individual fairness yet also achieves high accuracy in classification and regression models. Our notion of individual fairness requires that users who are similar in all task-relevant attributes such as job qualification, and disregarding all potentially discriminating attributes such as gender, should have similar outcomes. Since the case for fairness is ubiquitous across many tasks, we aim to learn general representations that can be applied to arbitrary downstream use-cases. We demonstrate the versatility of our method by applying it to classification and learning-to-rank tasks on two real-world datasets. Our experiments show substantial improvements over the best prior work for this setting.}, }
Endnote
%0 Report %A Lahoti, Preethi %A Weikum, Gerhard %A Gummadi, Krishna P. %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T iFair: Learning Individually Fair Data Representations for Algorithmic Decision Making : %G eng %U http://hdl.handle.net/21.11116/0000-0002-1545-9 %U http://arxiv.org/abs/1806.01059 %D 2018 %X People are rated and ranked, towards algorithmic decision making in an increasing number of applications, typically based on machine learning. Research on how to incorporate fairness into such tasks has prevalently pursued the paradigm of group fairness: ensuring that each ethnic or social group receives its fair share in the outcome of classifiers and rankings. In contrast, the alternative paradigm of individual fairness has received relatively little attention. This paper introduces a method for probabilistically clustering user records into a low-rank representation that captures individual fairness yet also achieves high accuracy in classification and regression models. Our notion of individual fairness requires that users who are similar in all task-relevant attributes such as job qualification, and disregarding all potentially discriminating attributes such as gender, should have similar outcomes. Since the case for fairness is ubiquitous across many tasks, we aim to learn general representations that can be applied to arbitrary downstream use-cases. We demonstrate the versatility of our method by applying it to classification and learning-to-rank tasks on two real-world datasets. Our experiments show substantial improvements over the best prior work for this setting. %K Computer Science, Learning, cs.LG,Computer Science, Information Retrieval, cs.IR,Statistics, Machine Learning, stat.ML
[136]
C. Li, Y. Sun, B. He, L. Wang, K. Hui, A. Yates, L. Sun, and J. Xu, “NPRF: A Neural Pseudo Relevance Feedback Framework for Ad-hoc Information Retrieval,” in The Conference on Empirical Methods in Natural Language Processing (EMNLP 2018), Brussels, Belgium, 2018.
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@inproceedings{DBLP:conf/emnlp/LiSHWHYSX18, TITLE = {{NPRF}: {A} Neural Pseudo Relevance Feedback Framework for Ad-hoc Information Retrieval}, AUTHOR = {Li, Canjia and Sun, Yingfei and He, Ben and Wang, Le and Hui, Kai and Yates, Andrew and Sun, Le and Xu, Jungang}, LANGUAGE = {eng}, ISBN = {978-1-948087-84-1}, URL = {https://aclanthology.info/papers/D18-1478/d18-1478}, PUBLISHER = {ACL}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {The Conference on Empirical Methods in Natural Language Processing (EMNLP 2018)}, EDITOR = {Riloff, Ellen and Chiang, David and Hockenmaier, Julia and Jun'ichi, Tsujii}, PAGES = {4482--4491}, ADDRESS = {Brussels, Belgium}, }
Endnote
%0 Conference Proceedings %A Li, Canjia %A Sun, Yingfei %A He, Ben %A Wang, Le %A Hui, Kai %A Yates, Andrew %A Sun, Le %A Xu, Jungang %+ External Organizations External Organizations External Organizations External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T NPRF: A Neural Pseudo Relevance Feedback Framework for Ad-hoc Information Retrieval : %G eng %U http://hdl.handle.net/21.11116/0000-0003-11BB-7 %U https://aclanthology.info/papers/D18-1478/d18-1478 %D 2018 %B Conference on Empirical Methods in Natural Language Processing %Z date of event: 2018-10-31 - 2018-11-04 %C Brussels, Belgium %B The Conference on Empirical Methods in Natural Language Processing %E Riloff, Ellen; Chiang, David; Hockenmaier, Julia; Jun'ichi, Tsujii %P 4482 - 4491 %I ACL %@ 978-1-948087-84-1
[137]
S. MacAvaney, B. Desmet, A. Cohan, L. Soldaini, A. Yates, A. Zirikly, and N. Goharian, “RSDD-Time: Temporal Annotation of Self-Reported Mental Health Diagnoses,” 2018. [Online]. Available: http://arxiv.org/abs/1806.07916. (arXiv: 1806.07916)
Abstract
Self-reported diagnosis statements have been widely employed in studying language related to mental health in social media. However, existing research has largely ignored the temporality of mental health diagnoses. In this work, we introduce RSDD-Time: a new dataset of 598 manually annotated self-reported depression diagnosis posts from Reddit that include temporal information about the diagnosis. Annotations include whether a mental health condition is present and how recently the diagnosis happened. Furthermore, we include exact temporal spans that relate to the date of diagnosis. This information is valuable for various computational methods to examine mental health through social media because one's mental health state is not static. We also test several baseline classification and extraction approaches, which suggest that extracting temporal information from self-reported diagnosis statements is challenging.
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@online{MacAveray_arXiv1806.07916, TITLE = {{RSDD}-Time: Temporal Annotation of Self-Reported Mental Health Diagnoses}, AUTHOR = {MacAvaney, Sean and Desmet, Bart and Cohan, Arman and Soldaini, Luca and Yates, Andrew and Zirikly, Ayah and Goharian, Nazli}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1806.07916}, EPRINT = {1806.07916}, EPRINTTYPE = {arXiv}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Self-reported diagnosis statements have been widely employed in studying language related to mental health in social media. However, existing research has largely ignored the temporality of mental health diagnoses. In this work, we introduce RSDD-Time: a new dataset of 598 manually annotated self-reported depression diagnosis posts from Reddit that include temporal information about the diagnosis. Annotations include whether a mental health condition is present and how recently the diagnosis happened. Furthermore, we include exact temporal spans that relate to the date of diagnosis. This information is valuable for various computational methods to examine mental health through social media because one's mental health state is not static. We also test several baseline classification and extraction approaches, which suggest that extracting temporal information from self-reported diagnosis statements is challenging.}, }
Endnote
%0 Report %A MacAvaney, Sean %A Desmet, Bart %A Cohan, Arman %A Soldaini, Luca %A Yates, Andrew %A Zirikly, Ayah %A Goharian, Nazli %+ External Organizations External Organizations External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T RSDD-Time: Temporal Annotation of Self-Reported Mental Health Diagnoses : %G eng %U http://hdl.handle.net/21.11116/0000-0002-5ED9-1 %U http://arxiv.org/abs/1806.07916 %D 2018 %X Self-reported diagnosis statements have been widely employed in studying language related to mental health in social media. However, existing research has largely ignored the temporality of mental health diagnoses. In this work, we introduce RSDD-Time: a new dataset of 598 manually annotated self-reported depression diagnosis posts from Reddit that include temporal information about the diagnosis. Annotations include whether a mental health condition is present and how recently the diagnosis happened. Furthermore, we include exact temporal spans that relate to the date of diagnosis. This information is valuable for various computational methods to examine mental health through social media because one's mental health state is not static. We also test several baseline classification and extraction approaches, which suggest that extracting temporal information from self-reported diagnosis statements is challenging. %K Computer Science, Computation and Language, cs.CL
[138]
S. MacAvaney, B. Desmet, A. Cohan, L. Soldaini, A. Yates, A. Zirikly, and N. Goharian, “RSDD-Time: Temporal Annotation of Self-Reported Mental Health Diagnoses,” in Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2018), New Orleans, LA, USA, 2018.
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@inproceedings{MacAvaney_NAACL_HLT2018, TITLE = {{RSDD}-Time: {T}emporal Annotation of Self-Reported Mental Health Diagnoses}, AUTHOR = {MacAvaney, Sean and Desmet, Bart and Cohan, Arman and Soldaini, Luca and Yates, Andrew and Zirikly, Ayah and Goharian, Nazli}, LANGUAGE = {eng}, ISBN = {978-1-948087-12-4}, URL = {http://aclweb.org/anthology/W18-0618}, DOI = {10.18653/v1/W18-0618}, PUBLISHER = {ACL}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2018)}, EDITOR = {Loveys, Kate and Niederhoffer, Kate and Prud'hommeaux, Emily and Resnik, Rebecca and Resnik, Philip}, PAGES = {168--173}, ADDRESS = {New Orleans, LA, USA}, }
Endnote
%0 Conference Proceedings %A MacAvaney, Sean %A Desmet, Bart %A Cohan, Arman %A Soldaini, Luca %A Yates, Andrew %A Zirikly, Ayah %A Goharian, Nazli %+ External Organizations External Organizations External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T RSDD-Time: Temporal Annotation of Self-Reported Mental Health Diagnoses : %G eng %U http://hdl.handle.net/21.11116/0000-0002-5E8C-8 %U http://aclweb.org/anthology/W18-0618 %R 10.18653/v1/W18-0618 %D 2018 %B Fifth Workshop on Computational Linguistics and Clinical Psychology %Z date of event: 2018-06-05 - 2018-06-05 %C New Orleans, LA, USA %B Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology %E Loveys, Kate; Niederhoffer, Kate; Prud'hommeaux, Emily; Resnik, Rebecca; Resnik, Philip %P 168 - 173 %I ACL %@ 978-1-948087-12-4 %U https://aclanthology.info/papers/W18-0618/w18-0618
[139]
S. MacAvaney, A. Yates, A. Cohan, L. Soldaini, K. Hui, N. Goharian, and O. Frieder, “Overcoming Low-Utility Facets for Complex Answer Retrieval,” in SIGIR 2018 Workshops: ProfS, KG4IR, and DATA:SEARCH (ProfS-KG4IR-Data:Search 2018), Ann Arbor, MI, USA, 2018.
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@inproceedings{MacAvaney_KG4IR2018, TITLE = {Overcoming Low-Utility Facets for Complex Answer Retrieval}, AUTHOR = {MacAvaney, Sean and Yates, Andrew and Cohan, Arman and Soldaini, Luca and Hui, Kai and Goharian, Nazli and Frieder, Ophir}, LANGUAGE = {eng}, URL = {http://ceur-ws.org/Vol-2127/paper1-kg4ir.pdf; urn:nbn:de:0074-2127-8}, PUBLISHER = {ceur.ws.org}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {SIGIR 2018 Workshops: ProfS, KG4IR, and DATA:SEARCH (ProfS-KG4IR-Data:Search 2018)}, EDITOR = {Dietz, Laura and Koetzen, Laura and Verberne, Suzan}, PAGES = {46--47}, SERIES = {CEUR Workshop Proceedings}, VOLUME = {2127}, ADDRESS = {Ann Arbor, MI, USA}, }
Endnote
%0 Conference Proceedings %A MacAvaney, Sean %A Yates, Andrew %A Cohan, Arman %A Soldaini, Luca %A Hui, Kai %A Goharian, Nazli %A Frieder, Ophir %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations External Organizations External Organizations %T Overcoming Low-Utility Facets for Complex Answer Retrieval : %G eng %U http://hdl.handle.net/21.11116/0000-0002-5E9C-6 %U http://ceur-ws.org/Vol-2127/paper1-kg4ir.pdf %D 2018 %B Second Workshop on Knowledge Graphs and Semantics for Text Retrieval, Analysis, and Understanding %Z date of event: 2018-07-12 - 2018-07-12 %C Ann Arbor, MI, USA %B SIGIR 2018 Workshops: ProfS, KG4IR, and DATA:SEARCH %E Dietz, Laura; Koetzen, Laura; Verberne, Suzan %P 46 - 47 %I ceur.ws.org %B CEUR Workshop Proceedings %N 2127 %U http://ceur-ws.org/Vol-2127/paper1-kg4ir.pdf
[140]
S. MacAvaney, A. Yates, A. Cohan, L. Soldaini, K. Hui, N. Goharian, and O. Frieder, “Characterizing Question Facets for Complex Answer Retrieval,” 2018. [Online]. Available: http://arxiv.org/abs/1805.00791. (arXiv: 1805.00791)
Abstract
Complex answer retrieval (CAR) is the process of retrieving answers to questions that have multifaceted or nuanced answers. In this work, we present two novel approaches for CAR based on the observation that question facets can vary in utility: from structural (facets that can apply to many similar topics, such as 'History') to topical (facets that are specific to the question's topic, such as the 'Westward expansion' of the United States). We first explore a way to incorporate facet utility into ranking models during query term score combination. We then explore a general approach to reform the structure of ranking models to aid in learning of facet utility in the query-document term matching phase. When we use our techniques with a leading neural ranker on the TREC CAR dataset, our methods rank first in the 2017 TREC CAR benchmark, and yield up to 26% higher performance than the next best method.
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@online{MacAvernay_arXIv1805.00791, TITLE = {Characterizing Question Facets for Complex Answer Retrieval}, AUTHOR = {MacAvaney, Sean and Yates, Andrew and Cohan, Arman and Soldaini, Luca and Hui, Kai and Goharian, Nazli and Frieder, Ophir}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1805.00791}, EPRINT = {1805.00791}, EPRINTTYPE = {arXiv}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Complex answer retrieval (CAR) is the process of retrieving answers to questions that have multifaceted or nuanced answers. In this work, we present two novel approaches for CAR based on the observation that question facets can vary in utility: from structural (facets that can apply to many similar topics, such as 'History') to topical (facets that are specific to the question's topic, such as the 'Westward expansion' of the United States). We first explore a way to incorporate facet utility into ranking models during query term score combination. We then explore a general approach to reform the structure of ranking models to aid in learning of facet utility in the query-document term matching phase. When we use our techniques with a leading neural ranker on the TREC CAR dataset, our methods rank first in the 2017 TREC CAR benchmark, and yield up to 26% higher performance than the next best method.}, }
Endnote
%0 Report %A MacAvaney, Sean %A Yates, Andrew %A Cohan, Arman %A Soldaini, Luca %A Hui, Kai %A Goharian, Nazli %A Frieder, Ophir %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations External Organizations External Organizations %T Characterizing Question Facets for Complex Answer Retrieval : %G eng %U http://hdl.handle.net/21.11116/0000-0002-5ECE-E %U http://arxiv.org/abs/1805.00791 %D 2018 %X Complex answer retrieval (CAR) is the process of retrieving answers to questions that have multifaceted or nuanced answers. In this work, we present two novel approaches for CAR based on the observation that question facets can vary in utility: from structural (facets that can apply to many similar topics, such as 'History') to topical (facets that are specific to the question's topic, such as the 'Westward expansion' of the United States). We first explore a way to incorporate facet utility into ranking models during query term score combination. We then explore a general approach to reform the structure of ranking models to aid in learning of facet utility in the query-document term matching phase. When we use our techniques with a leading neural ranker on the TREC CAR dataset, our methods rank first in the 2017 TREC CAR benchmark, and yield up to 26% higher performance than the next best method. %K Computer Science, Information Retrieval, cs.IR
[141]
S. MacAvaney, A. Yates, A. Cohan, L. Soldaini, K. Hui, N. Goharian, and O. Frieder, “Characterizing Question Facets for Complex Answer Retrieval,” in SIGIR’18, 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, Ann Arbor, MI, USA, 2018.
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@inproceedings{MacAvaney_SIGIR2018, TITLE = {Characterizing Question Facets for Complex Answer Retrieval}, AUTHOR = {MacAvaney, Sean and Yates, Andrew and Cohan, Arman and Soldaini, Luca and Hui, Kai and Goharian, Nazli and Frieder, Ophir}, LANGUAGE = {eng}, ISBN = {978-1-4503-5657-2}, DOI = {10.1145/3209978.3210135}, PUBLISHER = {ACM}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {SIGIR'18, 41st International ACM SIGIR Conference on Research and Development in Information Retrieval}, PAGES = {1205--1208}, ADDRESS = {Ann Arbor, MI, USA}, }
Endnote
%0 Conference Proceedings %A MacAvaney, Sean %A Yates, Andrew %A Cohan, Arman %A Soldaini, Luca %A Hui, Kai %A Goharian, Nazli %A Frieder, Ophir %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations External Organizations External Organizations %T Characterizing Question Facets for Complex Answer Retrieval : %G eng %U http://hdl.handle.net/21.11116/0000-0002-5ECA-2 %R 10.1145/3209978.3210135 %D 2018 %B 41st International ACM SIGIR Conference on Research and Development in Information Retrieval %Z date of event: 2018-07-08 - 2018-07-12 %C Ann Arbor, MI, USA %B SIGIR'18 %P 1205 - 1208 %I ACM %@ 978-1-4503-5657-2
[142]
P. Mandros, M. Boley, and J. Vreeken, “Discovering Reliable Dependencies from Data: Hardness and Improved Algorithms,” in IEEE International Conference on Data Mining (ICDM 2018), Singapore, Singapore, 2018.
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@inproceedings{mandros:18:fedora, TITLE = {Discovering Reliable Dependencies from Data: {H}ardness and Improved Algorithms}, AUTHOR = {Mandros, Panagiotis and Boley, Mario and Vreeken, Jilles}, LANGUAGE = {eng}, ISBN = {978-1-5386-9159-5}, DOI = {10.1109/ICDM.2018.00047}, PUBLISHER = {IEEE}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {IEEE International Conference on Data Mining (ICDM 2018)}, PAGES = {317--326}, ADDRESS = {Singapore, Singapore}, }
Endnote
%0 Conference Proceedings %A Mandros, Panagiotis %A Boley, Mario %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Discovering Reliable Dependencies from Data: Hardness and Improved Algorithms : %G eng %U http://hdl.handle.net/21.11116/0000-0002-9EA2-5 %R 10.1109/ICDM.2018.00047 %D 2018 %B IEEE International Conference on Data Mining %Z date of event: 2018-11-17 - 2018-11-20 %C Singapore, Singapore %B IEEE International Conference on Data Mining %P 317 - 326 %I IEEE %@ 978-1-5386-9159-5
[143]
P. Mandros, M. Boley, and J. Vreeken, “Discovering Reliable Dependencies from Data: Hardness and Improved Algorithms,” 2018. [Online]. Available: http://arxiv.org/abs/1809.05467. (arXiv: 1809.05467)
Abstract
The reliable fraction of information is an attractive score for quantifying (functional) dependencies in high-dimensional data. In this paper, we systematically explore the algorithmic implications of using this measure for optimization. We show that the problem is NP-hard, which justifies the usage of worst-case exponential-time as well as heuristic search methods. We then substantially improve the practical performance for both optimization styles by deriving a novel admissible bounding function that has an unbounded potential for additional pruning over the previously proposed one. Finally, we empirically investigate the approximation ratio of the greedy algorithm and show that it produces highly competitive results in a fraction of time needed for complete branch-and-bound style search.
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@online{Mandros_arXiv1809.05467, TITLE = {Discovering Reliable Dependencies from Data: Hardness and Improved Algorithms}, AUTHOR = {Mandros, Panagiotis and Boley, Mario and Vreeken, Jilles}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1809.05467}, EPRINT = {1809.05467}, EPRINTTYPE = {arXiv}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, ABSTRACT = {The reliable fraction of information is an attractive score for quantifying (functional) dependencies in high-dimensional data. In this paper, we systematically explore the algorithmic implications of using this measure for optimization. We show that the problem is NP-hard, which justifies the usage of worst-case exponential-time as well as heuristic search methods. We then substantially improve the practical performance for both optimization styles by deriving a novel admissible bounding function that has an unbounded potential for additional pruning over the previously proposed one. Finally, we empirically investigate the approximation ratio of the greedy algorithm and show that it produces highly competitive results in a fraction of time needed for complete branch-and-bound style search.}, }
Endnote
%0 Report %A Mandros, Panagiotis %A Boley, Mario %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Discovering Reliable Dependencies from Data: Hardness and Improved Algorithms : %G eng %U http://hdl.handle.net/21.11116/0000-0002-9EC9-A %U http://arxiv.org/abs/1809.05467 %D 2018 %X The reliable fraction of information is an attractive score for quantifying (functional) dependencies in high-dimensional data. In this paper, we systematically explore the algorithmic implications of using this measure for optimization. We show that the problem is NP-hard, which justifies the usage of worst-case exponential-time as well as heuristic search methods. We then substantially improve the practical performance for both optimization styles by deriving a novel admissible bounding function that has an unbounded potential for additional pruning over the previously proposed one. Finally, we empirically investigate the approximation ratio of the greedy algorithm and show that it produces highly competitive results in a fraction of time needed for complete branch-and-bound style search. %K Computer Science, Artificial Intelligence, cs.AI,Computer Science, Databases, cs.DB,Computer Science, Information Theory, cs.IT,Mathematics, Information Theory, math.IT
[144]
A. Marx and J. Vreeken, “Stochastic Complexity for Testing Conditional Independence on Discrete Data,” in Proceedings of the NeurIPS 2018 workshop on Causal Learning (NeurIPS CL 2018), Montréal, Canada, 2018.
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@inproceedings{marx:18:dice, TITLE = {Stochastic Complexity for Testing Conditional Independence on Discrete Data}, AUTHOR = {Marx, Alexander and Vreeken, Jilles}, LANGUAGE = {eng}, URL = {https://drive.google.com/file/d/1mMkO5YZ5gkBRRFbfYb4DDRCsCN243eb2/view}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of the NeurIPS 2018 workshop on Causal Learning (NeurIPS CL 2018)}, EID = {10}, ADDRESS = {Montr{\'e}al, Canada}, }
Endnote
%0 Conference Proceedings %A Marx, Alexander %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Stochastic Complexity for Testing Conditional Independence on Discrete Data : %G eng %U http://hdl.handle.net/21.11116/0000-0002-9EC2-1 %U https://drive.google.com/file/d/1mMkO5YZ5gkBRRFbfYb4DDRCsCN243eb2/view %D 2018 %B NeurIPS 2018 Workshop on Causal Learning %Z date of event: 2018-12-07 - 2018-12-07 %C Montréal, Canada %B Proceedings of the NeurIPS 2018 workshop on Causal Learning %Z sequence number: 10
[145]
A. Marx and J. Vreeken, “Causal Discovery by Telling Apart Parents and Children,” 2018. [Online]. Available: http://arxiv.org/abs/1808.06356. (arXiv: 1808.06356)
Abstract
We consider the problem of inferring the directed, causal graph from observational data, assuming no hidden confounders. We take an information theoretic approach, and make three main contributions. First, we show how through algorithmic information theory we can obtain SCI, a highly robust, effective and computationally efficient test for conditional independence---and show it outperforms the state of the art when applied in constraint-based inference methods such as stable PC. Second, building upon on SCI, we show how to tell apart the parents and children of a given node based on the algorithmic Markov condition. We give the Climb algorithm to efficiently discover the directed, causal Markov blanket---and show it is at least as accurate as inferring the global network, while being much more efficient. Last, but not least, we detail how we can use the Climb score to direct those edges that state of the art causal discovery algorithms based on PC or GES leave undirected---and show this improves their precision, recall and F1 scores by up to 20%.
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@online{Marx_arXiv1808.06356, TITLE = {Causal Discovery by Telling Apart Parents and Children}, AUTHOR = {Marx, Alexander and Vreeken, Jilles}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1808.06356}, EPRINT = {1808.06356}, EPRINTTYPE = {arXiv}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, ABSTRACT = {We consider the problem of inferring the directed, causal graph from observational data, assuming no hidden confounders. We take an information theoretic approach, and make three main contributions. First, we show how through algorithmic information theory we can obtain SCI, a highly robust, effective and computationally efficient test for conditional independence---and show it outperforms the state of the art when applied in constraint-based inference methods such as stable PC. Second, building upon on SCI, we show how to tell apart the parents and children of a given node based on the algorithmic Markov condition. We give the Climb algorithm to efficiently discover the directed, causal Markov blanket---and show it is at least as accurate as inferring the global network, while being much more efficient. Last, but not least, we detail how we can use the Climb score to direct those edges that state of the art causal discovery algorithms based on PC or GES leave undirected---and show this improves their precision, recall and F1 scores by up to 20%.}, }
Endnote
%0 Report %A Marx, Alexander %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Causal Discovery by Telling Apart Parents and Children : %G eng %U http://hdl.handle.net/21.11116/0000-0002-5F36-8 %U http://arxiv.org/abs/1808.06356 %D 2018 %X We consider the problem of inferring the directed, causal graph from observational data, assuming no hidden confounders. We take an information theoretic approach, and make three main contributions. First, we show how through algorithmic information theory we can obtain SCI, a highly robust, effective and computationally efficient test for conditional independence---and show it outperforms the state of the art when applied in constraint-based inference methods such as stable PC. Second, building upon on SCI, we show how to tell apart the parents and children of a given node based on the algorithmic Markov condition. We give the Climb algorithm to efficiently discover the directed, causal Markov blanket---and show it is at least as accurate as inferring the global network, while being much more efficient. Last, but not least, we detail how we can use the Climb score to direct those edges that state of the art causal discovery algorithms based on PC or GES leave undirected---and show this improves their precision, recall and F1 scores by up to 20%. %K Statistics, Machine Learning, stat.ML,Computer Science, Learning, cs.LG
[146]
S. Metzler and P. Miettinen, “Random Graph Generators for Hyperbolic Community Structures,” in Complex Networks and Their Applications VII, Cambridge, UK, 2018.
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@inproceedings{Metzler_COMPLEXNETWORKS2018, TITLE = {Random Graph Generators for Hyperbolic Community Structures}, AUTHOR = {Metzler, Saskia and Miettinen, Pauli}, LANGUAGE = {eng}, ISBN = {978-3-030-05410-6; 978-3-030-05411-3}, DOI = {10.1007/978-3-030-05411-3_54}, PUBLISHER = {Springer}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Complex Networks and Their Applications VII}, EDITOR = {Aiello, Luca Maria and Cherifi, Chantal and Cherifi, Hocine and Lambiotte, Renaud and Li{\'o}, Pietro and Rocha, Luis M.}, PAGES = {680--693}, SERIES = {Studies in Computational Intelligence}, VOLUME = {812}, ADDRESS = {Cambridge, UK}, }
Endnote
%0 Conference Proceedings %A Metzler, Saskia %A Miettinen, Pauli %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Random Graph Generators for Hyperbolic Community Structures : %G eng %U http://hdl.handle.net/21.11116/0000-0002-A929-2 %R 10.1007/978-3-030-05411-3_54 %D 2018 %B 7th International Conference on Complex Networks and Their Applications %Z date of event: 2018-12-11 - 2018-12-13 %C Cambridge, UK %B Complex Networks and Their Applications VII %E Aiello, Luca Maria; Cherifi, Chantal; Cherifi, Hocine; Lambiotte, Renaud; Lió, Pietro; Rocha, Luis M. %P 680 - 693 %I Springer %@ 978-3-030-05410-6 978-3-030-05411-3 %B Studies in Computational Intelligence %N 812
[147]
P. Mirza, F. Darari, and R. Mahendra, “KOI at SemEval-2018 Task 5: Building Knowledge Graph of Incidents,” in Proceedings of the 12th International Workshop on Semantic Evaluation (SemEval-2018), New Orleans, LA, 2018.
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@inproceedings{S18-1010, TITLE = {{KOI} at {SemEval}-2018 Task 5: {B}uilding Knowledge Graph of Incidents}, AUTHOR = {Mirza, Paramita and Darari, Fariz and Mahendra, Rahmad}, LANGUAGE = {eng}, ISBN = {978-1-948087-20-9}, DOI = {10.18653/v1/S18-1010}, PUBLISHER = {ACL}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of the 12th International Workshop on Semantic Evaluation (SemEval-2018)}, EDITOR = {Apidianaki, Marianna and Mohammad, Saif M. and May, Jonathan and Shutova, Ekatarina and Bethard, Steven and Carpuat, Marine}, PAGES = {81--87}, ADDRESS = {New Orleans, LA}, }
Endnote
%0 Conference Proceedings %A Mirza, Paramita %A Darari, Fariz %A Mahendra, Rahmad %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T KOI at SemEval-2018 Task 5: Building Knowledge Graph of Incidents : %G eng %U http://hdl.handle.net/21.11116/0000-0002-A818-6 %R 10.18653/v1/S18-1010 %D 2018 %B Twelfth International Workshop on Semantic Evaluation %Z date of event: 2018-06-05 - 2018-06-06 %C New Orleans, LA %B Proceedings of the 12th International Workshop on Semantic Evaluation %E Apidianaki, Marianna; Mohammad, Saif M.; May, Jonathan; Shutova, Ekatarina; Bethard, Steven; Carpuat, Marine %P 81 - 87 %I ACL %@ 978-1-948087-20-9 %U http://aclweb.org/anthology/S18-1010
[148]
P. Mirza, S. Razniewski, F. Darari, and G. Weikum, “Enriching Knowledge Bases with Counting Quantifiers,” in The Semantic Web -- ISWC 201, Monterey, CA, USA, 2018.
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@inproceedings{MirzaISWC2018, TITLE = {Enriching Knowledge Bases with Counting Quantifiers}, AUTHOR = {Mirza, Paramita and Razniewski, Simon and Darari, Fariz and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-3-030-00670-9}, DOI = {10.1007/978-3-030-00671-6_11}, PUBLISHER = {Springer}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {The Semantic Web -- ISWC 201}, EDITOR = {Vrande{\v c}i{\'c}, Denny and Bontcheva, Kalina and Su{\'a}rez-Figueroa, Mari Carmen and Presutti, Valentina and Celino, Irene and Sabou, Marta and Kaffee, Luci-Aim{\'e}e and Simperl, Elena}, PAGES = {179--197}, SERIES = {Lecture Notes in Computer Science}, VOLUME = {11136}, ADDRESS = {Monterey, CA, USA}, }
Endnote
%0 Conference Proceedings %A Mirza, Paramita %A Razniewski, Simon %A Darari, Fariz %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Enriching Knowledge Bases with Counting Quantifiers : %G eng %U http://hdl.handle.net/21.11116/0000-0001-E170-2 %R 10.1007/978-3-030-00671-6_11 %D 2018 %B The 17th International Semantic Web Conference %Z date of event: 2018-10-08 - 2018-10-12 %C Monterey, CA, USA %B The Semantic Web -- ISWC 201 %E Vrandečić, Denny; Bontcheva, Kalina; Suárez-Figueroa, Mari Carmen; Presutti, Valentina; Celino, Irene; Sabou, Marta; Kaffee, Luci-Aimée; Simperl, Elena %P 179 - 197 %I Springer %@ 978-3-030-00670-9 %B Lecture Notes in Computer Science %N 11136
[149]
P. Mirza, S. Razniewski, F. Darari, and G. Weikum, “Enriching Knowledge Bases with Counting Quantifiers,” 2018. [Online]. Available: http://arxiv.org/abs/1807.03656. (arXiv: 1807.03656)
Abstract
Information extraction traditionally focuses on extracting relations between identifiable entities, such as <Monterey, locatedIn, California>. Yet, texts often also contain Counting information, stating that a subject is in a specific relation with a number of objects, without mentioning the objects themselves, for example, "California is divided into 58 counties". Such counting quantifiers can help in a variety of tasks such as query answering or knowledge base curation, but are neglected by prior work. This paper develops the first full-fledged system for extracting counting information from text, called CINEX. We employ distant supervision using fact counts from a knowledge base as training seeds, and develop novel techniques for dealing with several challenges: (i) non-maximal training seeds due to the incompleteness of knowledge bases, (ii) sparse and skewed observations in text sources, and (iii) high diversity of linguistic patterns. Experiments with five human-evaluated relations show that CINEX can achieve 60% average precision for extracting counting information. In a large-scale experiment, we demonstrate the potential for knowledge base enrichment by applying CINEX to 2,474 frequent relations in Wikidata. CINEX can assert the existence of 2.5M facts for 110 distinct relations, which is 28% more than the existing Wikidata facts for these relations.
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@online{Mirza_arXiv:1807.03656, TITLE = {Enriching Knowledge Bases with Counting Quantifiers}, AUTHOR = {Mirza, Paramita and Razniewski, Simon and Darari, Fariz and Weikum, Gerhard}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1807.03656}, EPRINT = {1807.03656}, EPRINTTYPE = {arXiv}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Information extraction traditionally focuses on extracting relations between identifiable entities, such as <Monterey, locatedIn, California>. Yet, texts often also contain Counting information, stating that a subject is in a specific relation with a number of objects, without mentioning the objects themselves, for example, "California is divided into 58 counties". Such counting quantifiers can help in a variety of tasks such as query answering or knowledge base curation, but are neglected by prior work. This paper develops the first full-fledged system for extracting counting information from text, called CINEX. We employ distant supervision using fact counts from a knowledge base as training seeds, and develop novel techniques for dealing with several challenges: (i) non-maximal training seeds due to the incompleteness of knowledge bases, (ii) sparse and skewed observations in text sources, and (iii) high diversity of linguistic patterns. Experiments with five human-evaluated relations show that CINEX can achieve 60% average precision for extracting counting information. In a large-scale experiment, we demonstrate the potential for knowledge base enrichment by applying CINEX to 2,474 frequent relations in Wikidata. CINEX can assert the existence of 2.5M facts for 110 distinct relations, which is 28% more than the existing Wikidata facts for these relations.}, }
Endnote
%0 Report %A Mirza, Paramita %A Razniewski, Simon %A Darari, Fariz %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Enriching Knowledge Bases with Counting Quantifiers : %G eng %U http://hdl.handle.net/21.11116/0000-0001-E16D-7 %U http://arxiv.org/abs/1807.03656 %D 2018 %X Information extraction traditionally focuses on extracting relations between identifiable entities, such as <Monterey, locatedIn, California>. Yet, texts often also contain Counting information, stating that a subject is in a specific relation with a number of objects, without mentioning the objects themselves, for example, "California is divided into 58 counties". Such counting quantifiers can help in a variety of tasks such as query answering or knowledge base curation, but are neglected by prior work. This paper develops the first full-fledged system for extracting counting information from text, called CINEX. We employ distant supervision using fact counts from a knowledge base as training seeds, and develop novel techniques for dealing with several challenges: (i) non-maximal training seeds due to the incompleteness of knowledge bases, (ii) sparse and skewed observations in text sources, and (iii) high diversity of linguistic patterns. Experiments with five human-evaluated relations show that CINEX can achieve 60% average precision for extracting counting information. In a large-scale experiment, we demonstrate the potential for knowledge base enrichment by applying CINEX to 2,474 frequent relations in Wikidata. CINEX can assert the existence of 2.5M facts for 110 distinct relations, which is 28% more than the existing Wikidata facts for these relations. %K Computer Science, Computation and Language, cs.CL
[150]
A. Mishra, “Leveraging Semantic Annotations for Event-focused Search & Summarization,” Universität des Saarlandes, Saarbrücken, 2018.
Abstract
Today in this Big Data era, overwhelming amounts of textual information across different sources with a high degree of redundancy has made it hard for a consumer to retrospect on past events. A plausible solution is to link semantically similar information contained across the different sources to enforce a structure thereby providing multiple access paths to relevant information. Keeping this larger goal in view, this work uses Wikipedia and online news articles as two prominent yet disparate information sources to address the following three problems: • We address a linking problem to connect Wikipedia excerpts to news articles by casting it into an IR task. Our novel approach integrates time, geolocations, and entities with text to identify relevant documents that can be linked to a given excerpt. • We address an unsupervised extractive multi-document summarization task to generate a fixed-length event digest that facilitates efficient consumption of information contained within a large set of documents. Our novel approach proposes an ILP for global inference across text, time, geolocations, and entities associated with the event. • To estimate temporal focus of short event descriptions, we present a semi-supervised approach that leverages redundancy within a longitudinal news collection to estimate accurate probabilistic time models. Extensive experimental evaluations demonstrate the effectiveness and viability of our proposed approaches towards achieving the larger goal.
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@phdthesis{Mishraphd2018, TITLE = {Leveraging Semantic Annotations for Event-focused Search \& Summarization}, AUTHOR = {Mishra, Arunav}, LANGUAGE = {eng}, URL = {urn:nbn:de:bsz:291-scidok-ds-271081}, DOI = {10.22028/D291-27108}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Today in this Big Data era, overwhelming amounts of textual information across different sources with a high degree of redundancy has made it hard for a consumer to retrospect on past events. A plausible solution is to link semantically similar information contained across the different sources to enforce a structure thereby providing multiple access paths to relevant information. Keeping this larger goal in view, this work uses Wikipedia and online news articles as two prominent yet disparate information sources to address the following three problems: \mbox{$\bullet$} We address a linking problem to connect Wikipedia excerpts to news articles by casting it into an IR task. Our novel approach integrates time, geolocations, and entities with text to identify relevant documents that can be linked to a given excerpt. \mbox{$\bullet$} We address an unsupervised extractive multi-document summarization task to generate a fixed-length event digest that facilitates efficient consumption of information contained within a large set of documents. Our novel approach proposes an ILP for global inference across text, time, geolocations, and entities associated with the event. \mbox{$\bullet$} To estimate temporal focus of short event descriptions, we present a semi-supervised approach that leverages redundancy within a longitudinal news collection to estimate accurate probabilistic time models. Extensive experimental evaluations demonstrate the effectiveness and viability of our proposed approaches towards achieving the larger goal.}, }
Endnote
%0 Thesis %A Mishra, Arunav %Y Berberich, Klaus %A referee: Weikum, Gerhard %A referee: Hauff, Claudia %+ Databases and Information Systems, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Leveraging Semantic Annotations for Event-focused Search & Summarization : %G eng %U http://hdl.handle.net/21.11116/0000-0001-1844-8 %U urn:nbn:de:bsz:291-scidok-ds-271081 %R 10.22028/D291-27108 %I Universit&#228;t des Saarlandes %C Saarbr&#252;cken %D 2018 %8 08.02.2018 %P 252 p. %V phd %9 phd %X Today in this Big Data era, overwhelming amounts of textual information across different sources with a high degree of redundancy has made it hard for a consumer to retrospect on past events. A plausible solution is to link semantically similar information contained across the different sources to enforce a structure thereby providing multiple access paths to relevant information. Keeping this larger goal in view, this work uses Wikipedia and online news articles as two prominent yet disparate information sources to address the following three problems: &#8226; We address a linking problem to connect Wikipedia excerpts to news articles by casting it into an IR task. Our novel approach integrates time, geolocations, and entities with text to identify relevant documents that can be linked to a given excerpt. &#8226; We address an unsupervised extractive multi-document summarization task to generate a fixed-length event digest that facilitates efficient consumption of information contained within a large set of documents. Our novel approach proposes an ILP for global inference across text, time, geolocations, and entities associated with the event. &#8226; To estimate temporal focus of short event descriptions, we present a semi-supervised approach that leverages redundancy within a longitudinal news collection to estimate accurate probabilistic time models. Extensive experimental evaluations demonstrate the effectiveness and viability of our proposed approaches towards achieving the larger goal. %U https://publikationen.sulb.uni-saarland.de/handle/20.500.11880/26995
[151]
S. Nag Chowdhury, N. Tandon, H. Ferhatosmanoglu, and G. Weikum, “VISIR: Visual and Semantic Image Label Refinement,” in WSDM’18, 11th ACM International Conference on Web Search and Data Mining, Marina Del Rey, CA, USA, 2018.
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@inproceedings{NagChowdhury_WSDM2018, TITLE = {{VISIR}: {V}isual and Semantic Image Label Refinement}, AUTHOR = {Nag Chowdhury, Sreyasi and Tandon, Niket and Ferhatosmanoglu, Hakan and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-5581-0}, DOI = {10.1145/3159652.3159693}, PUBLISHER = {ACM}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {WSDM'18, 11th ACM International Conference on Web Search and Data Mining}, PAGES = {117--125}, ADDRESS = {Marina Del Rey, CA, USA}, }
Endnote
%0 Conference Proceedings %A Nag Chowdhury, Sreyasi %A Tandon, Niket %A Ferhatosmanoglu, Hakan %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T VISIR: Visual and Semantic Image Label Refinement : %G eng %U http://hdl.handle.net/21.11116/0000-0001-3CA2-5 %R 10.1145/3159652.3159693 %D 2018 %B 11th ACM International Conference on Web Search and Data Mining %Z date of event: 2018-02-05 - 2018-02-09 %C Marina Del Rey, CA, USA %B WSDM'18 %P 117 - 125 %I ACM %@ 978-1-4503-5581-0
[152]
S. Paramonov, D. Stepanova, and P. Miettinen, “Hybrid ASP-based Approach to Pattern Mining,” 2018. [Online]. Available: http://arxiv.org/abs/1808.07302. (arXiv: 1808.07302)
Abstract
Detecting small sets of relevant patterns from a given dataset is a central challenge in data mining. The relevance of a pattern is based on user-provided criteria; typically, all patterns that satisfy certain criteria are considered relevant. Rule-based languages like Answer Set Programming (ASP) seem well-suited for specifying such criteria in a form of constraints. Although progress has been made, on the one hand, on solving individual mining problems and, on the other hand, developing generic mining systems, the existing methods either focus on scalability or on generality. In this paper we make steps towards combining local (frequency, size, cost) and global (various condensed representations like maximal, closed, skyline) constraints in a generic and efficient way. We present a hybrid approach for itemset, sequence and graph mining which exploits dedicated highly optimized mining systems to detect frequent patterns and then filters the results using declarative ASP. To further demonstrate the generic nature of our hybrid framework we apply it to a problem of approximately tiling a database. Experiments on real-world datasets show the effectiveness of the proposed method and computational gains for itemset, sequence and graph mining, as well as approximate tiling. Under consideration in Theory and Practice of Logic Programming (TPLP).
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@online{Paramonov_arXiv1808.07302, TITLE = {Hybrid {ASP}-based Approach to Pattern Mining}, AUTHOR = {Paramonov, Sergey and Stepanova, Daria and Miettinen, Pauli}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1808.07302}, EPRINT = {1808.07302}, EPRINTTYPE = {arXiv}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Detecting small sets of relevant patterns from a given dataset is a central challenge in data mining. The relevance of a pattern is based on user-provided criteria; typically, all patterns that satisfy certain criteria are considered relevant. Rule-based languages like Answer Set Programming (ASP) seem well-suited for specifying such criteria in a form of constraints. Although progress has been made, on the one hand, on solving individual mining problems and, on the other hand, developing generic mining systems, the existing methods either focus on scalability or on generality. In this paper we make steps towards combining local (frequency, size, cost) and global (various condensed representations like maximal, closed, skyline) constraints in a generic and efficient way. We present a hybrid approach for itemset, sequence and graph mining which exploits dedicated highly optimized mining systems to detect frequent patterns and then filters the results using declarative ASP. To further demonstrate the generic nature of our hybrid framework we apply it to a problem of approximately tiling a database. Experiments on real-world datasets show the effectiveness of the proposed method and computational gains for itemset, sequence and graph mining, as well as approximate tiling. Under consideration in Theory and Practice of Logic Programming (TPLP).}, }
Endnote
%0 Report %A Paramonov, Sergey %A Stepanova, Daria %A Miettinen, Pauli %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Hybrid ASP-based Approach to Pattern Mining : %G eng %U http://hdl.handle.net/21.11116/0000-0002-5E60-9 %U http://arxiv.org/abs/1808.07302 %D 2018 %X Detecting small sets of relevant patterns from a given dataset is a central challenge in data mining. The relevance of a pattern is based on user-provided criteria; typically, all patterns that satisfy certain criteria are considered relevant. Rule-based languages like Answer Set Programming (ASP) seem well-suited for specifying such criteria in a form of constraints. Although progress has been made, on the one hand, on solving individual mining problems and, on the other hand, developing generic mining systems, the existing methods either focus on scalability or on generality. In this paper we make steps towards combining local (frequency, size, cost) and global (various condensed representations like maximal, closed, skyline) constraints in a generic and efficient way. We present a hybrid approach for itemset, sequence and graph mining which exploits dedicated highly optimized mining systems to detect frequent patterns and then filters the results using declarative ASP. To further demonstrate the generic nature of our hybrid framework we apply it to a problem of approximately tiling a database. Experiments on real-world datasets show the effectiveness of the proposed method and computational gains for itemset, sequence and graph mining, as well as approximate tiling. Under consideration in Theory and Practice of Logic Programming (TPLP). %K Computer Science, Artificial Intelligence, cs.AI
[153]
T. Pellissier Tanon, D. Stepanova, S. Razniewski, P. Mirza, and G. Weikum, “Completeness-aware Rule Learning from Knowledge Graphs,” in Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI 2018), Stockholm, Sweden, 2018.
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@inproceedings{PellissierIJCAI2018, TITLE = {Completeness-aware Rule Learning from Knowledge Graphs}, AUTHOR = {Pellissier Tanon, Thomas and Stepanova, Daria and Razniewski, Simon and Mirza, Paramita and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-0-9992411-2-7}, DOI = {10.24963/ijcai.2018/749}, PUBLISHER = {IJCAI}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI 2018)}, EDITOR = {Lang, J{\'e}r{\^o}me}, PAGES = {5339--5343}, ADDRESS = {Stockholm, Sweden}, }
Endnote
%0 Conference Proceedings %A Pellissier Tanon, Thomas %A Stepanova, Daria %A Razniewski, Simon %A Mirza, Paramita %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Completeness-aware Rule Learning from Knowledge Graphs : %G eng %U http://hdl.handle.net/21.11116/0000-0001-9070-D %R 10.24963/ijcai.2018/749 %D 2018 %B 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence %Z date of event: 2018-07-13 - 2018-07-19 %C Stockholm, Sweden %B Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence %E Lang, J&#233;r&#244;me %P 5339 - 5343 %I IJCAI %@ 978-0-9992411-2-7 %U https://doi.org/10.24963/ijcai.2018/749
[154]
M. Ponza, L. Del Corro, and G. Weikum, “Facts That Matter,” in The Conference on Empirical Methods in Natural Language Processing (EMNLP 2018), Brussels, Belgium, 2018.
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@inproceedings{D18-1129, TITLE = {Facts That Matter}, AUTHOR = {Ponza, Marco and Del Corro, Luciano and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-948087-84-1}, URL = {https://aclanthology.coli.uni-saarland.de/papers/D18-1129/d18-1129}, PUBLISHER = {ACL}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {The Conference on Empirical Methods in Natural Language Processing (EMNLP 2018)}, EDITOR = {Riloff, Ellen and Chiang, David and Hockenmaier, Julia and Jun'ichi, Tsujii}, PAGES = {1043--1048}, ADDRESS = {Brussels, Belgium}, }
Endnote
%0 Conference Proceedings %A Ponza, Marco %A Del Corro, Luciano %A Weikum, Gerhard %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Facts That Matter : %G eng %U http://hdl.handle.net/21.11116/0000-0002-A2C1-C %U https://aclanthology.coli.uni-saarland.de/papers/D18-1129/d18-1129 %D 2018 %B Conference on Empirical Methods in Natural Language Processing %Z date of event: 2018-10-31 - 2018-11-04 %C Brussels, Belgium %B The Conference on Empirical Methods in Natural Language Processing %E Riloff, Ellen; Chiang, David; Hockenmaier, Julia; Jun'ichi, Tsujii %P 1043 - 1048 %I ACL %@ 978-1-948087-84-1
[155]
K. Popat, S. Mukherjee, A. Yates, and G. Weikum, “DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning,” in The Conference on Empirical Methods in Natural Language Processing (EMNLP 2018), Brussels, Belgium, 2018.
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@inproceedings{D18-1003, TITLE = {{DeClarE}: {D}ebunking Fake News and False Claims using Evidence-Aware Deep Learning}, AUTHOR = {Popat, Kashyap and Mukherjee, Subhabrata and Yates, Andrew and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-948087-84-1}, URL = {https://aclanthology.coli.uni-saarland.de/papers/D18-1003/d18-1003}, PUBLISHER = {ACL}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {The Conference on Empirical Methods in Natural Language Processing (EMNLP 2018)}, EDITOR = {Riloff, Ellen and Chiang, David and Hockenmaier, Julia and Jun'ichi, Tsujii}, PAGES = {22--32}, ADDRESS = {Brussels, Belgium}, }
Endnote
%0 Conference Proceedings %A Popat, Kashyap %A Mukherjee, Subhabrata %A Yates, Andrew %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning : %G eng %U http://hdl.handle.net/21.11116/0000-0002-B348-3 %U https://aclanthology.coli.uni-saarland.de/papers/D18-1003/d18-1003 %D 2018 %B Conference on Empirical Methods in Natural Language Processing %Z date of event: 2018-10-31 - 2018-11-04 %C Brussels, Belgium %B The Conference on Empirical Methods in Natural Language Processing %E Riloff, Ellen; Chiang, David; Hockenmaier, Julia; Jun'ichi, Tsujii %P 22 - 32 %I ACL %@ 978-1-948087-84-1
[156]
K. Popat, S. Mukherjee, A. Yates, and G. Weikum, “DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning,” 2018. [Online]. Available: http://arxiv.org/abs/1809.06416. (arXiv: 1809.06416)
Abstract
Misinformation such as fake news is one of the big challenges of our society. Research on automated fact-checking has proposed methods based on supervised learning, but these approaches do not consider external evidence apart from labeled training instances. Recent approaches counter this deficit by considering external sources related to a claim. However, these methods require substantial feature modeling and rich lexicons. This paper overcomes these limitations of prior work with an end-to-end model for evidence-aware credibility assessment of arbitrary textual claims, without any human intervention. It presents a neural network model that judiciously aggregates signals from external evidence articles, the language of these articles and the trustworthiness of their sources. It also derives informative features for generating user-comprehensible explanations that makes the neural network predictions transparent to the end-user. Experiments with four datasets and ablation studies show the strength of our method.
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@online{Popat_arXiv1809.06416, TITLE = {{DeClarE}: {D}ebunking Fake News and False Claims using Evidence-Aware Deep Learning}, AUTHOR = {Popat, Kashyap and Mukherjee, Subhabrata and Yates, Andrew and Weikum, Gerhard}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1809.06416}, EPRINT = {1809.06416}, EPRINTTYPE = {arXiv}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Misinformation such as fake news is one of the big challenges of our society. Research on automated fact-checking has proposed methods based on supervised learning, but these approaches do not consider external evidence apart from labeled training instances. Recent approaches counter this deficit by considering external sources related to a claim. However, these methods require substantial feature modeling and rich lexicons. This paper overcomes these limitations of prior work with an end-to-end model for evidence-aware credibility assessment of arbitrary textual claims, without any human intervention. It presents a neural network model that judiciously aggregates signals from external evidence articles, the language of these articles and the trustworthiness of their sources. It also derives informative features for generating user-comprehensible explanations that makes the neural network predictions transparent to the end-user. Experiments with four datasets and ablation studies show the strength of our method.}, }
Endnote
%0 Report %A Popat, Kashyap %A Mukherjee, Subhabrata %A Yates, Andrew %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning : %G eng %U http://hdl.handle.net/21.11116/0000-0002-5EE1-7 %U http://arxiv.org/abs/1809.06416 %D 2018 %X Misinformation such as fake news is one of the big challenges of our society. Research on automated fact-checking has proposed methods based on supervised learning, but these approaches do not consider external evidence apart from labeled training instances. Recent approaches counter this deficit by considering external sources related to a claim. However, these methods require substantial feature modeling and rich lexicons. This paper overcomes these limitations of prior work with an end-to-end model for evidence-aware credibility assessment of arbitrary textual claims, without any human intervention. It presents a neural network model that judiciously aggregates signals from external evidence articles, the language of these articles and the trustworthiness of their sources. It also derives informative features for generating user-comprehensible explanations that makes the neural network predictions transparent to the end-user. Experiments with four datasets and ablation studies show the strength of our method. %K Computer Science, Computation and Language, cs.CL,Computer Science, Learning, cs.LG
[157]
K. Popat, S. Mukherjee, J. Strötgen, and G. Weikum, “CredEye: A Credibility Lens for Analyzing and Explaining Misinformation,” in Companion of the Word Wide Web Conference (WWW 2018), Lyon, France, 2018.
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@inproceedings{PopatWWW2017, TITLE = {{CredEye}: {A} Credibility Lens for Analyzing and Explaining Misinformation}, AUTHOR = {Popat, Kashyap and Mukherjee, Subhabrata and Str{\"o}tgen, Jannik and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-5640-4}, DOI = {10.1145/3184558.3186967}, PUBLISHER = {ACM}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {Companion of the Word Wide Web Conference (WWW 2018)}, EDITOR = {Champin, Pierre-Antoine and Gandon, Fabien and M{\'e}dini, Lionel}, PAGES = {155--158}, ADDRESS = {Lyon, France}, }
Endnote
%0 Conference Proceedings %A Popat, Kashyap %A Mukherjee, Subhabrata %A Str&#246;tgen, Jannik %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T CredEye: A Credibility Lens for Analyzing and Explaining Misinformation : %G eng %U http://hdl.handle.net/21.11116/0000-0000-B546-5 %R 10.1145/3184558.3186967 %D 2018 %B The Web Conference %Z date of event: 2018-04-23 - 2018-04-27 %C Lyon, France %B Companion of the Word Wide Web Conference %E Champin, Pierre-Antoine; Gandon , Fabien; M&#233;dini, Lionel %P 155 - 158 %I ACM %@ 978-1-4503-5640-4
[158]
Y. Ran, B. He, K. Hui, J. Xu, and L. Sun, “Neural Relevance Model Using Similarities with Elite Documents for Effective Clinical Decision Support,” International Journal of Data Mining and Bioinformatics, vol. 20, no. 2, 2018.
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@article{Ran_2018, TITLE = {Neural Relevance Model Using Similarities with Elite Documents for Effective Clinical Decision Support}, AUTHOR = {Ran, Yanhua and He, Ben and Hui, Kai and Xu, Jungang and Sun, Le}, LANGUAGE = {eng}, ISSN = {1748-5673}, DOI = {10.1504/IJDMB.2018.10015098}, PUBLISHER = {Inderscience Publ.}, ADDRESS = {Gen{\`e}ve}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, JOURNAL = {International Journal of Data Mining and Bioinformatics}, VOLUME = {20}, NUMBER = {2}, PAGES = {91--108}, }
Endnote
%0 Journal Article %A Ran, Yanhua %A He, Ben %A Hui, Kai %A Xu, Jungang %A Sun, Le %+ External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T Neural Relevance Model Using Similarities with Elite Documents for Effective Clinical Decision Support : %G eng %U http://hdl.handle.net/21.11116/0000-0002-5743-1 %R 10.1504/IJDMB.2018.10015098 %7 2018 %D 2018 %J International Journal of Data Mining and Bioinformatics %V 20 %N 2 %& 91 %P 91 - 108 %I Inderscience Publ. %C Gen&#232;ve %@ false
[159]
S. Razniewski and G. Weikum, “Knowledge Base Recall: Detecting and Resolving the Unknown Unknowns,” ACM SIGWEB Newsletter, no. Spring, 2018.
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@article{Razniewski2018, TITLE = {Knowledge Base Recall: Detecting and Resolving the Unknown Unknowns}, AUTHOR = {Razniewski, Simon and Weikum, Gerhard}, LANGUAGE = {eng}, DOI = {10.1145/3210578.3210581}, PUBLISHER = {ACM}, ADDRESS = {New York, NY}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, JOURNAL = {ACM SIGWEB Newsletter}, NUMBER = {Spring}, EID = {3}, }
Endnote
%0 Journal Article %A Razniewski, Simon %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Knowledge Base Recall: Detecting and Resolving the Unknown Unknowns : %G eng %U http://hdl.handle.net/21.11116/0000-0001-E175-D %R 10.1145/3210578.3210581 %7 2018 %D 2018 %J ACM SIGWEB Newsletter %N Spring %Z sequence number: 3 %I ACM %C New York, NY
[160]
M. Ringsquandl, E. Kharlamov, D. Stepanova, M. Hildebrandt, S. Lamparter, R. Lepratti, I. Horrocks, and P. Kroeger, “Filling Gaps in Industrial Knowledge Graphs via Event-Enhanced Embedding,” in ISWC 2018 Posters & Demonstrations, Industry and Blue Sky Ideas Tracks co-located with 17th International Semantic Web Conference (ISWC 2018) (ISWC-P&D-Industry-BlueSky 2018), Monterey, CA, USA, 2018.
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@inproceedings{Ringsquandl_ISWC2018_Poster, TITLE = {Filling Gaps in Industrial Knowledge Graphs via Event-Enhanced Embedding}, AUTHOR = {Ringsquandl, Martin and Kharlamov, Evgeny and Stepanova, Daria and Hildebrandt, Marcel and Lamparter, Steffen and Lepratti, Raffaello and Horrocks, Ian and Kroeger, Peer}, LANGUAGE = {eng}, ISSN = {1613-0073}, URL = {http://ceur-ws.org/Vol-2180/paper-52.pdf; urn:nbn:de:0074-2180-3}, PUBLISHER = {CEUR-WS.org}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {ISWC 2018 Posters \& Demonstrations, Industry and Blue Sky Ideas Tracks co-located with 17th International Semantic Web Conference (ISWC 2018) (ISWC-P\&D-Industry-BlueSky 2018)}, EDITOR = {van Erp, Marieke and Atre, Medha and Lopez, Vanessa and Srinivas, Kavitha and Fortuna, Carolina}, EID = {52}, SERIES = {CEUR Workshop Proceedings}, VOLUME = {2180}, ADDRESS = {Monterey, CA, USA}, }
Endnote
%0 Conference Proceedings %A Ringsquandl, Martin %A Kharlamov, Evgeny %A Stepanova, Daria %A Hildebrandt, Marcel %A Lamparter, Steffen %A Lepratti, Raffaello %A Horrocks, Ian %A Kroeger, Peer %+ External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations External Organizations External Organizations %T Filling Gaps in Industrial Knowledge Graphs via Event-Enhanced Embedding : %G eng %U http://hdl.handle.net/21.11116/0000-0002-5E67-2 %U http://ceur-ws.org/Vol-2180/paper-52.pdf %D 2018 %B 17th International Semantic Web Conference %Z date of event: 2018-10-08 - 2018-10-12 %C Monterey, CA, USA %B ISWC 2018 Posters & Demonstrations, Industry and Blue Sky Ideas Tracks co-located with 17th International Semantic Web Conference (ISWC 2018) %E van Erp, Marieke; Atre, Medha; Lopez, Vanessa; Srinivas, Kavitha; Fortuna, Carolina %Z sequence number: 52 %I CEUR-WS.org %B CEUR Workshop Proceedings %N 2180 %@ false
[161]
M. Ringsquandl, E. Kharlamov, D. Stepanova, M. Hildebrandt, S. Lamparter, R. Lepratti, I. Horrocks, and P. Kröger, “Event-Enhanced Learning for KG Completion,” in The Semantic Web (ESWC 2018), Heraklion, Crete, Greece, 2018.
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@inproceedings{Ringsquandl_ESWC2018, TITLE = {Event-Enhanced Learning for {KG} Completion}, AUTHOR = {Ringsquandl, Martin and Kharlamov, Evgeny and Stepanova, Daria and Hildebrandt, Marcel and Lamparter, Steffen and Lepratti, Raffaello and Horrocks, Ian and Kr{\"o}ger, Peer}, LANGUAGE = {eng}, ISBN = {978-3-319-93416-7}, DOI = {10.1007/978-3-319-93417-4_35}, PUBLISHER = {Springer}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {The Semantic Web (ESWC 2018)}, EDITOR = {Gangem, Aldo and Navigli, Roberto and Vidal, Maria-Esther and Hitzler, Pascal and Troncy, Rapha{\"e}l and Hollink, Laura and Tordai, Anna and Alam, Mehwish}, PAGES = {541--559}, SERIES = {Lecture Notes in Computer Science}, VOLUME = {10843}, ADDRESS = {Heraklion, Crete, Greece}, }
Endnote
%0 Conference Proceedings %A Ringsquandl, Martin %A Kharlamov, Evgeny %A Stepanova, Daria %A Hildebrandt, Marcel %A Lamparter, Steffen %A Lepratti, Raffaello %A Horrocks, Ian %A Kr&#246;ger, Peer %+ External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations External Organizations External Organizations %T Event-Enhanced Learning for KG Completion : %G eng %U http://hdl.handle.net/21.11116/0000-0002-5E82-2 %R 10.1007/978-3-319-93417-4_35 %D 2018 %B 15th Extended Semantic Web Conference %Z date of event: 2018-06-03 - 2018-06-07 %C Heraklion, Crete, Greece %B The Semantic Web %E Gangem, Aldo; Navigli, Roberto; Vidal, Maria-Esther; Hitzler, Pascal; Troncy, Rapha&#235;l; Hollink, Laura; Tordai, Anna; Alam, Mehwish %P 541 - 559 %I Springer %@ 978-3-319-93416-7 %B Lecture Notes in Computer Science %N 10843
[162]
D. Seyler, T. Dembelova, L. Del Corro, J. Hoffart, and G. Weikum, “A Study of the Importance of External Knowledge in the Named Entity Recognition Task,” in The 56th Annual Meeting of the Association for Computational Linguistics (ACL 2018), Melbourne, Australia, 2018.
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@inproceedings{AgrawalACL2018b, TITLE = {A Study of the Importance of External Knowledge in the Named Entity Recognition Task}, AUTHOR = {Seyler, Dominic and Dembelova, Tatiana and Del Corro, Luciano and Hoffart, Johannes and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-948087-34-6}, PUBLISHER = {ACL}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {The 56th Annual Meeting of the Association for Computational Linguistics (ACL 2018)}, PAGES = {241--246}, EID = {602}, ADDRESS = {Melbourne, Australia}, }
Endnote
%0 Conference Proceedings %A Seyler, Dominic %A Dembelova, Tatiana %A Del Corro, Luciano %A Hoffart, Johannes %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T A Study of the Importance of External Knowledge in the Named Entity Recognition Task : %G eng %U http://hdl.handle.net/21.11116/0000-0002-0C65-0 %D 2018 %B The 56th Annual Meeting of the Association for Computational Linguistics %Z date of event: 2018-07-15 - 2018-07-20 %C Melbourne, Australia %B The 56th Annual Meeting of the Association for Computational Linguistics %P 241 - 246 %Z sequence number: 602 %I ACL %@ 978-1-948087-34-6 %U http://aclweb.org/anthology/P18-2039
[163]
X. Shen, H. Su, W. Li, and D. Klakow, “NEXUS Network: Connecting the Preceding and the Following in Dialogue Generation,” in The Conference on Empirical Methods in Natural Language Processing (EMNLP 2018), Brussels, Belgium, 2018.
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@inproceedings{shen2018nexus, TITLE = {{NEXUS} Network: {C}onnecting the Preceding and the Following in Dialogue Generation}, AUTHOR = {Shen, Xiaoyu and Su, Hui and Li, Wenjie and Klakow, Dietrich}, LANGUAGE = {eng}, ISBN = {978-1-948087-84-1}, URL = {http://aclweb.org/anthology/D18-1463}, PUBLISHER = {ACL}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {The Conference on Empirical Methods in Natural Language Processing (EMNLP 2018)}, EDITOR = {Riloff, Ellen and Chiang, David and Hockenmaier, Julia and Jun'ichi, Tsujii}, PAGES = {4316--4327}, ADDRESS = {Brussels, Belgium}, }
Endnote
%0 Conference Proceedings %A Shen, Xiaoyu %A Su, Hui %A Li, Wenjie %A Klakow, Dietrich %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations %T NEXUS Network: Connecting the Preceding and the Following in Dialogue Generation : %G eng %U http://hdl.handle.net/21.11116/0000-0003-0DBD-B %U http://aclweb.org/anthology/D18-1463 %D 2018 %B Conference on Empirical Methods in Natural Language Processing %Z date of event: 2018-10-31 - 2018-11-04 %C Brussels, Belgium %B The Conference on Empirical Methods in Natural Language Processing %E Riloff, Ellen; Chiang, David; Hockenmaier, Julia; Jun'ichi, Tsujii %P 4316 - 4327 %I ACL %@ 978-1-948087-84-1
[164]
X. Shen, H. Su, S. Niu, and V. Demberg, “Improving Variational Encoder-Decoders in Dialogue Generation,” in Thirty-Second AAAI Conference on Artificial Intelligence, New Orleans, LA, USA, 2018.
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@inproceedings{shen2018improving, TITLE = {Improving Variational Encoder-Decoders in Dialogue Generation}, AUTHOR = {Shen, Xiaoyu and Su, Hui and Niu, Shuzi and Demberg, Vera}, LANGUAGE = {eng}, ISBN = {978-1-57735-800-8}, URL = {https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16402/16100}, PUBLISHER = {AAAI}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Thirty-Second AAAI Conference on Artificial Intelligence}, PAGES = {5456--5463}, EID = {16402}, ADDRESS = {New Orleans, LA, USA}, }
Endnote
%0 Conference Proceedings %A Shen, Xiaoyu %A Su, Hui %A Niu, Shuzi %A Demberg, Vera %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations %T Improving Variational Encoder-Decoders in Dialogue Generation : %G eng %U http://hdl.handle.net/21.11116/0000-0003-0DAB-F %U https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16402/16100 %D 2018 %B Thirty-Second AAAI Conference on Artificial Intelligence %Z date of event: 2018-02-02 - 2018-02-07 %C New Orleans, LA, USA %B Thirty-Second AAAI Conference on Artificial Intelligence %P 5456 - 5463 %Z sequence number: 16402 %I AAAI %@ 978-1-57735-800-8
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M. Singh, A. Mishra, Y. Oualil, K. Berberich, and D. Klakow, “Long-Span Language Models for Query-Focused Unsupervised Extractive Text Summarization,” in Advances in Information Retrieval (ECIR 2018), Grenoble, France, 2018.
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@inproceedings{SinghECIR2ss18, TITLE = {Long-Span Language Models for Query-Focused Unsupervised Extractive Text Summarization}, AUTHOR = {Singh, Mittul and Mishra, Arunav and Oualil, Youssef and Berberich, Klaus and Klakow, Dietrich}, LANGUAGE = {eng}, ISBN = {978-3-319-76940-0}, DOI = {10.1007/978-3-319-76941-7_59}, PUBLISHER = {Springer}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {Advances in Information Retrieval (ECIR 2018)}, EDITOR = {Pasi, Gabriella and Piwowarski, Benjamin and Azzopardi, Leif and Hanbury, Allan}, PAGES = {657--664}, SERIES = {Lecture Notes in Computer Science}, VOLUME = {10772}, ADDRESS = {Grenoble, France}, }
Endnote
%0 Conference Proceedings %A Singh, Mittul %A Mishra, Arunav %A Oualil, Youssef %A Berberich, Klaus %A Klakow, Dietrich %+ External Organizations External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Long-Span Language Models for Query-Focused Unsupervised Extractive Text Summarization : %G eng %U http://hdl.handle.net/21.11116/0000-0001-413D-2 %R 10.1007/978-3-319-76941-7_59 %D 2018 %B 40th European Conference on IR Research %Z date of event: 2018-03-26 - 2018-03-29 %C Grenoble, France %B Advances in Information Retrieval %E Pasi, Gabriella; Piwowarski, Benjamin; Azzopardi, Leif; Hanbury, Allan %P 657 - 664 %I Springer %@ 978-3-319-76940-0 %B Lecture Notes in Computer Science %N 10772
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A. Spitz, J. Strötgen, and M. Gertz, “Predicting Document Creation Times in News Citation Networks,” in Companion of the World Wide Web Conference (WWW 2018), Lyon, France, 2018.
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@inproceedings{SpitzWWW2017, TITLE = {Predicting Document Creation Times in News Citation Networks}, AUTHOR = {Spitz, Andreas and Str{\"o}tgen, Jannik and Gertz, Michael}, LANGUAGE = {eng}, ISBN = {978-1-4503-5640-4}, DOI = {10.1145/3184558.3191633}, PUBLISHER = {ACM}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {Companion of the World Wide Web Conference (WWW 2018)}, EDITOR = {Champin, Pierre-Antoine and Gandon, Fabien and M{\'e}dini, Lionel}, PAGES = {1731--1736}, ADDRESS = {Lyon, France}, }
Endnote
%0 Conference Proceedings %A Spitz, Andreas %A Str&#246;tgen, Jannik %A Gertz, Michael %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Predicting Document Creation Times in News Citation Networks : %G eng %U http://hdl.handle.net/21.11116/0000-0000-B544-7 %R 10.1145/3184558.3191633 %D 2018 %B The Web Conference %Z date of event: 2018-04-23 - 2018-04-27 %C Lyon, France %B Companion of the World Wide Web Conference %E Champin, Pierre-Antoine; Gandon, Fabien; M&#233;dini, Lionel %P 1731 - 1736 %I ACM %@ 978-1-4503-5640-4
[167]
D. Stepanova, V. T. Ho, and M. H. Gad-Elrab, “Rule Induction and Reasoning over Knowledge Graphs,” in Reasoning Web, Esch-sur-Alzette, Luxembourg, 2018.
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@inproceedings{StepanovaRW2018, TITLE = {Rule Induction and Reasoning over Knowledge Graphs}, AUTHOR = {Stepanova, Daria and Ho, Vinh Thinh and Gad-Elrab, Mohamed Hassan}, LANGUAGE = {eng}, ISBN = {978-3-030-00337-1}, DOI = {10.1007/978-3-030-00338-8_6}, PUBLISHER = {Springer}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {Reasoning Web}, EDITOR = {D'Amato, Claudia and Theobald, Martin}, PAGES = {142--172}, SERIES = {Lecture Notes in Computer Science}, VOLUME = {11078}, ADDRESS = {Esch-sur-Alzette, Luxembourg}, }
Endnote
%0 Conference Proceedings %A Stepanova, Daria %A Ho, Vinh Thinh %A Gad-Elrab, Mohamed Hassan %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Rule Induction and Reasoning over Knowledge Graphs : %G eng %U http://hdl.handle.net/21.11116/0000-0001-9066-9 %R 10.1007/978-3-030-00338-8_6 %D 2018 %B 14th Reasoning Web Summer School %Z date of event: 2018-09-22 - 2018-09-26 %C Esch-sur-Alzette, Luxembourg %B Reasoning Web %E D'Amato, Claudia; Theobald, Martin %P 142 - 172 %I Springer %@ 978-3-030-00337-1 %B Lecture Notes in Computer Science %N 11078
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J. Strötgen, A.-L. Minard, L. Lange, M. Speranza, and B. Magnini, “KRAUTS: A German Temporally Annotated News Corpus,” in Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan, 2018.
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@inproceedings{StroetgenELREC2018, TITLE = {{KRAUTS}: {A German} Temporally Annotated News Corpus}, AUTHOR = {Str{\"o}tgen, Jannik and Minard, Anne-Lyse and Lange, Lukas and Speranza, Manuela and Magnini, Bernardo}, LANGUAGE = {eng}, ISBN = {979-10-95546-00-9}, URL = {http://lrec2018.lrec-conf.org/en/}, PUBLISHER = {ELRA}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, EDITOR = {Calzolari, Nicoletta and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Hasida, Koiti}, PAGES = {536--540}, ADDRESS = {Miyazaki, Japan}, }
Endnote
%0 Conference Proceedings %A Str&#246;tgen, Jannik %A Minard, Anne-Lyse %A Lange, Lukas %A Speranza, Manuela %A Magnini, Bernardo %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T KRAUTS: A German Temporally Annotated News Corpus : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002E-8B8C-E %U http://lrec2018.lrec-conf.org/en/ %D 2018 %B 11th Language Resources and Evaluation Conference %Z date of event: 2018-05-07 - 2018-05-12 %C Miyazaki, Japan %B Eleventh International Conference on Language Resources and Evaluation %E Calzolari, Nicoletta; Choukri, Khalid; Cieri, Christopher; Declerck, Thierry; Goggi, Sara; Hasida, Koiti %P 536 - 540 %I ELRA %@ 979-10-95546-00-9
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J. Strötgen, R. Andrade, and D. Gupta, “Putting Dates on the Map: Harvesting and Analyzing Street Names with Date Mentions and their Explanations,” in JCDL’18, Joint Conference on Digital Libraries, Fort Worth, TX, USA, 2018.
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@inproceedings{StroetgenJCDL2018, TITLE = {Putting Dates on the Map: {H}arvesting and Analyzing Street Names with Date Mentions and their Explanations}, AUTHOR = {Str{\"o}tgen, Jannik and Andrade, Rosita and Gupta, Dhruv}, LANGUAGE = {eng}, ISBN = {978-1-4503-5178-2}, DOI = {10.1145/3197026.3197035}, PUBLISHER = {ACM}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {JCDL'18, Joint Conference on Digital Libraries}, PAGES = {79--88}, ADDRESS = {Fort Worth, TX, USA}, }
Endnote
%0 Conference Proceedings %A Str&#246;tgen, Jannik %A Andrade, Rosita %A Gupta, Dhruv %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Putting Dates on the Map: Harvesting and Analyzing Street Names with Date Mentions and their Explanations : %G eng %U http://hdl.handle.net/21.11116/0000-0000-B548-3 %R 10.1145/3197026.3197035 %D 2018 %B Joint Conference on Digital Libraries %Z date of event: 2018-06-03 - 2018-06-07 %C Fort Worth, TX, USA %B JCDL'18 %P 79 - 88 %I ACM %@ 978-1-4503-5178-2
[170]
H. Su, X. Shen, P. Hu, W. Li, and Y. Chen, “Dialogue Generation with GAN,” in Thirty-Second AAAI Conference on Artificial Intelligence, New Orleans, LA, USA, 2018.
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@inproceedings{Su_AAAI2018, TITLE = {Dialogue Generation with {GAN}}, AUTHOR = {Su, Hui and Shen, Xiaoyu and Hu, Pengwei and Li, Wenjie and Chen, Yun}, LANGUAGE = {eng}, ISBN = {978-1-57735-800-8}, URL = {https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16508/16519}, PUBLISHER = {AAAI}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Thirty-Second AAAI Conference on Artificial Intelligence}, PAGES = {8163--8164}, EID = {16402}, ADDRESS = {New Orleans, LA, USA}, }
Endnote
%0 Conference Proceedings %A Su, Hui %A Shen, Xiaoyu %A Hu, Pengwei %A Li, Wenjie %A Chen, Yun %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations %T Dialogue Generation with GAN : %G eng %U http://hdl.handle.net/21.11116/0000-0004-E562-B %U https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16508/16519 %D 2018 %B Thirty-Second AAAI Conference on Artificial Intelligence %Z date of event: 2018-02-02 - 2018-02-07 %C New Orleans, LA, USA %B Thirty-Second AAAI Conference on Artificial Intelligence %P 8163 - 8164 %Z sequence number: 16402 %I AAAI %@ 978-1-57735-800-8
[171]
L. Wang, Y. Wang, G. de Melo, and G. Weikum, “Five Shades of Untruth: Finer-Grained Classification of Fake News,” in Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mini (ASONAM 2018), Barcelona, Spain, 2018.
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@inproceedings{DBLP:conf/asunam/WangWMW18, TITLE = {Five Shades of Untruth: {F}iner-Grained Classification of Fake News}, AUTHOR = {Wang, Liqiang and Wang, Yafang and de Melo, Gerard and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-5386-6051-5}, DOI = {10.1109/ASONAM.2018.8508256}, PUBLISHER = {IEEE}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mini (ASONAM 2018)}, EDITOR = {Brandes, Ulrik and Reddy, Chandan and Tagarelli, Andrea}, PAGES = {553--594}, ADDRESS = {Barcelona, Spain}, }
Endnote
%0 Conference Proceedings %A Wang, Liqiang %A Wang, Yafang %A de Melo, Gerard %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Five Shades of Untruth: Finer-Grained Classification of Fake News : %G eng %U http://hdl.handle.net/21.11116/0000-0003-3633-7 %R 10.1109/ASONAM.2018.8508256 %D 2018 %B IEEE/ACM International Conference on Advances in Social Networks Analysis and Mini %Z date of event: 2018-08-28 - 2018-08-31 %C Barcelona, Spain %B Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mini %E Brandes, Ulrik; Reddy, Chandan; Tagarelli, Andrea %P 553 - 594 %I IEEE %@ 978-1-5386-6051-5
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H. Wu, Y. Ning, P. Chakraborty, J. Vreeken, N. Tatti, and N. Ramakrishnan, “Generating Realistic Synthetic Population Datasets,” ACM Transactions on Knowledge Discovery from Data, vol. 12, no. 4, 2018.
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@article{Wu_2018, TITLE = {Generating Realistic Synthetic Population Datasets}, AUTHOR = {Wu, Hao and Ning, Yue and Chakraborty, Prithwish and Vreeken, Jilles and Tatti, Nikolaj and Ramakrishnan, Naren}, LANGUAGE = {eng}, DOI = {10.1145/3182383}, PUBLISHER = {ACM}, ADDRESS = {New York, NY}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, JOURNAL = {ACM Transactions on Knowledge Discovery from Data}, VOLUME = {12}, NUMBER = {4}, PAGES = {1--22}, EID = {45}, }
Endnote
%0 Journal Article %A Wu, Hao %A Ning, Yue %A Chakraborty, Prithwish %A Vreeken, Jilles %A Tatti, Nikolaj %A Ramakrishnan, Naren %+ External Organizations External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T Generating Realistic Synthetic Population Datasets : %G eng %U http://hdl.handle.net/21.11116/0000-0002-16ED-B %R 10.1145/3182383 %7 2018 %D 2018 %J ACM Transactions on Knowledge Discovery from Data %O TKDD %V 12 %N 4 %& 1 %P 1 - 22 %Z sequence number: 45 %I ACM %C New York, NY
[173]
Y. Zhao, X. Shen, H. Senuma, and A. Aizawa, “A Comprehensive Study: Sentence Compression with Linguistic Knowledge-enhanced Gated Neural Network,” Data & Knowledge Engineering, vol. 117, 2018.
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@article{Zhao_2018, TITLE = {A Comprehensive Study: Sentence Compression with Linguistic Knowledge-enhanced Gated Neural Network}, AUTHOR = {Zhao, Yang and Shen, Xiaoyu and Senuma, Hajime and Aizawa, Akiko}, LANGUAGE = {eng}, ISSN = {0169-023X}, DOI = {10.1016/j.datak.2018.05.007}, PUBLISHER = {Elsevier}, ADDRESS = {Amsterdam}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, JOURNAL = {Data \& Knowledge Engineering}, VOLUME = {117}, PAGES = {307--318}, }
Endnote
%0 Journal Article %A Zhao, Yang %A Shen, Xiaoyu %A Senuma, Hajime %A Aizawa, Akiko %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T A Comprehensive Study: Sentence Compression with Linguistic Knowledge-enhanced Gated Neural Network : %G eng %U http://hdl.handle.net/21.11116/0000-0002-72D7-B %R 10.1016/j.datak.2018.05.007 %7 2018 %D 2018 %J Data & Knowledge Engineering %V 117 %& 307 %P 307 - 318 %I Elsevier %C Amsterdam %@ false
2017
[174]
A. Abujabal, R. Saha Roy, M. Yahya, and G. Weikum, “QUINT: Interpretable Question Answering over Knowledge Bases,” in The Conference on Empirical Methods in Natural Language Processing (EMNLP 2017), Copenhagen, Denmark, 2017.
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@inproceedings{AbujabalENMLP2017, TITLE = {{QUINT}: {I}nterpretable Question Answering over Knowledge Bases}, AUTHOR = {Abujabal, Abdalghani and Saha Roy, Rishiraj and Yahya, Mohamed and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-945626-97-5}, URL = {http://aclweb.org/anthology/D17-2011}, PUBLISHER = {ACL}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {The Conference on Empirical Methods in Natural Language Processing (EMNLP 2017)}, PAGES = {61--66}, ADDRESS = {Copenhagen, Denmark}, }
Endnote
%0 Conference Proceedings %A Abujabal, Abdalghani %A Saha Roy, Rishiraj %A Yahya, Mohamed %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T QUINT: Interpretable Question Answering over Knowledge Bases : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-F97C-E %U http://aclweb.org/anthology/D17-2011 %D 2017 %B Conference on Empirical Methods in Natural Language Processing %Z date of event: 2017-09-09 - 2017-09-11 %C Copenhagen, Denmark %B The Conference on Empirical Methods in Natural Language Processing %P 61 - 66 %I ACL %@ 978-1-945626-97-5 %U http://aclweb.org/anthology/D17-2011
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A. Abujabal, M. Yahya, M. Riedewald, and G. Weikum, “Automated Template Generation for Question Answering over Knowledge Graphs,” in WWW’17, 26th International Conference on World Wide Web, Perth, Australia, 2017.
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@inproceedings{AbujabalWWW2017, TITLE = {Automated Template Generation for Question Answering over Knowledge Graphs}, AUTHOR = {Abujabal, Abdalghani and Yahya, Mohamed and Riedewald, Mirek and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-4913-0}, DOI = {10.1145/3038912.3052583}, PUBLISHER = {ACM}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {WWW'17, 26th International Conference on World Wide Web}, PAGES = {1191--1200}, ADDRESS = {Perth, Australia}, }
Endnote
%0 Conference Proceedings %A Abujabal, Abdalghani %A Yahya, Mohamed %A Riedewald, Mirek %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Automated Template Generation for Question Answering over Knowledge Graphs : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-4F9C-E %R 10.1145/3038912.3052583 %D 2017 %B 26th International Conference on World Wide Web %Z date of event: 2017-04-03 - 2017-04-07 %C Perth, Australia %B WWW'17 %P 1191 - 1200 %I ACM %@ 978-1-4503-4913-0
[176]
P. Agarwal and J. Strötgen, “Tiwiki: Searching Wikipedia with Temporal Constraints,” in WWW ’17 Companion, Perth, Australia, 2017.
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@inproceedings{AgarwalStroetgen2017_TempWeb, TITLE = {Tiwiki: Searching {W}ikipedia with Temporal Constraints}, AUTHOR = {Agarwal, Prabal and Str{\"o}tgen, Jannik}, LANGUAGE = {eng}, ISBN = {978-1-4503-4914-7}, DOI = {10.1145/3041021.3051112}, PUBLISHER = {ACM}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {WWW '17 Companion}, PAGES = {1595--1600}, ADDRESS = {Perth, Australia}, }
Endnote
%0 Conference Proceedings %A Agarwal, Prabal %A Str&#246;tgen, Jannik %+ International Max Planck Research School, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Tiwiki: Searching Wikipedia with Temporal Constraints : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-53AE-9 %R 10.1145/3041021.3051112 %D 2017 %B 26th International Conference on World Wide Web Companion %Z date of event: 2017-04-03 - 2017-04-07 %C Perth, Australia %B WWW '17 Companion %P 1595 - 1600 %I ACM %@ 978-1-4503-4914-7
[177]
R. Andrade and J. Strötgen, “All Dates Lead to Rome: Extracting and Explaining Temporal References in Street Names,” in WWW’17 Companion, Perth, Australia, 2017.
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@inproceedings{AndradeWWW2017, TITLE = {All Dates Lead to {R}ome: {E}xtracting and Explaining Temporal References in Street Names}, AUTHOR = {Andrade, Rosita and Str{\"o}tgen, Jannik}, LANGUAGE = {eng}, ISBN = {978-1-4503-4914-7}, DOI = {10.1145/3041021.3054249}, PUBLISHER = {ACM}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {WWW'17 Companion}, PAGES = {757--758}, ADDRESS = {Perth, Australia}, }
Endnote
%0 Conference Proceedings %A Andrade, Rosita %A Str&#246;tgen, Jannik %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T All Dates Lead to Rome: Extracting and Explaining Temporal References in Street Names : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-62AE-1 %R 10.1145/3041021.3054249 %D 2017 %B 26th International Conference on World Wide Web %Z date of event: 2017-04-03 - 2017-04-07 %C Perth, Australia %B WWW'17 Companion %P 757 - 758 %I ACM %@ 978-1-4503-4914-7
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A. Bhattacharyya and J. Vreeken, “Efficiently Summarising Event Sequences with Rich Interleaving Patterns,” in Proceedings of the Seventeenth SIAM International Conference on Data Mining (SDM 2017), Houston, TX, USA, 2017.
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@inproceedings{bhattacharyya:17:squish, TITLE = {Efficiently Summarising Event Sequences with Rich Interleaving Patterns}, AUTHOR = {Bhattacharyya, Apratim and Vreeken, Jilles}, LANGUAGE = {eng}, ISBN = {978-1-61197-497-3}, DOI = {10.1137/1.9781611974973.89}, PUBLISHER = {SIAM}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {Proceedings of the Seventeenth SIAM International Conference on Data Mining (SDM 2017)}, PAGES = {795--803}, ADDRESS = {Houston, TX, USA}, }
Endnote
%0 Conference Proceedings %A Bhattacharyya, Apratim %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Efficiently Summarising Event Sequences with Rich Interleaving Patterns : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-4BDC-D %R 10.1137/1.9781611974973.89 %D 2017 %B 17th SIAM International Conference on Data Mining %Z date of event: 2017-04-27 - 2017-04-29 %C Houston, TX, USA %B Proceedings of the Seventeenth SIAM International Conference on Data Mining %P 795 - 803 %I SIAM %@ 978-1-61197-497-3
[179]
A. Bhattacharyya and J. Vreeken, “Efficiently Summarising Event Sequences with Rich Interleaving Patterns,” 2017. [Online]. Available: http://arxiv.org/abs/1701.08096. (arXiv: 1701.08096)
Abstract
Discovering the key structure of a database is one of the main goals of data mining. In pattern set mining we do so by discovering a small set of patterns that together describe the data well. The richer the class of patterns we consider, and the more powerful our description language, the better we will be able to summarise the data. In this paper we propose \ourmethod, a novel greedy MDL-based method for summarising sequential data using rich patterns that are allowed to interleave. Experiments show \ourmethod is orders of magnitude faster than the state of the art, results in better models, as well as discovers meaningful semantics in the form patterns that identify multiple choices of values.
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@online{DBLP:journals/corr/BhattacharyyaV17, TITLE = {Efficiently Summarising Event Sequences with Rich Interleaving Patterns}, AUTHOR = {Bhattacharyya, Apratim and Vreeken, Jilles}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1701.08096}, EPRINT = {1701.08096}, EPRINTTYPE = {arXiv}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Discovering the key structure of a database is one of the main goals of data mining. In pattern set mining we do so by discovering a small set of patterns that together describe the data well. The richer the class of patterns we consider, and the more powerful our description language, the better we will be able to summarise the data. In this paper we propose \ourmethod, a novel greedy MDL-based method for summarising sequential data using rich patterns that are allowed to interleave. Experiments show \ourmethod is orders of magnitude faster than the state of the art, results in better models, as well as discovers meaningful semantics in the form patterns that identify multiple choices of values.}, }
Endnote
%0 Report %A Bhattacharyya, Apratim %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Efficiently Summarising Event Sequences with Rich Interleaving Patterns : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-90E4-A %U http://arxiv.org/abs/1701.08096 %D 2017 %X Discovering the key structure of a database is one of the main goals of data mining. In pattern set mining we do so by discovering a small set of patterns that together describe the data well. The richer the class of patterns we consider, and the more powerful our description language, the better we will be able to summarise the data. In this paper we propose \ourmethod, a novel greedy MDL-based method for summarising sequential data using rich patterns that are allowed to interleave. Experiments show \ourmethod is orders of magnitude faster than the state of the art, results in better models, as well as discovers meaningful semantics in the form patterns that identify multiple choices of values. %K Computer Science, Artificial Intelligence, cs.AI,Computer Science, Databases, cs.DB
[180]
A. J. Biega, A. Ghazimatin, H. Ferhatosmanoglu, K. P. Gummadi, and G. Weikum, “Learning to Un-Rank: Quantifying Search Exposure for Users in Online Communities,” in CIKM’17, 26th ACM International Conference on Information and Knowledge Management, Singapore, Singapore, 2017.
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@inproceedings{Biega_CIKM2017, TITLE = {Learning to Un-Rank: {Q}uantifying Search Exposure for Users in Online Communities}, AUTHOR = {Biega, Asia J. and Ghazimatin, Azin and Ferhatosmanoglu, Hakan and Gummadi, Krishna P. and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-4918-5}, DOI = {10.1145/3132847.3133040}, PUBLISHER = {ACM}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {CIKM'17, 26th ACM International Conference on Information and Knowledge Management}, PAGES = {267--276}, ADDRESS = {Singapore, Singapore}, }
Endnote
%0 Conference Proceedings %A Biega, Asia J. %A Ghazimatin, Azin %A Ferhatosmanoglu, Hakan %A Gummadi, Krishna P. %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Learning to Un-Rank: Quantifying Search Exposure for Users in Online Communities : %G eng %U http://hdl.handle.net/21.11116/0000-0000-3BA4-5 %R 10.1145/3132847.3133040 %D 2017 %B 26th ACM International Conference on Information and Knowledge Management %Z date of event: 2017-11-06 - 2017-11-10 %C Singapore, Singapore %B CIKM'17 %P 267 - 276 %I ACM %@ 978-1-4503-4918-5
[181]
A. J. Biega, R. Saha Roy, and G. Weikum, “Privacy through Solidarity: A User-Utility-Preserving Framework to Counter Profiling,” in SIGIR’17, 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, Shinjuku, Tokyo, Japan, 2017.
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@inproceedings{BiegaSIGIR2017, TITLE = {Privacy through Solidarity: {A} User-Utility-Preserving Framework to Counter Profiling}, AUTHOR = {Biega, Asia J. and Saha Roy, Rishiraj and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-5022-8}, DOI = {10.1145/3077136.3080830}, PUBLISHER = {ACM}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {SIGIR'17, 40th International ACM SIGIR Conference on Research and Development in Information Retrieval}, PAGES = {675--684}, ADDRESS = {Shinjuku, Tokyo, Japan}, }
Endnote
%0 Conference Proceedings %A Biega, Asia J. %A Saha Roy, Rishiraj %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Privacy through Solidarity: A User-Utility-Preserving Framework to Counter Profiling : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-F901-2 %R 10.1145/3077136.3080830 %D 2017 %B 40th International ACM SIGIR Conference on Research and Development in Information Retrieval %Z date of event: 2017-08-07 - 2017-08-11 %C Shinjuku, Tokyo, Japan %B SIGIR'17 %P 675 - 684 %I ACM %@ 978-1-4503-5022-8
[182]
N. Boldyrev, “Alignment of Multi-Cultural Knowledge Repositories,” Universität des Saarlandes, Saarbrücken, 2017.
Abstract
The ability to interconnect multiple knowledge repositories within a single framework is a key asset for various use cases such as document retrieval and question answering. However, independently created repositories are inherently heterogeneous, reflecting their diverse origins. Thus, there is a need to align concepts and entities across knowledge repositories. A limitation of prior work is the assumption of high afinity between the repositories at hand, in terms of structure and terminology. The goal of this dissertation is to develop methods for constructing and curating alignments between multi-cultural knowledge repositories. The first contribution is a system, ACROSS, for reducing the terminological gap between repositories. The second contribution is two alignment methods, LILIANA and SESAME, that cope with structural diversity. The third contribution, LAIKA, is an approach to compute alignments between dynamic repositories. Experiments with a suite ofWeb-scale knowledge repositories show high quality alignments. In addition, the application benefits of LILIANA and SESAME are demonstrated by use cases in search and exploration.
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@phdthesis{BOLDYREVPHD2017, TITLE = {Alignment of Multi-Cultural Knowledge Repositories}, AUTHOR = {Boldyrev, Natalia}, LANGUAGE = {eng}, URL = {urn:nbn:de:bsz:291-scidok-ds-269407}, DOI = {10.22028/D291-26940}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, ABSTRACT = {The ability to interconnect multiple knowledge repositories within a single framework is a key asset for various use cases such as document retrieval and question answering. However, independently created repositories are inherently heterogeneous, reflecting their diverse origins. Thus, there is a need to align concepts and entities across knowledge repositories. A limitation of prior work is the assumption of high afinity between the repositories at hand, in terms of structure and terminology. The goal of this dissertation is to develop methods for constructing and curating alignments between multi-cultural knowledge repositories. The first contribution is a system, ACROSS, for reducing the terminological gap between repositories. The second contribution is two alignment methods, LILIANA and SESAME, that cope with structural diversity. The third contribution, LAIKA, is an approach to compute alignments between dynamic repositories. Experiments with a suite ofWeb-scale knowledge repositories show high quality alignments. In addition, the application benefits of LILIANA and SESAME are demonstrated by use cases in search and exploration.}, }
Endnote
%0 Thesis %A Boldyrev, Natalia %Y Weikum, Gerhard %A referee: Berberich, Klaus %A referee: Spaniol, Marc %+ Databases and Information Systems, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Alignment of Multi-Cultural Knowledge Repositories : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002E-87D8-2 %R 10.22028/D291-26940 %U urn:nbn:de:bsz:291-scidok-ds-269407 %I Universit&#228;t des Saarlandes %C Saarbr&#252;cken %D 2017 %8 06.12.2017 %P X, 124 p. %V phd %9 phd %X The ability to interconnect multiple knowledge repositories within a single framework is a key asset for various use cases such as document retrieval and question answering. However, independently created repositories are inherently heterogeneous, reflecting their diverse origins. Thus, there is a need to align concepts and entities across knowledge repositories. A limitation of prior work is the assumption of high afinity between the repositories at hand, in terms of structure and terminology. The goal of this dissertation is to develop methods for constructing and curating alignments between multi-cultural knowledge repositories. The first contribution is a system, ACROSS, for reducing the terminological gap between repositories. The second contribution is two alignment methods, LILIANA and SESAME, that cope with structural diversity. The third contribution, LAIKA, is an approach to compute alignments between dynamic repositories. Experiments with a suite ofWeb-scale knowledge repositories show high quality alignments. In addition, the application benefits of LILIANA and SESAME are demonstrated by use cases in search and exploration. %U https://publikationen.sulb.uni-saarland.de/handle/20.500.11880/26891
[183]
N. Boldyrev, M. Spaniol, J. Strötgen, and G. Weikum, “SESAME: European Statistics Explored via Semantic Alignment onto Wikipedia,” in WWW’17 Companion, Perth, Australia, 2017.
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@inproceedings{BoldyrevWWW2017, TITLE = {{SESAME}: {E}uropean Statistics Explored via Semantic Alignment onto {Wikipedia}}, AUTHOR = {Boldyrev, Natalia and Spaniol, Marc and Str{\"o}tgen, Jannik and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-4914-7}, DOI = {10.1145/3041021.3054732}, PUBLISHER = {ACM}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {WWW'17 Companion}, PAGES = {177--181}, ADDRESS = {Perth, Australia}, }
Endnote
%0 Conference Proceedings %A Boldyrev, Natalia %A Spaniol, Marc %A Str&#246;tgen, Jannik %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T SESAME: European Statistics Explored via Semantic Alignment onto Wikipedia : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-80B0-0 %R 10.1145/3041021.3054732 %D 2017 %B 26th International Conference on World Wide Web %Z date of event: 2017-04-03 - 2017-04-07 %C Perth, Australia %B WWW'17 Companion %P 177 - 181 %I ACM %@ 978-1-4503-4914-7
[184]
M. Boley, B. R. Goldsmith, L. M. Ghiringhelli, and J. Vreeken, “Identifying Consistent Statements about Numerical Data with Dispersion-Corrected Subgroup Discovery,” 2017. [Online]. Available: http://arxiv.org/abs/1701.07696. (arXiv: 1701.07696)
Abstract
Existing algorithms for subgroup discovery with numerical targets do not optimize the error or target variable dispersion of the groups they find. This often leads to unreliable or inconsistent statements about the data, rendering practical applications, especially in scientific domains, futile. Therefore, we here extend the optimistic estimator framework for optimal subgroup discovery to a new class of objective functions: we show how tight estimators can be computed efficiently for all functions that are determined by subgroup size (non-decreasing dependence), the subgroup median value, and a dispersion measure around the median (non-increasing dependence). In the important special case when dispersion is measured using the average absolute deviation from the median, this novel approach yields a linear time algorithm. Empirical evaluation on a wide range of datasets shows that, when used within branch-and-bound search, this approach is highly efficient and indeed discovers subgroups with much smaller errors.
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@online{DBLP:journals/corr/BoleyGGV17, TITLE = {Identifying Consistent Statements about Numerical Data with Dispersion-Corrected Subgroup Discovery}, AUTHOR = {Boley, Mario and Goldsmith, Bryan R. and Ghiringhelli, Luca M. and Vreeken, Jilles}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1701.07696}, EPRINT = {1701.07696}, EPRINTTYPE = {arXiv}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Existing algorithms for subgroup discovery with numerical targets do not optimize the error or target variable dispersion of the groups they find. This often leads to unreliable or inconsistent statements about the data, rendering practical applications, especially in scientific domains, futile. Therefore, we here extend the optimistic estimator framework for optimal subgroup discovery to a new class of objective functions: we show how tight estimators can be computed efficiently for all functions that are determined by subgroup size (non-decreasing dependence), the subgroup median value, and a dispersion measure around the median (non-increasing dependence). In the important special case when dispersion is measured using the average absolute deviation from the median, this novel approach yields a linear time algorithm. Empirical evaluation on a wide range of datasets shows that, when used within branch-and-bound search, this approach is highly efficient and indeed discovers subgroups with much smaller errors.}, }
Endnote
%0 Report %A Boley, Mario %A Goldsmith, Bryan R. %A Ghiringhelli, Luca M. %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Identifying Consistent Statements about Numerical Data with Dispersion-Corrected Subgroup Discovery : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-90DB-F %U http://arxiv.org/abs/1701.07696 %D 2017 %X Existing algorithms for subgroup discovery with numerical targets do not optimize the error or target variable dispersion of the groups they find. This often leads to unreliable or inconsistent statements about the data, rendering practical applications, especially in scientific domains, futile. Therefore, we here extend the optimistic estimator framework for optimal subgroup discovery to a new class of objective functions: we show how tight estimators can be computed efficiently for all functions that are determined by subgroup size (non-decreasing dependence), the subgroup median value, and a dispersion measure around the median (non-increasing dependence). In the important special case when dispersion is measured using the average absolute deviation from the median, this novel approach yields a linear time algorithm. Empirical evaluation on a wide range of datasets shows that, when used within branch-and-bound search, this approach is highly efficient and indeed discovers subgroups with much smaller errors. %K Computer Science, Artificial Intelligence, cs.AI,Computer Science, Databases, cs.DB
[185]
M. Boley, B. R. Goldsmith, L. M. Ghiringhelli, and J. Vreeken, “Identifying Consistent Statements about Numerical Data with Dispersion-Corrected Subgroup Discovery,” Data Mining and Knowledge Discovery, vol. 31, no. 5, 2017.
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@article{Boley2017, TITLE = {Identifying Consistent Statements about Numerical Data with Dispersion-Corrected Subgroup Discovery}, AUTHOR = {Boley, Mario and Goldsmith, Bryan R. and Ghiringhelli, Luca M. and Vreeken, Jilles}, LANGUAGE = {eng}, DOI = {10.1007/s10618-017-0520-3}, PUBLISHER = {Springer}, ADDRESS = {London}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, JOURNAL = {Data Mining and Knowledge Discovery}, VOLUME = {31}, NUMBER = {5}, PAGES = {1391--1418}, }
Endnote
%0 Journal Article %A Boley, Mario %A Goldsmith, Bryan R. %A Ghiringhelli, Luca M. %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Identifying Consistent Statements about Numerical Data with Dispersion-Corrected Subgroup Discovery : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-90E1-0 %R 10.1007/s10618-017-0520-3 %7 2017-06-28 %D 2017 %8 28.06.2017 %J Data Mining and Knowledge Discovery %V 31 %N 5 %& 1391 %P 1391 - 1418 %I Springer %C London
[186]
K. Budhathoki and J. Vreeken, “MDL for Causal Inference on Discrete Data,” in 17th IEEE International Conference on Data Mining (ICDM 2017), New Orleans, LA, USA, 2017.
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@inproceedings{BudhathokiICDM2017, TITLE = {{MDL} for Causal Inference on Discrete Data}, AUTHOR = {Budhathoki, Kailash and Vreeken, Jilles}, LANGUAGE = {eng}, ISBN = {978-1-5386-3835-4}, DOI = {10.1109/ICDM.2017.87}, PUBLISHER = {IEEE}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {17th IEEE International Conference on Data Mining (ICDM 2017)}, PAGES = {751--756}, ADDRESS = {New Orleans, LA, USA}, }
Endnote
%0 Conference Proceedings %A Budhathoki, Kailash %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T MDL for Causal Inference on Discrete Data : %G eng %U http://hdl.handle.net/21.11116/0000-0000-6458-D %R 10.1109/ICDM.2017.87 %D 2017 %B 17th IEEE International Conference on Data Mining %Z date of event: 2017-11-18 - 2017-11-21 %C New Orleans, LA, USA %B 17th IEEE International Conference on Data Mining %P 751 - 756 %I IEEE %@ 978-1-5386-3835-4
[187]
K. Budhathoki and J. Vreeken, “Correlation by Compression,” in Proceedings of the Seventeenth SIAM International Conference on Data Mining (SDM 2017), Houston, TX, USA, 2017.
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@inproceedings{budhathoki:17:cbc, TITLE = {Correlation by Compression}, AUTHOR = {Budhathoki, Kailash and Vreeken, Jilles}, LANGUAGE = {eng}, ISBN = {978-1-611974-87-4}, DOI = {10.1137/1.9781611974973.59}, PUBLISHER = {SIAM}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {Proceedings of the Seventeenth SIAM International Conference on Data Mining (SDM 2017)}, EDITOR = {Chawla, Nitesh}, PAGES = {525--533}, ADDRESS = {Houston, TX, USA}, }
Endnote
%0 Conference Proceedings %A Budhathoki, Kailash %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Correlation by Compression : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-4BD8-6 %R 10.1137/1.9781611974973.59 %D 2017 %B 17th SIAM International Conference on Data Mining %Z date of event: 2017-04-27 - 2017-04-29 %C Houston, TX, USA %B Proceedings of the Seventeenth SIAM International Conference on Data Mining %E Chawla, Nitesh; Wang, Wei %P 525 - 533 %I SIAM %@ 978-1-611974-87-4
[188]
K. Budhathoki and J. Vreeken, “Causal Inference by Stochastic Complexity,” 2017. [Online]. Available: http://arxiv.org/abs/1702.06776. (arXiv: 1702.06776)
Abstract
The algorithmic Markov condition states that the most likely causal direction between two random variables X and Y can be identified as that direction with the lowest Kolmogorov complexity. Due to the halting problem, however, this notion is not computable. We hence propose to do causal inference by stochastic complexity. That is, we propose to approximate Kolmogorov complexity via the Minimum Description Length (MDL) principle, using a score that is mini-max optimal with regard to the model class under consideration. This means that even in an adversarial setting, such as when the true distribution is not in this class, we still obtain the optimal encoding for the data relative to the class. We instantiate this framework, which we call CISC, for pairs of univariate discrete variables, using the class of multinomial distributions. Experiments show that CISC is highly accurate on synthetic, benchmark, as well as real-world data, outperforming the state of the art by a margin, and scales extremely well with regard to sample and domain sizes.
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@online{DBLP:journals/corr/BudhathokiV17, TITLE = {Causal Inference by Stochastic Complexity}, AUTHOR = {Budhathoki, Kailash and Vreeken, Jilles}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1702.06776}, EPRINT = {1702.06776}, EPRINTTYPE = {arXiv}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, ABSTRACT = {The algorithmic Markov condition states that the most likely causal direction between two random variables X and Y can be identified as that direction with the lowest Kolmogorov complexity. Due to the halting problem, however, this notion is not computable. We hence propose to do causal inference by stochastic complexity. That is, we propose to approximate Kolmogorov complexity via the Minimum Description Length (MDL) principle, using a score that is mini-max optimal with regard to the model class under consideration. This means that even in an adversarial setting, such as when the true distribution is not in this class, we still obtain the optimal encoding for the data relative to the class. We instantiate this framework, which we call CISC, for pairs of univariate discrete variables, using the class of multinomial distributions. Experiments show that CISC is highly accurate on synthetic, benchmark, as well as real-world data, outperforming the state of the art by a margin, and scales extremely well with regard to sample and domain sizes.}, }
Endnote
%0 Report %A Budhathoki, Kailash %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Causal Inference by Stochastic Complexity : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-90F2-A %U http://arxiv.org/abs/1702.06776 %D 2017 %X The algorithmic Markov condition states that the most likely causal direction between two random variables X and Y can be identified as that direction with the lowest Kolmogorov complexity. Due to the halting problem, however, this notion is not computable. We hence propose to do causal inference by stochastic complexity. That is, we propose to approximate Kolmogorov complexity via the Minimum Description Length (MDL) principle, using a score that is mini-max optimal with regard to the model class under consideration. This means that even in an adversarial setting, such as when the true distribution is not in this class, we still obtain the optimal encoding for the data relative to the class. We instantiate this framework, which we call CISC, for pairs of univariate discrete variables, using the class of multinomial distributions. Experiments show that CISC is highly accurate on synthetic, benchmark, as well as real-world data, outperforming the state of the art by a margin, and scales extremely well with regard to sample and domain sizes. %K Computer Science, Learning, cs.LG,Computer Science, Artificial Intelligence, cs.AI
[189]
K. Budhathoki and J. Vreeken, “Causal Inference by Compression,” in 16th IEEE International Conference on Data Mining (ICDM 2016), Barcelona, Spain, 2017.
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@inproceedings{budhathoki:16:origo, TITLE = {Causal Inference by Compression}, AUTHOR = {Budhathoki, Kailash and Vreeken, Jilles}, LANGUAGE = {eng}, ISBN = {978-1-5090-5473-2}, DOI = {10.1109/ICDM.2016.0015}, PUBLISHER = {IEEE}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {16th IEEE International Conference on Data Mining (ICDM 2016)}, EDITOR = {Bonchi, Francesco and Domingo-Ferrer, Josep and Baeza-Yates, Ricardo and Zhou, Zhi-Hua and Wu, Xindong}, PAGES = {41--50}, ADDRESS = {Barcelona, Spain}, }
Endnote
%0 Conference Proceedings %A Budhathoki, Kailash %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Causal Inference by Compression : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-1CC0-6 %R 10.1109/ICDM.2016.0015 %D 2017 %8 02.02.2017 %B 16th International Conference on Data Mining %Z date of event: 2016-12-12 - 2016-12-15 %C Barcelona, Spain %B 16th IEEE International Conference on Data Mining %E Bonchi, Francesco; Domingo-Ferrer, Josep; Baeza-Yates, Ricardo; Zhou, Zhi-Hua; Wu, Xindong %P 41 - 50 %I IEEE %@ 978-1-5090-5473-2
[190]
A. Chakraborty, A. Hannak, A. J. Biega, and K. Gummadi, “Fair Sharing for Sharing Economy Platforms,” in FATREC-Workshop on Responsible Recommendation, Como, Itlay, 2017.
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@inproceedings{Chakraborty_FATREC2017, TITLE = {Fair Sharing for Sharing Economy Platforms}, AUTHOR = {Chakraborty, Abhijnan and Hannak, Aniko and Biega, Asia J. and Gummadi, Krishna}, LANGUAGE = {eng}, DOI = {10.18122/B2BX2S}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {FATREC-Workshop on Responsible Recommendation}, ADDRESS = {Como, Itlay}, }
Endnote
%0 Conference Proceedings %A Chakraborty, Abhijnan %A Hannak, Aniko %A Biega, Asia J. %A Gummadi, Krishna %+ External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Fair Sharing for Sharing Economy Platforms : %G eng %U http://hdl.handle.net/21.11116/0000-0002-57E1-E %R 10.18122/B2BX2S %D 2017 %B Fairness, Accountability and Transparency in Recommender Systems - Workshop on Responsible Recommendation %Z date of event: 2017-08-31 - 2017-08-31 %C Como, Itlay %B FATREC-Workshop on Responsible Recommendation
[191]
C. X. Chu, N. Tandon, and G. Weikum, “Distilling Task Knowledge from How-To Communities,” in WWW’17, 26th International Conference on World Wide Web, Perth, Australia, 2017.
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@inproceedings{Cuong:WWW2017, TITLE = {Distilling Task Knowledge from How-To Communities}, AUTHOR = {Chu, Cuong Xuan and Tandon, Niket and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-4913-0}, DOI = {10.1145/3038912.3052715}, PUBLISHER = {ACM}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {WWW'17, 26th International Conference on World Wide Web}, PAGES = {805--814}, ADDRESS = {Perth, Australia}, }
Endnote
%0 Conference Proceedings %A Chu, Cuong Xuan %A Tandon, Niket %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Distilling Task Knowledge from How-To Communities : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-54BE-E %R 10.1145/3038912.3052715 %D 2017 %B 26th International Conference on World Wide Web %Z date of event: 2017-04-03 - 2017-04-07 %C Perth, Australia %B WWW'17 %P 805 - 814 %I ACM %@ 978-1-4503-4913-0
[192]
A. Cohan, S. Young, A. Yates, and N. Goharian, “Triaging Content Severity in Online Mental Health Forums,” 2017. [Online]. Available: http://arxiv.org/abs/1702.06875. (arXiv: 1702.06875)
Abstract
Mental health forums are online communities where people express their issues and seek help from moderators and other users. In such forums, there are often posts with severe content indicating that the user is in acute distress and there is a risk of attempted self-harm. Moderators need to respond to these severe posts in a timely manner to prevent potential self-harm. However, the large volume of daily posted content makes it difficult for the moderators to locate and respond to these critical posts. We present a framework for triaging user content into four severity categories which are defined based on indications of self-harm ideation. Our models are based on a feature-rich classification framework which includes lexical, psycholinguistic, contextual and topic modeling features. Our approaches improve the state of the art in triaging the content severity in mental health forums by large margins (up to 17% improvement over the F-1 scores). Using the proposed model, we analyze the mental state of users and we show that overall, long-term users of the forum demonstrate a decreased severity of risk over time. Our analysis on the interaction of the moderators with the users further indicates that without an automatic way to identify critical content, it is indeed challenging for the moderators to provide timely response to the users in need.
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@online{Cohan_arXiv2017, TITLE = {Triaging Content Severity in Online Mental Health Forums}, AUTHOR = {Cohan, Arman and Young, Sydney and Yates, Andrew and Goharian, Nazli}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1702.06875}, EPRINT = {1702.06875}, EPRINTTYPE = {arXiv}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Mental health forums are online communities where people express their issues and seek help from moderators and other users. In such forums, there are often posts with severe content indicating that the user is in acute distress and there is a risk of attempted self-harm. Moderators need to respond to these severe posts in a timely manner to prevent potential self-harm. However, the large volume of daily posted content makes it difficult for the moderators to locate and respond to these critical posts. We present a framework for triaging user content into four severity categories which are defined based on indications of self-harm ideation. Our models are based on a feature-rich classification framework which includes lexical, psycholinguistic, contextual and topic modeling features. Our approaches improve the state of the art in triaging the content severity in mental health forums by large margins (up to 17% improvement over the F-1 scores). Using the proposed model, we analyze the mental state of users and we show that overall, long-term users of the forum demonstrate a decreased severity of risk over time. Our analysis on the interaction of the moderators with the users further indicates that without an automatic way to identify critical content, it is indeed challenging for the moderators to provide timely response to the users in need.}, }
Endnote
%0 Report %A Cohan, Arman %A Young, Sydney %A Yates, Andrew %A Goharian, Nazli %+ External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Triaging Content Severity in Online Mental Health Forums : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002E-06AF-F %U http://arxiv.org/abs/1702.06875 %D 2017 %X Mental health forums are online communities where people express their issues and seek help from moderators and other users. In such forums, there are often posts with severe content indicating that the user is in acute distress and there is a risk of attempted self-harm. Moderators need to respond to these severe posts in a timely manner to prevent potential self-harm. However, the large volume of daily posted content makes it difficult for the moderators to locate and respond to these critical posts. We present a framework for triaging user content into four severity categories which are defined based on indications of self-harm ideation. Our models are based on a feature-rich classification framework which includes lexical, psycholinguistic, contextual and topic modeling features. Our approaches improve the state of the art in triaging the content severity in mental health forums by large margins (up to 17% improvement over the F-1 scores). Using the proposed model, we analyze the mental state of users and we show that overall, long-term users of the forum demonstrate a decreased severity of risk over time. Our analysis on the interaction of the moderators with the users further indicates that without an automatic way to identify critical content, it is indeed challenging for the moderators to provide timely response to the users in need. %K Computer Science, Computation and Language, cs.CL,Computer Science, Information Retrieval, cs.IR,cs.SI
[193]
A. Cohan, S. Young, A. Yates, and N. Goharian, “Triaging Content Severity in Online Mental Health Forums,” Journal of the Association for Information Science and Technology, vol. 68, no. 11, 2017.
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@article{Cohan2017, TITLE = {Triaging Content Severity in Online Mental Health Forums}, AUTHOR = {Cohan, Arman and Young, Sydney and Yates, Andrew and Goharian, Nazli}, LANGUAGE = {eng}, ISSN = {2330-1635}, DOI = {10.1002/asi.23865}, PUBLISHER = {Wiley}, ADDRESS = {Chichester, UK}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, JOURNAL = {Journal of the Association for Information Science and Technology}, VOLUME = {68}, NUMBER = {11}, PAGES = {2675--2689}, }
Endnote
%0 Journal Article %A Cohan, Arman %A Young, Sydney %A Yates, Andrew %A Goharian, Nazli %+ External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Triaging Content Severity in Online Mental Health Forums : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002E-06B9-8 %R 10.1002/asi.23865 %7 2017-09-25 %D 2017 %8 25.09.2017 %J Journal of the Association for Information Science and Technology %O asis&t %V 68 %N 11 %& 2675 %P 2675 - 2689 %I Wiley %C Chichester, UK %@ false
[194]
C. Costa, G. Chatzimilioudis, D. Zeinalipour-Yazti, and M. F. Mokbel, “Towards Real-Time Road Traffic Analytics using Telco Big Data,” in BIRTE ’17, Eleventh International Workshop on Real-Time Business Intelligence and Analytics, Munich, Germany, 2017.
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@inproceedings{birte17traffictbd, TITLE = {Towards Real-Time Road Traffic Analytics using {Telco Big Data}}, AUTHOR = {Costa, Constantinos and Chatzimilioudis, Georgios and Zeinalipour-Yazti, Demetrios and Mokbel, Mohamed F.}, LANGUAGE = {eng}, ISBN = {978-1-4503-5425-7}, DOI = {10.1145/3129292.3129296}, PUBLISHER = {ACM}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {BIRTE '17, Eleventh International Workshop on Real-Time Business Intelligence and Analytics}, EDITOR = {Chatziantoniou, Damianos and Castellanos, Malu and Chrysanthis, Panos K.}, EID = {5}, ADDRESS = {Munich, Germany}, }
Endnote
%0 Conference Proceedings %A Costa, Constantinos %A Chatzimilioudis, Georgios %A Zeinalipour-Yazti, Demetrios %A Mokbel, Mohamed F. %+ External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Towards Real-Time Road Traffic Analytics using Telco Big Data : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-DDB7-A %R 10.1145/3129292.3129296 %D 2017 %B Eleventh International Workshop on Real-Time Business Intelligence and Analytics %Z date of event: 2017-08-28 - 2017-08-28 %C Munich, Germany %B BIRTE '17 %E Chatziantoniou, Damianos; Castellanos, Malu; Chrysanthis, Panos K. %Z sequence number: 5 %I ACM %@ 978-1-4503-5425-7
[195]
C. Costa, G. Chatzimilioudis, D. Zeinalipour-Yazti, and M. F. Mokbel, “SPATE: Compacting and Exploring Telco Big Data,” in ICDE 2017, 33rd IEEE International Conference on Data Engineering, San Diego, CA, USA, 2017.
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@inproceedings{icde17-spate-demo, TITLE = {{SPATE}: Compacting and Exploring Telco Big Data}, AUTHOR = {Costa, Constantinos and Chatzimilioudis, Georgios and Zeinalipour-Yazti, Demetrios and Mokbel, Mohamed F.}, LANGUAGE = {eng}, ISBN = {978-1-5090-6544-8}, DOI = {10.1109/ICDE.2017.203}, PUBLISHER = {IEEE}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {ICDE 2017, 33rd IEEE International Conference on Data Engineering}, PAGES = {1419--1420}, ADDRESS = {San Diego, CA, USA}, }
Endnote
%0 Conference Proceedings %A Costa, Constantinos %A Chatzimilioudis, Georgios %A Zeinalipour-Yazti, Demetrios %A Mokbel, Mohamed F. %+ External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T SPATE: Compacting and Exploring Telco Big Data : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-62BA-5 %R 10.1109/ICDE.2017.203 %D 2017 %B 33rd IEEE International Conference on Data Engineering %Z date of event: 2017-04-19 - 2017-04-22 %C San Diego, CA, USA %B ICDE 2017 %P 1419 - 1420 %I IEEE %@ 978-1-5090-6544-8
[196]
C. Costa, G. Chatzimilioudis, D. Zeinalipour-Yazti, and M. F. Mokbel, “Efficient Exploration of Telco Big Data with Compression and Decaying,” in ICDE 2017, 33rd IEEE International Conference on Data Engineering, San Diego, CA, USA, 2017.
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@inproceedings{icde17-spate, TITLE = {Efficient Exploration of Telco Big Data with Compression and Decaying}, AUTHOR = {Costa, Constantinos and Chatzimilioudis, Georgios and Zeinalipour-Yazti, Demetrios and Mokbel, Mohamed F.}, LANGUAGE = {eng}, ISBN = {978-1-5090-6544-8}, DOI = {10.1109/ICDE.2017.175}, PUBLISHER = {IEEE}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {ICDE 2017, 33rd IEEE International Conference on Data Engineering}, PAGES = {1332--1343}, ADDRESS = {San Diego, CA, USA}, }
Endnote
%0 Conference Proceedings %A Costa, Constantinos %A Chatzimilioudis, Georgios %A Zeinalipour-Yazti, Demetrios %A Mokbel, Mohamed F. %+ External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Efficient Exploration of Telco Big Data with Compression and Decaying : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-62B3-4 %R 10.1109/ICDE.2017.175 %D 2017 %B 33rd IEEE International Conference on Data Engineering %Z date of event: 2017-04-19 - 2017-04-22 %C San Diego, CA, USA %B ICDE 2017 %P 1332 - 1343 %I IEEE %@ 978-1-5090-6544-8
[197]
S. Das, A. Mishra, K. Berberich, and V. Setty, “Estimating Event Focus Time Using Neural Word Embeddings,” in CIKM’17, 26th ACM International Conference on Information and Knowledge Management, Singapore, Singapore, 2017.
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@inproceedings{Das_CIKM2017, TITLE = {Estimating Event Focus Time Using Neural Word Embeddings}, AUTHOR = {Das, Supratim and Mishra, Arunav and Berberich, Klaus and Setty, Vinay}, LANGUAGE = {eng}, ISBN = {978-1-4503-4918-5}, DOI = {10.1145/3132847.3133131}, PUBLISHER = {ACM}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {CIKM'17, 26th ACM International Conference on Information and Knowledge Management}, PAGES = {2039--2042}, ADDRESS = {Singapore, Singapore}, }
Endnote
%0 Conference Proceedings %A Das, Supratim %A Mishra, Arunav %A Berberich, Klaus %A Setty, Vinay %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Estimating Event Focus Time Using Neural Word Embeddings : %G eng %U http://hdl.handle.net/21.11116/0000-0000-635B-B %R 10.1145/3132847.3133131 %D 2017 %B 26th ACM International Conference on Information and Knowledge Management %Z date of event: 2017-11-06 - 2017-11-10 %C Singapore, Singapore %B CIKM'17 %P 2039 - 2042 %I ACM %@ 978-1-4503-4918-5
[198]
S. Das, K. Berberich, D. Klakow, A. Mishra, and V. Setty, “Estimating Event Focus Time with Distributed Representation of Words,” Universität des Saarlandes, Saarbrücken, 2017.
Abstract
Time is an important dimension as it aids in disambiguating and understanding news- worthy events that happened in the past. It helps in chronological ordering of events to understand its causality, evolution, and ramifications. In Information Retrieval, time alongside text is known to improve the quality of search results. So, making use of the temporal dimensionality in the text-based analysis is an interesting idea to explore. Considering the importance of time, methods to automatically resolve temporal foci’s of events are essential. In this thesis, we try to solve this research question by training our models on two different kinds of corpora and then evaluate on a set of historical event-queries.
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@mastersthesis{dasmaster17, TITLE = {Estimating Event Focus Time with Distributed Representation of Words}, AUTHOR = {Das, Supratim and Berberich, Klaus and Klakow, Dietrich and Mishra, Arunav and Setty, Vinay}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, ABSTRACT = {Time is an important dimension as it aids in disambiguating and understanding news- worthy events that happened in the past. It helps in chronological ordering of events to understand its causality, evolution, and ramifications. In Information Retrieval, time alongside text is known to improve the quality of search results. So, making use of the temporal dimensionality in the text-based analysis is an interesting idea to explore. Considering the importance of time, methods to automatically resolve temporal foci{\textquoteright}s of events are essential. In this thesis, we try to solve this research question by training our models on two different kinds of corpora and then evaluate on a set of historical event-queries.}, }
Endnote
%0 Thesis %A Das, Supratim %A Berberich, Klaus %A Klakow, Dietrich %A Mishra, Arunav %A Setty, Vinay %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Estimating Event Focus Time with Distributed Representation of Words : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-DFF1-7 %I Universit&#228;t des Saarlandes %C Saarbr&#252;cken %D 2017 %P 83 p. %V master %9 master %X Time is an important dimension as it aids in disambiguating and understanding news- worthy events that happened in the past. It helps in chronological ordering of events to understand its causality, evolution, and ramifications. In Information Retrieval, time alongside text is known to improve the quality of search results. So, making use of the temporal dimensionality in the text-based analysis is an interesting idea to explore. Considering the importance of time, methods to automatically resolve temporal foci&#8217;s of events are essential. In this thesis, we try to solve this research question by training our models on two different kinds of corpora and then evaluate on a set of historical event-queries.
[199]
S. Dutta, “Efficient knowledge Management for Named Entities from Text,” Universität des Saarlandes, Saarbrücken, 2017.
Abstract
The evolution of search from keywords to entities has necessitated the efficient harvesting and management of entity-centric information for constructing knowledge bases catering to various applications such as semantic search, question answering, and information retrieval. The vast amounts of natural language texts available across diverse domains on the Web provide rich sources for discovering facts about named entities such as people, places, and organizations. A key challenge, in this regard, entails the need for precise identification and disambiguation of entities across documents for extraction of attributes/relations and their proper representation in knowledge bases. Additionally, the applicability of such repositories not only involves the quality and accuracy of the stored information, but also storage management and query processing efficiency. This dissertation aims to tackle the above problems by presenting efficient approaches for entity-centric knowledge acquisition from texts and its representation in knowledge repositories. This dissertation presents a robust approach for identifying text phrases pertaining to the same named entity across huge corpora, and their disambiguation to canonical entities present in a knowledge base, by using enriched semantic contexts and link validation encapsulated in a hierarchical clustering framework. This work further presents language and consistency features for classification models to compute the credibility of obtained textual facts, ensuring quality of the extracted information. Finally, an encoding algorithm, using frequent term detection and improved data locality, to represent entities for enhanced knowledge base storage and query performance is presented.
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@phdthesis{duttaphd17, TITLE = {Efficient knowledge Management for Named Entities from Text}, AUTHOR = {Dutta, Sourav}, LANGUAGE = {eng}, URL = {urn:nbn:de:bsz:291-scidok-67924}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, ABSTRACT = {The evolution of search from keywords to entities has necessitated the efficient harvesting and management of entity-centric information for constructing knowledge bases catering to various applications such as semantic search, question answering, and information retrieval. The vast amounts of natural language texts available across diverse domains on the Web provide rich sources for discovering facts about named entities such as people, places, and organizations. A key challenge, in this regard, entails the need for precise identification and disambiguation of entities across documents for extraction of attributes/relations and their proper representation in knowledge bases. Additionally, the applicability of such repositories not only involves the quality and accuracy of the stored information, but also storage management and query processing efficiency. This dissertation aims to tackle the above problems by presenting efficient approaches for entity-centric knowledge acquisition from texts and its representation in knowledge repositories. This dissertation presents a robust approach for identifying text phrases pertaining to the same named entity across huge corpora, and their disambiguation to canonical entities present in a knowledge base, by using enriched semantic contexts and link validation encapsulated in a hierarchical clustering framework. This work further presents language and consistency features for classification models to compute the credibility of obtained textual facts, ensuring quality of the extracted information. Finally, an encoding algorithm, using frequent term detection and improved data locality, to represent entities for enhanced knowledge base storage and query performance is presented.}, }
Endnote
%0 Thesis %A Dutta, Sourav %Y Weikum, Gerhard %A referee: Nejdl, Wolfgang %A referee: Berberich, Klaus %+ Databases and Information Systems, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Efficient knowledge Management for Named Entities from Text : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-A793-E %U urn:nbn:de:bsz:291-scidok-67924 %I Universit&#228;t des Saarlandes %C Saarbr&#252;cken %D 2017 %P xv, 134 p. %V phd %9 phd %X The evolution of search from keywords to entities has necessitated the efficient harvesting and management of entity-centric information for constructing knowledge bases catering to various applications such as semantic search, question answering, and information retrieval. The vast amounts of natural language texts available across diverse domains on the Web provide rich sources for discovering facts about named entities such as people, places, and organizations. A key challenge, in this regard, entails the need for precise identification and disambiguation of entities across documents for extraction of attributes/relations and their proper representation in knowledge bases. Additionally, the applicability of such repositories not only involves the quality and accuracy of the stored information, but also storage management and query processing efficiency. This dissertation aims to tackle the above problems by presenting efficient approaches for entity-centric knowledge acquisition from texts and its representation in knowledge repositories. This dissertation presents a robust approach for identifying text phrases pertaining to the same named entity across huge corpora, and their disambiguation to canonical entities present in a knowledge base, by using enriched semantic contexts and link validation encapsulated in a hierarchical clustering framework. This work further presents language and consistency features for classification models to compute the credibility of obtained textual facts, ensuring quality of the extracted information. Finally, an encoding algorithm, using frequent term detection and improved data locality, to represent entities for enhanced knowledge base storage and query performance is presented. %U http://scidok.sulb.uni-saarland.de/volltexte/2017/6792/http://scidok.sulb.uni-saarland.de/doku/lic_ohne_pod.php?la=de
[200]
P. Ernst, A. Mishra, A. Anand, and V. Setty, “BioNex: A System For Biomedical News Event Exploration,” in SIGIR’17, 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, Shinjuku, Tokyo, Japan, 2017.
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@inproceedings{Ernst_SIGIR2017, TITLE = {{BioNex}: {A} System For Biomedical News Event Exploration}, AUTHOR = {Ernst, Patrick and Mishra, Arunav and Anand, Avishek and Setty, Vinay}, LANGUAGE = {eng}, ISBN = {978-1-4503-5022-8}, DOI = {10.1145/3077136.3084150}, PUBLISHER = {ACM}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {SIGIR'17, 40th International ACM SIGIR Conference on Research and Development in Information Retrieval}, PAGES = {1277--1280}, ADDRESS = {Shinjuku, Tokyo, Japan}, }
Endnote
%0 Conference Proceedings %A Ernst, Patrick %A Mishra, Arunav %A Anand, Avishek %A Setty, Vinay %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T BioNex: A System For Biomedical News Event Exploration : %G eng %U http://hdl.handle.net/21.11116/0000-0002-A2D1-A %R 10.1145/3077136.3084150 %D 2017 %B 40th International ACM SIGIR Conference on Research and Development in Information Retrieval %Z date of event: 2017-08-07 - 2017-08-11 %C Shinjuku, Tokyo, Japan %B SIGIR'17 %P 1277 - 1280 %I ACM %@ 978-1-4503-5022-8
[201]
S. Eslami, “Utility-preserving Profile Removal in Online Forums,” Universität des Saarlandes, Saarbrücken, 2017.
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@mastersthesis{EslamiMSc2017, TITLE = {Utility-preserving Profile Removal in Online Forums}, AUTHOR = {Eslami, Sedigheh}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, }
Endnote
%0 Thesis %A Eslami, Sedigheh %Y Weikum, Gerhard %A referee: Saha Roy, Rishiraj %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Utility-preserving Profile Removal in Online Forums : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-9236-4 %I Universit&#228;t des Saarlandes %C Saarbr&#252;cken %D 2017 %P XII, 66 p. %V master %9 master
[202]
S. Eslami, A. J. Biega, R. Saha Roy, and G. Weikum, “Privacy of Hidden Profiles: Utility-Preserving Profile Removal in Online Forums,” in CIKM’17, 26th ACM International Conference on Information and Knowledge Management, Singapore, Singapore, 2017.
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@inproceedings{Eslami_CIKM2017, TITLE = {Privacy of Hidden Profiles: {U}tility-Preserving Profile Removal in Online Forums}, AUTHOR = {Eslami, Sedigheh and Biega, Asia J. and Saha Roy, Rishiraj and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-4918-5}, DOI = {10.1145/3132847.3133140}, PUBLISHER = {ACM}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {CIKM'17, 26th ACM International Conference on Information and Knowledge Management}, PAGES = {2063--2066}, ADDRESS = {Singapore, Singapore}, }
Endnote
%0 Conference Proceedings %A Eslami, Sedigheh %A Biega, Asia J. %A Saha Roy, Rishiraj %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Privacy of Hidden Profiles: Utility-Preserving Profile Removal in Online Forums : %G eng %U http://hdl.handle.net/21.11116/0000-0000-3BA2-7 %R 10.1145/3132847.3133140 %D 2017 %B 26th ACM International Conference on Information and Knowledge Management %Z date of event: 2017-11-06 - 2017-11-10 %C Singapore, Singapore %B CIKM'17 %P 2063 - 2066 %I ACM %@ 978-1-4503-4918-5
[203]
E. Galbrun and P. Miettinen, “Redescription Mining: An Overview,” IEEE Intelligent Informatics Bulletin, vol. 18, no. 2, 2017.
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@article{Galbrun_2017c, TITLE = {Redescription Mining: An Overview}, AUTHOR = {Galbrun, Esther and Miettinen, Pauli}, LANGUAGE = {eng}, ISSN = {1727-5997}, PUBLISHER = {IEEE Computer Society}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, JOURNAL = {IEEE Intelligent Informatics Bulletin}, VOLUME = {18}, NUMBER = {2}, PAGES = {7--12}, EID = {2}, }
Endnote
%0 Journal Article %A Galbrun, Esther %A Miettinen, Pauli %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Redescription Mining: An Overview : %G eng %U http://hdl.handle.net/21.11116/0000-0002-5E2B-6 %7 2017 %D 2017 %J IEEE Intelligent Informatics Bulletin %V 18 %N 2 %& 7 %P 7 - 12 %Z sequence number: 2 %I IEEE Computer Society %@ false %U http://www.comp.hkbu.edu.hk/~iib/2017/Dec/article2/iib_vol18no2_article2.pdf
[204]
E. Galbrun and P. Miettinen, Redescription Mining. Cham: Springer International, 2017.
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@book{galbrun18redescription, TITLE = {Redescription Mining}, AUTHOR = {Galbrun, Esther and Miettinen, Pauli}, LANGUAGE = {eng}, ISBN = {978-3-319-72889-6}, DOI = {10.1007/978-3-319-72889-6}, PUBLISHER = {Springer International}, ADDRESS = {Cham}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, PAGES = {XI, 80 p.}, }
Endnote
%0 Book %A Galbrun, Esther %A Miettinen, Pauli %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Redescription Mining : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002E-90D3-1 %R 10.1007/978-3-319-72889-6 %@ 978-3-319-72889-6 %I Springer International %C Cham %D 2017 %P XI, 80 p.
[205]
E. Galbrun and P. Miettinen, “Analysing Political Opinions Using Redescription Mining,” in 16th IEEE International Conference on Data Mining Workshops (ICDMW 2016), Barcelona, Spain, 2017.
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@inproceedings{galbrun16analysing, TITLE = {Analysing Political Opinions Using Redescription Mining}, AUTHOR = {Galbrun, Esther and Miettinen, Pauli}, LANGUAGE = {eng}, ISBN = {978-1-5090-5910-2}, DOI = {10.1109/ICDMW.2016.121}, PUBLISHER = {IEEE}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {16th IEEE International Conference on Data Mining Workshops (ICDMW 2016)}, EDITOR = {Domeniconi, Carlotta and Gullo, Francesco and Bonchi, Francesco and Domingo-Ferrer, Josep and Baeza-Yates, Ricardo and Zhou, Zhi-Hua and Wu, Xindong}, PAGES = {422--427}, ADDRESS = {Barcelona, Spain}, }
Endnote
%0 Conference Proceedings %A Galbrun, Esther %A Miettinen, Pauli %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Analysing Political Opinions Using Redescription Mining : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-2247-5 %R 10.1109/ICDMW.2016.121 %D 2017 %8 02.02.2017 %B 16th International Conference on Data Mining %Z date of event: 2015-12-12 - 2015-12-15 %C Barcelona, Spain %B 16th IEEE International Conference on Data Mining Workshops %E Domeniconi, Carlotta; Gullo, Francesco; Bonchi, Francesco; Domingo-Ferrer, Josep; Baeza-Yates, Ricardo; Zhou, Zhi-Hua; Wu, Xindong %P 422 - 427 %I IEEE %@ 978-1-5090-5910-2
[206]
K. Gashteovski, R. Gemulla, and L. Del Corro, “MinIE: Minimizing Facts in Open Information Extraction,” in The Conference on Empirical Methods in Natural Language Processing (EMNLP 2017), Copenhagen, Denmark, 2017.
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@inproceedings{DBLP:conf/emnlp/GashteovskiGC17, TITLE = {{MinIE}: {M}inimizing Facts in Open Information Extraction}, AUTHOR = {Gashteovski, Kiril and Gemulla, Rainer and Del Corro, Luciano}, LANGUAGE = {eng}, ISBN = {978-1-945626-83-8}, URL = {http://aclanthology.info/papers/D17-1277/d17-1277}, PUBLISHER = {ACL}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {The Conference on Empirical Methods in Natural Language Processing (EMNLP 2017)}, PAGES = {2620--2630}, ADDRESS = {Copenhagen, Denmark}, }
Endnote
%0 Conference Proceedings %A Gashteovski, Kiril %A Gemulla, Rainer %A Del Corro, Luciano %+ External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T MinIE: Minimizing Facts in Open Information Extraction : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002E-30F4-2 %U http://aclanthology.info/papers/D17-1277/d17-1277 %D 2017 %B Conference on Empirical Methods in Natural Language Processing %Z date of event: 2017-09-09 - 2017-09-11 %C Copenhagen, Denmark %B The Conference on Empirical Methods in Natural Language Processing %P 2620 - 2630 %I ACL %@ 978-1-945626-83-8 %U http://www.aclweb.org/anthology/D17-1277
[207]
X. Ge, A. Daphalapurkar, M. Shmipi, K. Darpun, K. Pelechrinis, P. K. Chrysanthis, and D. Zeinalipour-Yazti, “Data-driven Serendipity Navigation in Urban Places,” in IEEE 37th International Conference on Distributed Computing Systems (ICDCS 2017), Atlanta, GA, USA, 2017.
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@inproceedings{icdcs17-serendipity-demo, TITLE = {Data-driven Serendipity Navigation in Urban Places}, AUTHOR = {Ge, Xiaoyi and Daphalapurkar, Ameya and Shmipi, Manali and Darpun, Kohli and Pelechrinis, Konstantinos and Chrysanthis, Panos K. and Zeinalipour-Yazti, Demetrios}, LANGUAGE = {eng}, ISBN = {978-1-5386-1792-2}, DOI = {10.1109/ICDCS.2017.286}, PUBLISHER = {IEEE}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {IEEE 37th International Conference on Distributed Computing Systems (ICDCS 2017)}, EDITOR = {Lee, Kisung and Liu, Ling}, PAGES = {2501--2504}, ADDRESS = {Atlanta, GA, USA}, }
Endnote
%0 Conference Proceedings %A Ge, Xiaoyi %A Daphalapurkar, Ameya %A Shmipi, Manali %A Darpun, Kohli %A Pelechrinis, Konstantinos %A Chrysanthis, Panos K. %A Zeinalipour-Yazti, Demetrios %+ External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Data-driven Serendipity Navigation in Urban Places : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-082B-7 %R 10.1109/ICDCS.2017.286 %D 2017 %B 37th IEEE International Conference on Distributed Computing Systems %Z date of event: 2017-06-05 - 2017-06-08 %C Atlanta, GA, USA %B IEEE 37th International Conference on Distributed Computing Systems %E Lee, Kisung; Liu, Ling %P 2501 - 2504 %I IEEE %@ 978-1-5386-1792-2
[208]
B. Goldsmith, M. Boley, J. Vreeken, M. Scheffler, and L. Ghiringhelli,, “Uncovering Structure-property Relationships of Materials by Subgroup Discovery,” New Journal of Physics, vol. 19, no. 1, 2017.
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@article{goldsmith:17:gold, TITLE = {Uncovering Structure-property Relationships of Materials by Subgroup Discovery}, AUTHOR = {Goldsmith, Brian and Boley, Mario and Vreeken, Jilles and Scheffler, Matthias and Ghiringhelli,, Luca}, LANGUAGE = {eng}, ISSN = {1367-2630}, DOI = {10.1088/1367-2630/aa57c2}, PUBLISHER = {IOP Publishing}, ADDRESS = {Bristol}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, JOURNAL = {New Journal of Physics}, VOLUME = {19}, NUMBER = {1}, EID = {013031}, }
Endnote
%0 Journal Article %A Goldsmith, Brian %A Boley, Mario %A Vreeken, Jilles %A Scheffler, Matthias %A Ghiringhelli,, Luca %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T Uncovering Structure-property Relationships of Materials by Subgroup Discovery : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-4BF5-4 %R 10.1088/1367-2630/aa57c2 %7 2017 %D 2017 %J New Journal of Physics %O New J. Phys. %V 19 %N 1 %Z sequence number: 013031 %I IOP Publishing %C Bristol %@ false %U http://iopscience.iop.org/article/10.1088/1367-2630/aa57c2
[209]
A. Grycner, “Constructing Lexicons of Relational Phrases,” Universität des Saarlandes, Saarbrücken, 2017.
Abstract
Knowledge Bases are one of the key components of Natural Language Understanding systems. For example, DBpedia, YAGO, and Wikidata capture and organize knowledge about named entities and relations between them, which is often crucial for tasks like Question Answering and Named Entity Disambiguation. While Knowledge Bases have good coverage of prominent entities, they are often limited with respect to relations. The goal of this thesis is to bridge this gap and automatically create lexicons of textual representations of relations, namely relational phrases. The lexicons should contain information about paraphrases, hierarchy, as well as semantic types of arguments of relational phrases. The thesis makes three main contributions. The first contribution addresses disambiguating relational phrases by aligning them with the WordNet dictionary. Moreover, the alignment allows imposing the WordNet hierarchy on the relational phrases. The second contribution proposes a method for graph construction of relations using Probabilistic Graphical Models. In addition, we apply this model to relation paraphrasing. The third contribution presents a method for constructing a lexicon of relational paraphrases with fine-grained semantic typing of arguments. This method is based on information from a multilingual parallel corpus.
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BibTeX
@phdthesis{Grynerphd17, TITLE = {Constructing Lexicons of Relational Phrases}, AUTHOR = {Grycner, Adam}, LANGUAGE = {eng}, URL = {urn:nbn:de:bsz:291-scidok-69101}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, ABSTRACT = {Knowledge Bases are one of the key components of Natural Language Understanding systems. For example, DBpedia, YAGO, and Wikidata capture and organize knowledge about named entities and relations between them, which is often crucial for tasks like Question Answering and Named Entity Disambiguation. While Knowledge Bases have good coverage of prominent entities, they are often limited with respect to relations. The goal of this thesis is to bridge this gap and automatically create lexicons of textual representations of relations, namely relational phrases. The lexicons should contain information about paraphrases, hierarchy, as well as semantic types of arguments of relational phrases. The thesis makes three main contributions. The first contribution addresses disambiguating relational phrases by aligning them with the WordNet dictionary. Moreover, the alignment allows imposing the WordNet hierarchy on the relational phrases. The second contribution proposes a method for graph construction of relations using Probabilistic Graphical Models. In addition, we apply this model to relation paraphrasing. The third contribution presents a method for constructing a lexicon of relational paraphrases with fine-grained semantic typing of arguments. This method is based on information from a multilingual parallel corpus.}, }
Endnote
%0 Thesis %A Grycner, Adam %Y Weikum, Gerhard %A referee: Klakow, Dietrich %A referee: Ponzetto, Simone Paolo %+ Databases and Information Systems, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T Constructing Lexicons of Relational Phrases : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-933B-1 %U urn:nbn:de:bsz:291-scidok-69101 %I Universit&#228;t des Saarlandes %C Saarbr&#252;cken %D 2017 %P 125 p. %V phd %9 phd %X Knowledge Bases are one of the key components of Natural Language Understanding systems. For example, DBpedia, YAGO, and Wikidata capture and organize knowledge about named entities and relations between them, which is often crucial for tasks like Question Answering and Named Entity Disambiguation. While Knowledge Bases have good coverage of prominent entities, they are often limited with respect to relations. The goal of this thesis is to bridge this gap and automatically create lexicons of textual representations of relations, namely relational phrases. The lexicons should contain information about paraphrases, hierarchy, as well as semantic types of arguments of relational phrases. The thesis makes three main contributions. The first contribution addresses disambiguating relational phrases by aligning them with the WordNet dictionary. Moreover, the alignment allows imposing the WordNet hierarchy on the relational phrases. The second contribution proposes a method for graph construction of relations using Probabilistic Graphical Models. In addition, we apply this model to relation paraphrasing. The third contribution presents a method for constructing a lexicon of relational paraphrases with fine-grained semantic typing of arguments. This method is based on information from a multilingual parallel corpus. %U http://scidok.sulb.uni-saarland.de/volltexte/2017/6910/http://scidok.sulb.uni-saarland.de/doku/lic_ohne_pod.php?la=de
[210]
A. Guimarães, L. Wang, and G. Weikum, “Us and Them: Adversarial Politics on Twitter,” in 17th IEEE International Conference on Data Mining Workshops (ICDMW 2017 ), New Orleans, LA, USA, 2017.
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BibTeX
@inproceedings{Guimaraes_ICDMW2017, TITLE = {Us and Them: {A}dversarial Politics on {Twitter}}, AUTHOR = {Guimar{\~a}es, Anna and Wang, Liqiang and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-5386-1480-8}, DOI = {10.1109/ICDMW.2017.119}, PUBLISHER = {IEEE}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {17th IEEE International Conference on Data Mining Workshops (ICDMW 2017 )}, EDITOR = {Gottumukkala, Raju and Ning, Xia and Dong, Guozhu and Raghavan, Vijav and Aluru, Srinivas and Karypis, George and Miele, Lucio and Wu, Xindong}, PAGES = {872--877}, ADDRESS = {New Orleans, LA, USA}, }
Endnote
%0 Conference Proceedings %A Guimar&#227;es, Anna %A Wang, Liqiang %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Us and Them: Adversarial Politics on Twitter : %G eng %U http://hdl.handle.net/21.11116/0000-0000-3B89-4 %R 10.1109/ICDMW.2017.119 %D 2017 %B 17th International Conference on Data Mining %Z date of event: 2017-11-18 - 2017-11-21 %C New Orleans, LA, USA %B 17th IEEE International Conference on Data Mining Workshops %E Gottumukkala, Raju; Ning, Xia; Dong, Guozhu; Raghavan, Vijav; Aluru, Srinivas; Karypis, George; Miele, Lucio; Wu, Xindong %P 872 - 877 %I IEEE %@ 978-1-5386-1480-8
[211]
D. Gupta, K. Berberich, J. Strötgen, and D. Zeinalipour-Yazti, “Generating Semantic Aspects for Queries,” Max-Planck-Institut für Informatik, Saarbrücken, MPI-I-2017-5-001, 2017.
Abstract
Ambiguous information needs expressed in a limited number of keywords often result in long-winded query sessions and many query reformulations. In this work, we tackle ambiguous queries by providing automatically gen- erated semantic aspects that can guide users to satisfying results regarding their information needs. To generate semantic aspects, we use semantic an- notations available in the documents and leverage models representing the semantic relationships between annotations of the same type. The aspects in turn provide us a foundation for representing text in a completely structured manner, thereby allowing for a semantically-motivated organization of search results. We evaluate our approach on a testbed of over 5,000 aspects on Web scale document collections amounting to more than 450 million documents, with temporal, geographic, and named entity annotations as example dimen- sions. Our experimental results show that our general approach is Web-scale ready and finds relevant aspects for highly ambiguous queries.
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@techreport{Guptareport2007, TITLE = {Generating Semantic Aspects for Queries}, AUTHOR = {Gupta, Dhruv and Berberich, Klaus and Str{\"o}tgen, Jannik and Zeinalipour-Yazti, Demetrios}, LANGUAGE = {eng}, ISSN = {0946-011X}, NUMBER = {MPI-I-2017-5-001}, INSTITUTION = {Max-Planck-Institut f{\"u}r Informatik}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Ambiguous information needs expressed in a limited number of keywords often result in long-winded query sessions and many query reformulations. In this work, we tackle ambiguous queries by providing automatically gen- erated semantic aspects that can guide users to satisfying results regarding their information needs. To generate semantic aspects, we use semantic an- notations available in the documents and leverage models representing the semantic relationships between annotations of the same type. The aspects in turn provide us a foundation for representing text in a completely structured manner, thereby allowing for a semantically-motivated organization of search results. We evaluate our approach on a testbed of over 5,000 aspects on Web scale document collections amounting to more than 450 million documents, with temporal, geographic, and named entity annotations as example dimen- sions. Our experimental results show that our general approach is Web-scale ready and finds relevant aspects for highly ambiguous queries.}, TYPE = {Research Report}, }
Endnote
%0 Report %A Gupta, Dhruv %A Berberich, Klaus %A Str&#246;tgen, Jannik %A Zeinalipour-Yazti, Demetrios %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Generating Semantic Aspects for Queries : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002E-07DD-0 %Y Max-Planck-Institut f&#252;r Informatik %C Saarbr&#252;cken %D 2017 %P 39 p. %X Ambiguous information needs expressed in a limited number of keywords often result in long-winded query sessions and many query reformulations. In this work, we tackle ambiguous queries by providing automatically gen- erated semantic aspects that can guide users to satisfying results regarding their information needs. To generate semantic aspects, we use semantic an- notations available in the documents and leverage models representing the semantic relationships between annotations of the same type. The aspects in turn provide us a foundation for representing text in a completely structured manner, thereby allowing for a semantically-motivated organization of search results. We evaluate our approach on a testbed of over 5,000 aspects on Web scale document collections amounting to more than 450 million documents, with temporal, geographic, and named entity annotations as example dimen- sions. Our experimental results show that our general approach is Web-scale ready and finds relevant aspects for highly ambiguous queries. %B Research Report %@ false
[212]
S. Gurajada, “Distributed Querying of Large Labeled Graphs,” Universität des Saarlandes, Saarbrücken, 2017.
Abstract
Graph is a vital abstract data type that has profound significance in several applications. Because of its versitality, graphs have been adapted into several different forms and one such adaption with many practical applications is the “Labeled Graph”, where vertices and edges are labeled. An enormous research effort has been invested in to the task of managing and querying graphs, yet a lot challenges are left unsolved. In this thesis, we advance the state-of-the-art for the following query models, and propose a distributed solution to process them in an efficient and scalable manner. • Set Reachability. We formalize and investigate a generalization of the basic notion of reachability, called set reachability. Set reachability deals with finding all reachable pairs for a given source and target sets. We present a non-iterative distributed solution that takes only a single round of communication for any set reachability query. This is achieved by precomputation, replication, and indexing of partial reachabilities among the boundary vertices. • Basic Graph Patterns (BGP). Supported by majority of query languages, BGP queries are a common mode of querying knowledge graphs, biological datasets, etc. We present a novel distributed architecture that relies on the concepts of asynchronous executions, join-ahead pruning, and a multi-threaded query processing framework to process BGP queries in an efficient and scalable manner. • Generalized Graph Patterns (GGP). These queries combine the semantics of pattern matching and navigational queries, and are popular in scenarios where the schema of an underlying graph is either unknown or partially known. We present a distributed solution with bimodal indexing layout that individually support efficient processing of BGP queries and navigational queries. Furthermore, we design a unified query optimizer and a processor to efficiently process GGP queries and also in a scalable manner. To this end, we propose a prototype distributed engine, coined “TriAD” (Triple Asynchronous and Distributed) that supports all the aforementioned query models. We also provide a detailed empirical evaluation of TriAD in comparison to several state-of-the-art systems over multiple real-world and synthetic datasets.
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@phdthesis{guraphd2017, TITLE = {Distributed Querying of Large Labeled Graphs}, AUTHOR = {Gurajada, Sairam}, LANGUAGE = {eng}, URL = {urn:nbn:de:bsz:291-scidok-67738}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, ABSTRACT = {Graph is a vital abstract data type that has profound significance in several applications. Because of its versitality, graphs have been adapted into several different forms and one such adaption with many practical applications is the {\textquotedblleft}Labeled Graph{\textquotedblright}, where vertices and edges are labeled. An enormous research effort has been invested in to the task of managing and querying graphs, yet a lot challenges are left unsolved. In this thesis, we advance the state-of-the-art for the following query models, and propose a distributed solution to process them in an efficient and scalable manner. \mbox{$\bullet$} Set Reachability. We formalize and investigate a generalization of the basic notion of reachability, called set reachability. Set reachability deals with finding all reachable pairs for a given source and target sets. We present a non-iterative distributed solution that takes only a single round of communication for any set reachability query. This is achieved by precomputation, replication, and indexing of partial reachabilities among the boundary vertices. \mbox{$\bullet$} Basic Graph Patterns (BGP). Supported by majority of query languages, BGP queries are a common mode of querying knowledge graphs, biological datasets, etc. We present a novel distributed architecture that relies on the concepts of asynchronous executions, join-ahead pruning, and a multi-threaded query processing framework to process BGP queries in an efficient and scalable manner. \mbox{$\bullet$} Generalized Graph Patterns (GGP). These queries combine the semantics of pattern matching and navigational queries, and are popular in scenarios where the schema of an underlying graph is either unknown or partially known. We present a distributed solution with bimodal indexing layout that individually support efficient processing of BGP queries and navigational queries. Furthermore, we design a unified query optimizer and a processor to efficiently process GGP queries and also in a scalable manner. To this end, we propose a prototype distributed engine, coined {\textquotedblleft}TriAD{\textquotedblright} (Triple Asynchronous and Distributed) that supports all the aforementioned query models. We also provide a detailed empirical evaluation of TriAD in comparison to several state-of-the-art systems over multiple real-world and synthetic datasets.}, }
Endnote
%0 Thesis %A Gurajada, Sairam %Y Theobald, Martin %A referee: Weikum, Gerhard %A referee: &#214;zsu, M. Tamer %A referee: Michel, Sebastian %+ Databases and Information Systems, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Distributed Querying of Large Labeled Graphs : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-8202-E %U urn:nbn:de:bsz:291-scidok-67738 %I Universit&#228;t des Saarlandes %C Saarbr&#252;cken %D 2017 %P x, 167 p. %V phd %9 phd %X Graph is a vital abstract data type that has profound significance in several applications. Because of its versitality, graphs have been adapted into several different forms and one such adaption with many practical applications is the &#8220;Labeled Graph&#8221;, where vertices and edges are labeled. An enormous research effort has been invested in to the task of managing and querying graphs, yet a lot challenges are left unsolved. In this thesis, we advance the state-of-the-art for the following query models, and propose a distributed solution to process them in an efficient and scalable manner. &#8226; Set Reachability. We formalize and investigate a generalization of the basic notion of reachability, called set reachability. Set reachability deals with finding all reachable pairs for a given source and target sets. We present a non-iterative distributed solution that takes only a single round of communication for any set reachability query. This is achieved by precomputation, replication, and indexing of partial reachabilities among the boundary vertices. &#8226; Basic Graph Patterns (BGP). Supported by majority of query languages, BGP queries are a common mode of querying knowledge graphs, biological datasets, etc. We present a novel distributed architecture that relies on the concepts of asynchronous executions, join-ahead pruning, and a multi-threaded query processing framework to process BGP queries in an efficient and scalable manner. &#8226; Generalized Graph Patterns (GGP). These queries combine the semantics of pattern matching and navigational queries, and are popular in scenarios where the schema of an underlying graph is either unknown or partially known. We present a distributed solution with bimodal indexing layout that individually support efficient processing of BGP queries and navigational queries. Furthermore, we design a unified query optimizer and a processor to efficiently process GGP queries and also in a scalable manner. To this end, we propose a prototype distributed engine, coined &#8220;TriAD&#8221; (Triple Asynchronous and Distributed) that supports all the aforementioned query models. We also provide a detailed empirical evaluation of TriAD in comparison to several state-of-the-art systems over multiple real-world and synthetic datasets. %U http://scidok.sulb.uni-saarland.de/volltexte/2017/6773/http://scidok.sulb.uni-saarland.de/doku/lic_ohne_pod.php?la=de
[213]
K. Hui and K. Berberich, “Transitivity, Time Consumption, and Quality of Preference Judgments in Crowdsourcing,” in Advances in Information Retrieval (ECIR 2017), Aberdeen, UK, 2017.
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@inproceedings{hui2017full, TITLE = {Transitivity, Time Consumption, and Quality of Preference Judgments in Crowdsourcing}, AUTHOR = {Hui, Kai and Berberich, Klaus}, LANGUAGE = {eng}, ISBN = {978-3-319-56607-8}, DOI = {10.1007/978-3-319-56608-5_19}, PUBLISHER = {Springer}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {Advances in Information Retrieval (ECIR 2017)}, EDITOR = {Jose, Joemon M. and Hauff, Claudia and Altingovde, Ismail Sengor and Song, Dawei and Albakour, Dyaa and Watt, Stuart and Tait, John}, PAGES = {239--251}, SERIES = {Lecture Notes in Computer Science}, VOLUME = {10193}, ADDRESS = {Aberdeen, UK}, }
Endnote
%0 Conference Proceedings %A Hui, Kai %A Berberich, Klaus %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Transitivity, Time Consumption, and Quality of Preference Judgments in Crowdsourcing : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-1F75-5 %R 10.1007/978-3-319-56608-5_19 %D 2017 %B 39th European Conference on Information Retrieval %Z date of event: 2016-04-09 - 2017-04-13 %C Aberdeen, UK %B Advances in Information Retrieval %E Jose, Joemon M.; Hauff, Claudia; Altingovde, Ismail Sengor; Song, Dawei; Albakour, Dyaa; Watt, Stuart; Tait, John %P 239 - 251 %I Springer %@ 978-3-319-56607-8 %B Lecture Notes in Computer Science %N 10193
[214]
K. Hui, A. Yates, K. Berberich, and G. de Melo, “RE-PACRR: A Context and Density-Aware Neural Information Retrieval Model,” 2017. [Online]. Available: http://arxiv.org/abs/1706.10192. (arXiv: 1706.10192)
Abstract
Ad-hoc retrieval models can benefit from considering different patterns in the interactions between a query and a document, effectively assessing the relevance of a document for a given user query. Factors to be considered in this interaction include (i) the matching of unigrams and ngrams, (ii) the proximity of the matched query terms, (iii) their position in the document, and (iv) how the different relevance signals are combined over different query terms. While previous work has successfully modeled some of these factors, not all aspects have been fully explored. In this work, we close this gap by proposing different neural components and incorporating them into a single architecture, leading to a novel neural IR model called RE-PACRR. Extensive comparisons with established models on TREC Web Track data confirm that the proposed model yields promising search results.
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@online{HuiarXiv2017b, TITLE = {{RE-PACRR}: {A} Context and Density-Aware Neural Information Retrieval Model}, AUTHOR = {Hui, Kai and Yates, Andrew and Berberich, Klaus and de Melo, Gerard}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1706.10192}, EPRINT = {1706.10192}, EPRINTTYPE = {arXiv}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Ad-hoc retrieval models can benefit from considering different patterns in the interactions between a query and a document, effectively assessing the relevance of a document for a given user query. Factors to be considered in this interaction include (i) the matching of unigrams and ngrams, (ii) the proximity of the matched query terms, (iii) their position in the document, and (iv) how the different relevance signals are combined over different query terms. While previous work has successfully modeled some of these factors, not all aspects have been fully explored. In this work, we close this gap by proposing different neural components and incorporating them into a single architecture, leading to a novel neural IR model called RE-PACRR. Extensive comparisons with established models on TREC Web Track data confirm that the proposed model yields promising search results.}, }
Endnote
%0 Report %A Hui, Kai %A Yates, Andrew %A Berberich, Klaus %A de Melo, Gerard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T RE-PACRR: A Context and Density-Aware Neural Information Retrieval Model : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002E-064D-D %U http://arxiv.org/abs/1706.10192 %D 2017 %X Ad-hoc retrieval models can benefit from considering different patterns in the interactions between a query and a document, effectively assessing the relevance of a document for a given user query. Factors to be considered in this interaction include (i) the matching of unigrams and ngrams, (ii) the proximity of the matched query terms, (iii) their position in the document, and (iv) how the different relevance signals are combined over different query terms. While previous work has successfully modeled some of these factors, not all aspects have been fully explored. In this work, we close this gap by proposing different neural components and incorporating them into a single architecture, leading to a novel neural IR model called RE-PACRR. Extensive comparisons with established models on TREC Web Track data confirm that the proposed model yields promising search results. %K Computer Science, Information Retrieval, cs.IR,Computer Science, Computation and Language, cs.CL
[215]
K. Hui, A. Yates, K. Berberich, and G. de Melo, “Position-Aware Representations for Relevance Matching in Neural Information Retrieval,” in WWW’17 Companion, Perth, Australia, 2017.
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@inproceedings{HuiWWW2017, TITLE = {Position-Aware Representations for Relevance Matching in Neural Information Retrieval}, AUTHOR = {Hui, Kai and Yates, Andrew and Berberich, Klaus and de Melo, Gerard}, LANGUAGE = {eng}, ISBN = {978-1-4503-4914-7}, DOI = {10.1145/3041021.3054258}, PUBLISHER = {ACM}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {WWW'17 Companion}, PAGES = {799--800}, ADDRESS = {Perth, Australia}, }
Endnote
%0 Conference Proceedings %A Hui, Kai %A Yates, Andrew %A Berberich, Klaus %A de Melo, Gerard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Position-Aware Representations for Relevance Matching in Neural Information Retrieval : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-90A4-B %R 10.1145/3041021.3054258 %D 2017 %B 26th International Conference on World Wide Web %Z date of event: 2017-04-03 - 2017-04-07 %C Perth, Australia %B WWW'17 Companion %P 799 - 800 %I ACM %@ 978-1-4503-4914-7
[216]
K. Hui, A. Yates, K. Berberich, and G. de Melo, “PACRR: A Position-Aware Neural IR Model for Relevance Matching,” in The Conference on Empirical Methods in Natural Language Processing (EMNLP 2017), Copenhagen, Denmark, 2017.
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@inproceedings{HuiENMLP2017, TITLE = {{PACRR}: A Position-Aware Neural {IR} Model for Relevance Matching}, AUTHOR = {Hui, Kai and Yates, Andrew and Berberich, Klaus and de Melo, Gerard}, LANGUAGE = {eng}, ISBN = {978-1-945626-83-8}, URL = {https://aclanthology.info/pdf/D/D17/D17-1111.pdf}, PUBLISHER = {ACL}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {The Conference on Empirical Methods in Natural Language Processing (EMNLP 2017)}, PAGES = {1060--1069}, ADDRESS = {Copenhagen, Denmark}, }
Endnote
%0 Conference Proceedings %A Hui, Kai %A Yates, Andrew %A Berberich, Klaus %A de Melo, Gerard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T PACRR: A Position-Aware Neural IR Model for Relevance Matching : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002E-063F-D %U https://aclanthology.info/pdf/D/D17/D17-1111.pdf %D 2017 %B Conference on Empirical Methods in Natural Language Processing %Z date of event: 2017-09-09 - 2017-09-11 %C Copenhagen, Denmark %B The Conference on Empirical Methods in Natural Language Processing %P 1060 - 1069 %I ACL %@ 978-1-945626-83-8 %U https://aclanthology.info/pdf/D/D17/D17-1111.pdf
[217]
K. Hui, A. Yates, K. Berberich, and G. de Melo, “PACRR: A Position-Aware Neural IR Model for Relevance Matching,” 2017. [Online]. Available: http://arxiv.org/abs/1704.03940. (arXiv: 1704.03940)
Abstract
In order to adopt deep learning for information retrieval, models are needed that can capture all relevant information required to assess the relevance of a document to a given user query. While previous works have successfully captured unigram term matches, how to fully employ position-dependent information such as proximity and term dependencies has been insufficiently explored. In this work, we propose a novel neural IR model named PACRR (Position-Aware Convolutional-Recurrent Relevance), aiming at better modeling position-dependent interactions between a query and a document via convolutional layers as well as recurrent layers. Extensive experiments on six years' TREC Web Track data confirm that the proposed model yields better results under different benchmarks.
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@online{DBLP:journals/corr/HuiYBM17, TITLE = {{PACRR}: A Position-Aware Neural {IR} Model for Relevance Matching}, AUTHOR = {Hui, Kai and Yates, Andrew and Berberich, Klaus and de Melo, Gerard}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1704.03940}, EPRINT = {1704.03940}, EPRINTTYPE = {arXiv}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, ABSTRACT = {In order to adopt deep learning for information retrieval, models are needed that can capture all relevant information required to assess the relevance of a document to a given user query. While previous works have successfully captured unigram term matches, how to fully employ position-dependent information such as proximity and term dependencies has been insufficiently explored. In this work, we propose a novel neural IR model named PACRR (Position-Aware Convolutional-Recurrent Relevance), aiming at better modeling position-dependent interactions between a query and a document via convolutional layers as well as recurrent layers. Extensive experiments on six years' TREC Web Track data confirm that the proposed model yields better results under different benchmarks.}, }
Endnote
%0 Report %A Hui, Kai %A Yates, Andrew %A Berberich, Klaus %A de Melo, Gerard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T PACRR: A Position-Aware Neural IR Model for Relevance Matching : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-90A8-3 %U http://arxiv.org/abs/1704.03940 %D 2017 %X In order to adopt deep learning for information retrieval, models are needed that can capture all relevant information required to assess the relevance of a document to a given user query. While previous works have successfully captured unigram term matches, how to fully employ position-dependent information such as proximity and term dependencies has been insufficiently explored. In this work, we propose a novel neural IR model named PACRR (Position-Aware Convolutional-Recurrent Relevance), aiming at better modeling position-dependent interactions between a query and a document via convolutional layers as well as recurrent layers. Extensive experiments on six years' TREC Web Track data confirm that the proposed model yields better results under different benchmarks. %K Computer Science, Information Retrieval, cs.IR,Computer Science, Computation and Language, cs.CL
[218]
K. Hui and K. Berberich, “Merge-Tie-Judge: Low-Cost Preference Judgments with Ties,” in ICTIR’17, 7th International Conference on the Theory of Information Retrieval, Amsterdam, The Netherlands, 2017.
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@inproceedings{HuiICTIR2017b, TITLE = {{Merge-Tie-Judge}: Low-Cost Preference Judgments with Ties}, AUTHOR = {Hui, Kai and Berberich, Klaus}, LANGUAGE = {eng}, ISBN = {978-1-4503-4490-6}, DOI = {10.1145/3121050.3121095}, PUBLISHER = {ACM}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {ICTIR'17, 7th International Conference on the Theory of Information Retrieval}, PAGES = {277--280}, ADDRESS = {Amsterdam, The Netherlands}, }
Endnote
%0 Conference Proceedings %A Hui, Kai %A Berberich, Klaus %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Merge-Tie-Judge: Low-Cost Preference Judgments with Ties : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002E-064B-2 %R 10.1145/3121050.3121095 %D 2017 %B 7th International Conference on the Theory of Information Retrieval %Z date of event: 2017-10-01 - 2017-10-04 %C Amsterdam, The Netherlands %B ICTIR'17 %P 277 - 280 %I ACM %@ 978-1-4503-4490-6
[219]
K. Hui and K. Berberich, “Low-Cost Preference Judgment via Ties,” in Advances in Information Retrieval (ECIR 2017), Aberdeen, UK, 2017.
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@inproceedings{hui2017short, TITLE = {Low-Cost Preference Judgment via Ties}, AUTHOR = {Hui, Kai and Berberich, Klaus}, LANGUAGE = {eng}, ISBN = {978-3-319-56607-8}, DOI = {10.1007/978-3-319-56608-5_58}, PUBLISHER = {Springer}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {Advances in Information Retrieval (ECIR 2017)}, EDITOR = {Jose, Joemon M. and Hauff, Claudia and Altingovde, Ismail Sengor and Song, Dawei and Albakour, Dyaa and Watt, Stuart and Tait, John}, PAGES = {626--632}, SERIES = {Lecture Notes in Computer Science}, VOLUME = {10193}, ADDRESS = {Aberdeen, UK}, }
Endnote
%0 Conference Proceedings %A Hui, Kai %A Berberich, Klaus %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Low-Cost Preference Judgment via Ties : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-1F7B-A %R 10.1007/978-3-319-56608-5_58 %D 2017 %B 39th European Conference on Information Retrieval %Z date of event: 2017-04-09 - 2017-04-13 %C Aberdeen, UK %B Advances in Information Retrieval %E Jose, Joemon M.; Hauff, Claudia; Altingovde, Ismail Sengor; Song, Dawei; Albakour, Dyaa; Watt, Stuart; Tait, John %P 626 - 632 %I Springer %@ 978-3-319-56607-8 %B Lecture Notes in Computer Science %N 10193
[220]
K. Hui, K. Berberich, and I. Mele, “Dealing with Incomplete Judgments in Cascade Measures,” in ICTIR’17, 7th International Conference on the Theory of Information Retrieval, Amsterdam, The Netherlands, 2017.
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@inproceedings{HuiICTIR2017, TITLE = {Dealing with Incomplete Judgments in Cascade Measures}, AUTHOR = {Hui, Kai and Berberich, Klaus and Mele, Ida}, LANGUAGE = {eng}, ISBN = {978-1-4503-4490-6}, DOI = {10.1145/3121050.3121064}, PUBLISHER = {ACM}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {ICTIR'17, 7th International Conference on the Theory of Information Retrieval}, PAGES = {83--90}, ADDRESS = {Amsterdam, The Netherlands}, }
Endnote
%0 Conference Proceedings %A Hui, Kai %A Berberich, Klaus %A Mele, Ida %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Dealing with Incomplete Judgments in Cascade Measures : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002E-0649-6 %R 10.1145/3121050.3121064 %D 2017 %B 7th International Conference on the Theory of Information Retrieval %Z date of event: 2017-10-01 - 2017-10-04 %C Amsterdam, The Netherlands %B ICTIR'17 %P 83 - 90 %I ACM %@ 978-1-4503-4490-6
[221]
K. Hui, “Automatic Methods for Low-Cost Evaluation and Position-Aware Models for Neural Information Retrieval,” Universität des Saarlandes, Saarbrücken, 2017.
Abstract
An information retrieval (IR) system assists people in consuming huge amount of data, where the evaluation and the construction of such systems are important. However, there exist two difficulties: the overwhelmingly large number of query-document pairs to judge, making IR evaluation a manually laborious task; and the complicated patterns to model due to the non-symmetric, heterogeneous relationships between a query-document pair, where different interaction patterns such as term dependency and proximity have been demonstrated to be useful, yet are non-trivial for a single IR model to encode. In this thesis we attempt to address both difficulties from the perspectives of IR evaluation and of the retrieval model respectively, by reducing the manual cost with automatic methods, by investigating the usage of crowdsourcing in collecting preference judgments, and by proposing novel neural retrieval models. In particular, to address the large number of query-document pairs in IR evaluation, a low-cost selective labeling method is proposed to pick out a small subset of representative documents for manual judgments in favor of the follow-up prediction for the remaining query-document pairs; furthermore, a language-model based cascade measure framework is developed to evaluate the novelty and diversity, utilizing the content of the labeled documents to mitigate incomplete labels. In addition, we also attempt to make the preference judgments practically usable by empirically investigating different properties of the judgments when collected via crowdsourcing; and by proposing a novel judgment mechanism, making a compromise between the judgment quality and the number of judgments. Finally, to model different complicated patterns in a single retrieval model, inspired by the recent advances in deep learning, we develop novel neural IR models to incorporate different patterns like term dependency, query proximity, density of relevance, and query coverage in a single model. We demonstrate their superior performances through evaluations on different datasets.
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@phdthesis{HUiphd2017, TITLE = {Automatic Methods for Low-Cost Evaluation and Position-Aware Models for Neural Information Retrieval}, AUTHOR = {Hui, Kai}, LANGUAGE = {eng}, URL = {urn:nbn:de:bsz:291-scidok-ds-269423}, DOI = {10.22028/D291-26942}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, ABSTRACT = {An information retrieval (IR) system assists people in consuming huge amount of data, where the evaluation and the construction of such systems are important. However, there exist two difficulties: the overwhelmingly large number of query-document pairs to judge, making IR evaluation a manually laborious task; and the complicated patterns to model due to the non-symmetric, heterogeneous relationships between a query-document pair, where different interaction patterns such as term dependency and proximity have been demonstrated to be useful, yet are non-trivial for a single IR model to encode. In this thesis we attempt to address both difficulties from the perspectives of IR evaluation and of the retrieval model respectively, by reducing the manual cost with automatic methods, by investigating the usage of crowdsourcing in collecting preference judgments, and by proposing novel neural retrieval models. In particular, to address the large number of query-document pairs in IR evaluation, a low-cost selective labeling method is proposed to pick out a small subset of representative documents for manual judgments in favor of the follow-up prediction for the remaining query-document pairs; furthermore, a language-model based cascade measure framework is developed to evaluate the novelty and diversity, utilizing the content of the labeled documents to mitigate incomplete labels. In addition, we also attempt to make the preference judgments practically usable by empirically investigating different properties of the judgments when collected via crowdsourcing; and by proposing a novel judgment mechanism, making a compromise between the judgment quality and the number of judgments. Finally, to model different complicated patterns in a single retrieval model, inspired by the recent advances in deep learning, we develop novel neural IR models to incorporate different patterns like term dependency, query proximity, density of relevance, and query coverage in a single model. We demonstrate their superior performances through evaluations on different datasets.}, }
Endnote
%0 Thesis %A Hui, Kai %Y Berberich, Klaus %A referee: Weikum, Gerhard %A referee: Dietz, Laura %+ Databases and Information Systems, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Automatic Methods for Low-Cost Evaluation and Position-Aware Models for Neural Information Retrieval : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002E-8921-E %U urn:nbn:de:bsz:291-scidok-ds-269423 %R 10.22028/D291-26942 %I Universit&#228;t des Saarlandes %C Saarbr&#252;cken %D 2017 %P xiv, 130 p. %V phd %9 phd %X An information retrieval (IR) system assists people in consuming huge amount of data, where the evaluation and the construction of such systems are important. However, there exist two difficulties: the overwhelmingly large number of query-document pairs to judge, making IR evaluation a manually laborious task; and the complicated patterns to model due to the non-symmetric, heterogeneous relationships between a query-document pair, where different interaction patterns such as term dependency and proximity have been demonstrated to be useful, yet are non-trivial for a single IR model to encode. In this thesis we attempt to address both difficulties from the perspectives of IR evaluation and of the retrieval model respectively, by reducing the manual cost with automatic methods, by investigating the usage of crowdsourcing in collecting preference judgments, and by proposing novel neural retrieval models. In particular, to address the large number of query-document pairs in IR evaluation, a low-cost selective labeling method is proposed to pick out a small subset of representative documents for manual judgments in favor of the follow-up prediction for the remaining query-document pairs; furthermore, a language-model based cascade measure framework is developed to evaluate the novelty and diversity, utilizing the content of the labeled documents to mitigate incomplete labels. In addition, we also attempt to make the preference judgments practically usable by empirically investigating different properties of the judgments when collected via crowdsourcing; and by proposing a novel judgment mechanism, making a compromise between the judgment quality and the number of judgments. Finally, to model different complicated patterns in a single retrieval model, inspired by the recent advances in deep learning, we develop novel neural IR models to incorporate different patterns like term dependency, query proximity, density of relevance, and query coverage in a single model. We demonstrate their superior performances through evaluations on different datasets. %U https://publikationen.sulb.uni-saarland.de/handle/20.500.11880/26894
[222]
R. Jäschke, J. Strötgen, E. Krotova, and F. Fischer, “„Der Helmut Kohl unter den Brotaufstrichen“ - Zur Extraktion vossianischer Antonomasien aus großen Zeitungskorpora,” in DHd 2017, 4. Tagung des Verbands Digital Humanities im deutschsprachigen Raum e.V., Bern, Switzerland, 2017, pp. 120–124.
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@inproceedings{JaeschkeEtAl2017_DHD, TITLE = {{{``Der Helmut Kohl unter den Brotaufstrichen'' -- Zur Extraktion vossianischer Antonomasien aus gro{\ss}en Zeitungskorpora}}}, AUTHOR = {J{\"a}schke, Robert and Str{\"o}tgen, Jannik and Krotova, Elena and Fischer, Frank}, LANGUAGE = {deu}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {DHd 2017, 4. Tagung des Verbands Digital Humanities im deutschsprachigen Raum e.V.}, PAGES = {120--124}, ADDRESS = {Bern, Switzerland}, }
Endnote
%0 Conference Proceedings %A J&#228;schke, Robert %A Str&#246;tgen, Jannik %A Krotova, Elena %A Fischer, Frank %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T &#8222;Der Helmut Kohl unter den Brotaufstrichen&#8220; - Zur Extraktion vossianischer Antonomasien aus gro&#223;en Zeitungskorpora : %G deu %U http://hdl.handle.net/11858/00-001M-0000-002C-4E05-A %D 2017 %B 4. Tagung des Verbands Digital Humanities im deutschsprachigen Raum e.V. %Z date of event: 2017-02-13 - 2017-02-18 %C Bern, Switzerland %B DHd 2017 %P 120 - 124
[223]
H. Jhamtani, R. Saha Roy, N. Chhaya, and E. Nyberg, “Leveraging Site Search Logs to Identify Missing Content on Enterprise Webpages,” in Advances in Information Retrieval (ECIR 2017), Aberdeen, UK, 2017.
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@inproceedings{JhamtaniECIR2017, TITLE = {Leveraging Site Search Logs to Identify Missing Content on Enterprise Webpages}, AUTHOR = {Jhamtani, Harsh and Saha Roy, Rishiraj and Chhaya, Niyati and Nyberg, Eric}, LANGUAGE = {eng}, ISBN = {978-3-319-56607-8}, DOI = {10.1007/978-3-319-56608-5_41}, PUBLISHER = {Springer}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {Advances in Information Retrieval (ECIR 2017)}, EDITOR = {Jose, Joemon M. and Hauff, Claudia and Altingovde, Ismail Sengor and Song, Dawei and Albakour, Dyaa and Watt, Stuart and Tait, John}, PAGES = {506--512}, SERIES = {Lecture Notes in Computer Science}, VOLUME = {10193}, ADDRESS = {Aberdeen, UK}, }
Endnote
%0 Conference Proceedings %A Jhamtani, Harsh %A Saha Roy, Rishiraj %A Chhaya, Niyati %A Nyberg, Eric %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T Leveraging Site Search Logs to Identify Missing Content on Enterprise Webpages : %G eng %U http://hdl.handle.net/21.11116/0000-0000-DB33-0 %R 10.1007/978-3-319-56608-5_41 %D 2017 %B 39th European Conference on Information Retrieval %Z date of event: 2017-04-09 - 2017-04-13 %C Aberdeen, UK %B Advances in Information Retrieval %E Jose, Joemon M.; Hauff, Claudia; Altingovde, Ismail Sengor; Song, Dawei; Albakour, Dyaa; Watt, Stuart; Tait, John %P 506 - 512 %I Springer %@ 978-3-319-56607-8 %B Lecture Notes in Computer Science %N 10193
[224]
J. Kalofolias, E. Galbrun, and P. Miettinen, “From Sets of Good Redescriptions to Good Sets of Redescriptions,” in 16th IEEE International Conference on Data Mining (ICDM 2016), Barcelona, Spain, 2017.
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@inproceedings{kalofolias16from, TITLE = {From Sets of Good Redescriptions to Good Sets of Redescriptions}, AUTHOR = {Kalofolias, Janis and Galbrun, Esther and Miettinen, Pauli}, LANGUAGE = {eng}, ISBN = {978-1-5090-5473-2}, DOI = {10.1109/ICDM.2016.0032}, PUBLISHER = {IEEE}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {16th IEEE International Conference on Data Mining (ICDM 2016)}, PAGES = {211--220}, ADDRESS = {Barcelona, Spain}, }
Endnote
%0 Conference Proceedings %A Kalofolias, Janis %A Galbrun, Esther %A Miettinen, Pauli %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T From Sets of Good Redescriptions to Good Sets of Redescriptions : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-224D-A %R 10.1109/ICDM.2016.0032 %D 2017 %8 02.02.2017 %B 16th International Conference on Data Mining %Z date of event: 2016-12-12 - 2016-12-15 %C Barcelona, Spain %B 16th IEEE International Conference on Data Mining %P 211 - 220 %I IEEE %@ 978-1-5090-5473-2
[225]
J. Kalofolias, M. Boley, and J. Vreeken, “Efficiently Discovering Locally Exceptional Yet Globally Representative Subgroups,” in 17th IEEE International Conference on Data Mining (ICDM 2017), New Orleans, LA, USA, 2017.
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@inproceedings{KalofoliasICDM2017, TITLE = {Efficiently Discovering Locally Exceptional Yet Globally Representative Subgroups}, AUTHOR = {Kalofolias, Janis and Boley, Mario and Vreeken, Jilles}, LANGUAGE = {eng}, ISBN = {978-1-5386-3835-4}, DOI = {10.1109/ICDM.2017.29}, PUBLISHER = {IEEE}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {17th IEEE International Conference on Data Mining (ICDM 2017)}, PAGES = {197--206}, ADDRESS = {New Orleans, LA, USA}, }
Endnote
%0 Conference Proceedings %A Kalofolias, Janis %A Boley, Mario %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Efficiently Discovering Locally Exceptional Yet Globally Representative Subgroups : %G eng %U http://hdl.handle.net/21.11116/0000-0000-63C2-5 %R 10.1109/ICDM.2017.29 %D 2017 %B 17th IEEE International Conference on Data Mining %Z date of event: 2017-11-18 - 2017-11-21 %C New Orleans, LA, USA %B 17th IEEE International Conference on Data Mining %P 197 - 206 %I IEEE %@ 978-1-5386-3835-4
[226]
J. Kalofolias, M. Boley, and J. Vreeken, “Efficiently Discovering Locally Exceptional yet Globally Representative Subgroups,” 2017. [Online]. Available: http://arxiv.org/abs/1709.07941. (arXiv: 1709.07941)
Abstract
Subgroup discovery is a local pattern mining technique to find interpretable descriptions of sub-populations that stand out on a given target variable. That is, these sub-populations are exceptional with regard to the global distribution. In this paper we argue that in many applications, such as scientific discovery, subgroups are only useful if they are additionally representative of the global distribution with regard to a control variable. That is, when the distribution of this control variable is the same, or almost the same, as over the whole data. We formalise this objective function and give an efficient algorithm to compute its tight optimistic estimator for the case of a numeric target and a binary control variable. This enables us to use the branch-and-bound framework to efficiently discover the top-$k$ subgroups that are both exceptional as well as representative. Experimental evaluation on a wide range of datasets shows that with this algorithm we discover meaningful representative patterns and are up to orders of magnitude faster in terms of node evaluations as well as time.
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@online{Kalofolias_arXiv2017, TITLE = {Efficiently Discovering Locally Exceptional yet Globally Representative Subgroups}, AUTHOR = {Kalofolias, Janis and Boley, Mario and Vreeken, Jilles}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1709.07941}, EPRINT = {1709.07941}, EPRINTTYPE = {arXiv}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Subgroup discovery is a local pattern mining technique to find interpretable descriptions of sub-populations that stand out on a given target variable. That is, these sub-populations are exceptional with regard to the global distribution. In this paper we argue that in many applications, such as scientific discovery, subgroups are only useful if they are additionally representative of the global distribution with regard to a control variable. That is, when the distribution of this control variable is the same, or almost the same, as over the whole data. We formalise this objective function and give an efficient algorithm to compute its tight optimistic estimator for the case of a numeric target and a binary control variable. This enables us to use the branch-and-bound framework to efficiently discover the top-$k$ subgroups that are both exceptional as well as representative. Experimental evaluation on a wide range of datasets shows that with this algorithm we discover meaningful representative patterns and are up to orders of magnitude faster in terms of node evaluations as well as time.}, }
Endnote
%0 Report %A Kalofolias, Janis %A Boley, Mario %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Efficiently Discovering Locally Exceptional yet Globally Representative Subgroups : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002E-0685-D %U http://arxiv.org/abs/1709.07941 %D 2017 %X Subgroup discovery is a local pattern mining technique to find interpretable descriptions of sub-populations that stand out on a given target variable. That is, these sub-populations are exceptional with regard to the global distribution. In this paper we argue that in many applications, such as scientific discovery, subgroups are only useful if they are additionally representative of the global distribution with regard to a control variable. That is, when the distribution of this control variable is the same, or almost the same, as over the whole data. We formalise this objective function and give an efficient algorithm to compute its tight optimistic estimator for the case of a numeric target and a binary control variable. This enables us to use the branch-and-bound framework to efficiently discover the top-$k$ subgroups that are both exceptional as well as representative. Experimental evaluation on a wide range of datasets shows that with this algorithm we discover meaningful representative patterns and are up to orders of magnitude faster in terms of node evaluations as well as time. %K Computer Science, Databases, cs.DB,Computer Science, Artificial Intelligence, cs.AI
[227]
M. Kamp, M. Boley, O. Missura, and T. Gärtner, “Effective Parallelisation for Machine Learning,” in Advances in Neural Information Processing Systems 30, Long Beach, CA, USA, 2017.
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@inproceedings{NIPS2017_7226, TITLE = {Effective Parallelisation for Machine Learning}, AUTHOR = {Kamp, Michael and Boley, Mario and Missura, Olana and G{\"a}rtner, Thomas}, LANGUAGE = {eng}, PUBLISHER = {Curran Associates}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Advances in Neural Information Processing Systems 30}, EDITOR = {Guyon, I. and Luxburg, U. V. and Bengio, S. and Wallach, H. and Fergus, R. and Vishwanathan, S. and Garnett, R.}, PAGES = {6477--6488}, EID = {7226}, ADDRESS = {Long Beach, CA, USA}, }
Endnote
%0 Conference Proceedings %A Kamp, Michael %A Boley, Mario %A Missura, Olana %A G&#228;rtner, Thomas %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T Effective Parallelisation for Machine Learning : %G eng %U http://hdl.handle.net/21.11116/0000-0002-BA32-4 %D 2017 %B Thirty-first Conference on Neural Information Processing Systems %Z date of event: 2017-12-04 - 2017-12-09 %C Long Beach, CA, USA %B Advances in Neural Information Processing Systems 30 %E Guyon, I.; Luxburg, U. V.; Bengio, S.; Wallach, H.; Fergus, R.; Vishwanathan, S.; Garnett, R. %P 6477 - 6488 %Z sequence number: 7226 %I Curran Associates %U http://papers.nips.cc/paper/7226-effective-parallelisation-for-machine-learning.pdf
[228]
S. Karaev and P. Miettinen, “Algorithms for Approximate Subtropical Matrix Factorization,” 2017. [Online]. Available: http://arxiv.org/abs/1707.08872. (arXiv: 1707.08872)
Abstract
Matrix factorization methods are important tools in data mining and analysis. They can be used for many tasks, ranging from dimensionality reduction to visualization. In this paper we concentrate on the use of matrix factorizations for finding patterns from the data. Rather than using the standard algebra -- and the summation of the rank-1 components to build the approximation of the original matrix -- we use the subtropical algebra, which is an algebra over the nonnegative real values with the summation replaced by the maximum operator. Subtropical matrix factorizations allow "winner-takes-it-all" interpretations of the rank-1 components, revealing different structure than the normal (nonnegative) factorizations. We study the complexity and sparsity of the factorizations, and present a framework for finding low-rank subtropical factorizations. We present two specific algorithms, called Capricorn and Cancer, that are part of our framework. They can be used with data that has been corrupted with different types of noise, and with different error metrics, including the sum-of-absolute differences, Frobenius norm, and Jensen--Shannon divergence. Our experiments show that the algorithms perform well on data that has subtropical structure, and that they can find factorizations that are both sparse and easy to interpret.
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@online{Karaev_arXiv2017, TITLE = {Algorithms for Approximate Subtropical Matrix Factorization}, AUTHOR = {Karaev, Sanjar and Miettinen, Pauli}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1707.08872}, EPRINT = {1707.08872}, EPRINTTYPE = {arXiv}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Matrix factorization methods are important tools in data mining and analysis. They can be used for many tasks, ranging from dimensionality reduction to visualization. In this paper we concentrate on the use of matrix factorizations for finding patterns from the data. Rather than using the standard algebra -- and the summation of the rank-1 components to build the approximation of the original matrix -- we use the subtropical algebra, which is an algebra over the nonnegative real values with the summation replaced by the maximum operator. Subtropical matrix factorizations allow "winner-takes-it-all" interpretations of the rank-1 components, revealing different structure than the normal (nonnegative) factorizations. We study the complexity and sparsity of the factorizations, and present a framework for finding low-rank subtropical factorizations. We present two specific algorithms, called Capricorn and Cancer, that are part of our framework. They can be used with data that has been corrupted with different types of noise, and with different error metrics, including the sum-of-absolute differences, Frobenius norm, and Jensen--Shannon divergence. Our experiments show that the algorithms perform well on data that has subtropical structure, and that they can find factorizations that are both sparse and easy to interpret.}, }
Endnote
%0 Report %A Karaev, Sanjar %A Miettinen, Pauli %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Algorithms for Approximate Subtropical Matrix Factorization : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002E-065A-F %U http://arxiv.org/abs/1707.08872 %D 2017 %X Matrix factorization methods are important tools in data mining and analysis. They can be used for many tasks, ranging from dimensionality reduction to visualization. In this paper we concentrate on the use of matrix factorizations for finding patterns from the data. Rather than using the standard algebra -- and the summation of the rank-1 components to build the approximation of the original matrix -- we use the subtropical algebra, which is an algebra over the nonnegative real values with the summation replaced by the maximum operator. Subtropical matrix factorizations allow "winner-takes-it-all" interpretations of the rank-1 components, revealing different structure than the normal (nonnegative) factorizations. We study the complexity and sparsity of the factorizations, and present a framework for finding low-rank subtropical factorizations. We present two specific algorithms, called Capricorn and Cancer, that are part of our framework. They can be used with data that has been corrupted with different types of noise, and with different error metrics, including the sum-of-absolute differences, Frobenius norm, and Jensen--Shannon divergence. Our experiments show that the algorithms perform well on data that has subtropical structure, and that they can find factorizations that are both sparse and easy to interpret. %K Computer Science, Learning, cs.LG %U http://people.mpi-inf.mpg.de/~pmiettin/tropical/
[229]
E. Kuzey, “Populating Knowledge bases with Temporal Information,” Universität des Saarlandes, Saarbrücken, 2017.
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@phdthesis{KuzeyPhd2017, TITLE = {Populating Knowledge bases with Temporal Information}, AUTHOR = {Kuzey, Erdal}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, }
Endnote
%0 Thesis %A Kuzey, Erdal %Y Weikum, Gerhard %A referee: de Rijke , Maarten %A referee: Suchanek, Fabian %+ Databases and Information Systems, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T Populating Knowledge bases with Temporal Information : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-EAE5-7 %I Universit&#228;t des Saarlandes %C Saarbr&#252;cken %D 2017 %P XIV, 143 p. %V phd %9 phd %U http://scidok.sulb.uni-saarland.de/volltexte/2017/6811/http://scidok.sulb.uni-saarland.de/doku/lic_ohne_pod.php?la=de
[230]
L. Lange, “Time in Newspaper: A Large-Scale Analysis of Temporal Expressions in News Corpora,” Universität des Saarlandes, Saarbrücken, 2017.
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@mastersthesis{LangeBcS2017, TITLE = {Time in Newspaper: {A} Large-Scale Analysis of Temporal Expressions in News Corpora}, AUTHOR = {Lange, Lukas}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, TYPE = {Bachelor's thesis}, }
Endnote
%0 Thesis %A Lange, Lukas %Y Str&#246;tgen, Jannik %A referee: Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Time in Newspaper: A Large-Scale Analysis of Temporal Expressions in News Corpora : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-5D08-B %I Universit&#228;t des Saarlandes %C Saarbr&#252;cken %D 2017 %P 77 p. %V bachelor %9 bachelor
[231]
F. A. Lisi and D. Stepanova, “Combining Rule Learning and Nonmonotonic Reasoning for Link Prediction in Knowledge Graphs,” in Doctoral Consortium, Challenge, Industry Track, Tutorials and Posters @ RuleML+RR, London, UK, 2017.
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@inproceedings{LisiRuleML2017, TITLE = {Combining Rule Learning and Nonmonotonic Reasoning for Link Prediction in Knowledge Graphs}, AUTHOR = {Lisi, Francesca Alessandra and Stepanova, Daria}, LANGUAGE = {eng}, ISSN = {1613-0073}, URL = {urn:nbn:de:0074-1875-8}, PUBLISHER = {CEUR-WS.org}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Doctoral Consortium, Challenge, Industry Track, Tutorials and Posters @ RuleML+RR}, EDITOR = {Bassiliades, Nick and Bikakis, Antonis and Constantini, Stefania and Franconi, Enrico and Giurca, Adrian and Kontchakov, Roman and Patkosi, Theodore and Sadri, Fariba and Van Woensel, William}, SERIES = {CEUR Workshop Proceedings}, VOLUME = {1875}, ADDRESS = {London, UK}, }
Endnote
%0 Conference Proceedings %A Lisi, Francesca Alessandra %A Stepanova, Daria %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Combining Rule Learning and Nonmonotonic Reasoning for Link Prediction in Knowledge Graphs : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002E-55FC-8 %D 2017 %B International Joint Conference on Rules and Reasoning %Z date of event: 2017-07-12 - 2017-07-15 %C London, UK %B Doctoral Consortium, Challenge, Industry Track, Tutorials and Posters @ RuleML+RR %E Bassiliades, Nick; Bikakis, Antonis; Constantini, Stefania; Franconi, Enrico; Giurca, Adrian; Kontchakov, Roman; Patkosi, Theodore; Sadri, Fariba; Van Woensel, William %I CEUR-WS.org %B CEUR Workshop Proceedings %N 1875 %@ false %U http://ceur-ws.org/Vol-1875/paper20.pdf
[232]
S. MacAvaney, K. Hui, and A. Yates, “An Approach for Weakly-Supervised Deep Information Retrieval,” 2017. [Online]. Available: http://arxiv.org/abs/1707.00189. (arXiv: 1707.00189)
Abstract
Recent developments in neural information retrieval models have been promising, but a problem remains: human relevance judgments are expensive to produce, while neural models require a considerable amount of training data. In an attempt to fill this gap, we present an approach that---given a weak training set of pseudo-queries, documents, relevance information---filters the data to produce effective positive and negative query-document pairs. This allows large corpora to be used as neural IR model training data, while eliminating training examples that do not transfer well to relevance scoring. The filters include unsupervised ranking heuristics and a novel measure of interaction similarity. We evaluate our approach using a news corpus with article headlines acting as pseudo-queries and article content as documents, with implicit relevance between an article's headline and its content. By using our approach to train state-of-the-art neural IR models and comparing to established baselines, we find that training data generated by our approach can lead to good results on a benchmark test collection.
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@online{MacAvaney_arXiv2017, TITLE = {An Approach for Weakly-Supervised Deep Information Retrieval}, AUTHOR = {MacAvaney, Sean and Hui, Kai and Yates, Andrew}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1707.00189}, EPRINT = {1707.00189}, EPRINTTYPE = {arXiv}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Recent developments in neural information retrieval models have been promising, but a problem remains: human relevance judgments are expensive to produce, while neural models require a considerable amount of training data. In an attempt to fill this gap, we present an approach that---given a weak training set of pseudo-queries, documents, relevance information---filters the data to produce effective positive and negative query-document pairs. This allows large corpora to be used as neural IR model training data, while eliminating training examples that do not transfer well to relevance scoring. The filters include unsupervised ranking heuristics and a novel measure of interaction similarity. We evaluate our approach using a news corpus with article headlines acting as pseudo-queries and article content as documents, with implicit relevance between an article's headline and its content. By using our approach to train state-of-the-art neural IR models and comparing to established baselines, we find that training data generated by our approach can lead to good results on a benchmark test collection.}, }
Endnote
%0 Report %A MacAvaney, Sean %A Hui, Kai %A Yates, Andrew %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T An Approach for Weakly-Supervised Deep Information Retrieval : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002E-06C5-C %U http://arxiv.org/abs/1707.00189 %D 2017 %X Recent developments in neural information retrieval models have been promising, but a problem remains: human relevance judgments are expensive to produce, while neural models require a considerable amount of training data. In an attempt to fill this gap, we present an approach that---given a weak training set of pseudo-queries, documents, relevance information---filters the data to produce effective positive and negative query-document pairs. This allows large corpora to be used as neural IR model training data, while eliminating training examples that do not transfer well to relevance scoring. The filters include unsupervised ranking heuristics and a novel measure of interaction similarity. We evaluate our approach using a news corpus with article headlines acting as pseudo-queries and article content as documents, with implicit relevance between an article's headline and its content. By using our approach to train state-of-the-art neural IR models and comparing to established baselines, we find that training data generated by our approach can lead to good results on a benchmark test collection. %K Computer Science, Information Retrieval, cs.IR,Computer Science, Computation and Language, cs.CL
[233]
P. Mandros, M. Boley, and J. Vreeken, “Discovering Reliable Approximate Functional Dependencies,” in KDD’17, 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, 2017.
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@inproceedings{MandrosKDD2017, TITLE = {Discovering Reliable Approximate Functional Dependencies}, AUTHOR = {Mandros, Panagiotis and Boley, Mario and Vreeken, Jilles}, LANGUAGE = {eng}, ISBN = {978-1-4503-4887-4}, DOI = {10.1145/3097983.3098062}, PUBLISHER = {ACM}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {KDD'17, 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining}, PAGES = {355--363}, ADDRESS = {Halifax, NS, Canada}, }
Endnote
%0 Conference Proceedings %A Mandros, Panagiotis %A Boley, Mario %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Discovering Reliable Approximate Functional Dependencies : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002E-065F-5 %R 10.1145/3097983.3098062 %D 2017 %B 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining %Z date of event: 2017-08-13 - 2017-08-17 %C Halifax, NS, Canada %B KDD'17 %P 355 - 363 %I ACM %@ 978-1-4503-4887-4
[234]
P. Mandros, M. Boley, and J. Vreeken, “Discovering Reliable Approximate Functional Dependencies,” 2017. [Online]. Available: http://arxiv.org/abs/1705.09391. (arXiv: 1705.09391)
Abstract
Given a database and a target attribute of interest, how can we tell whether there exists a functional, or approximately functional dependence of the target on any set of other attributes in the data? How can we reliably, without bias to sample size or dimensionality, measure the strength of such a dependence? And, how can we efficiently discover the optimal or $\alpha$-approximate top-$k$ dependencies? These are exactly the questions we answer in this paper. As we want to be agnostic on the form of the dependence, we adopt an information-theoretic approach, and construct a reliable, bias correcting score that can be efficiently computed. Moreover, we give an effective optimistic estimator of this score, by which for the first time we can mine the approximate functional dependencies from data with guarantees of optimality. Empirical evaluation shows that the derived score achieves a good bias for variance trade-off, can be used within an efficient discovery algorithm, and indeed discovers meaningful dependencies. Most important, it remains reliable in the face of data sparsity.
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@online{DBLP:journals/corr/MandrosBV17, TITLE = {Discovering Reliable Approximate Functional Dependencies}, AUTHOR = {Mandros, Panagiotis and Boley, Mario and Vreeken, Jilles}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1705.09391}, EPRINT = {1705.09391}, EPRINTTYPE = {arXiv}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Given a database and a target attribute of interest, how can we tell whether there exists a functional, or approximately functional dependence of the target on any set of other attributes in the data? How can we reliably, without bias to sample size or dimensionality, measure the strength of such a dependence? And, how can we efficiently discover the optimal or $\alpha$-approximate top-$k$ dependencies? These are exactly the questions we answer in this paper. As we want to be agnostic on the form of the dependence, we adopt an information-theoretic approach, and construct a reliable, bias correcting score that can be efficiently computed. Moreover, we give an effective optimistic estimator of this score, by which for the first time we can mine the approximate functional dependencies from data with guarantees of optimality. Empirical evaluation shows that the derived score achieves a good bias for variance trade-off, can be used within an efficient discovery algorithm, and indeed discovers meaningful dependencies. Most important, it remains reliable in the face of data sparsity.}, }
Endnote
%0 Report %A Mandros, Panagiotis %A Boley, Mario %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Discovering Reliable Approximate Functional Dependencies : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-90F8-D %U http://arxiv.org/abs/1705.09391 %D 2017 %X Given a database and a target attribute of interest, how can we tell whether there exists a functional, or approximately functional dependence of the target on any set of other attributes in the data? How can we reliably, without bias to sample size or dimensionality, measure the strength of such a dependence? And, how can we efficiently discover the optimal or $\alpha$-approximate top-$k$ dependencies? These are exactly the questions we answer in this paper. As we want to be agnostic on the form of the dependence, we adopt an information-theoretic approach, and construct a reliable, bias correcting score that can be efficiently computed. Moreover, we give an effective optimistic estimator of this score, by which for the first time we can mine the approximate functional dependencies from data with guarantees of optimality. Empirical evaluation shows that the derived score achieves a good bias for variance trade-off, can be used within an efficient discovery algorithm, and indeed discovers meaningful dependencies. Most important, it remains reliable in the face of data sparsity. %K Computer Science, Databases, cs.DB,Computer Science, Artificial Intelligence, cs.AI,Computer Science, Information Theory, cs.IT,Mathematics, Information Theory, math.IT
[235]
A. Marx and J. Vreeken, “Telling Cause from Effect Using MDL-Based Local and Global Regression,” in 17th IEEE International Conference on Data Mining (ICDM 2017), New Orleans, LA, USA, 2017.
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@inproceedings{MarxICDM2017, TITLE = {Telling Cause from Effect Using {MDL}-Based Local and Global Regression}, AUTHOR = {Marx, Alexander and Vreeken, Jilles}, LANGUAGE = {eng}, ISBN = {978-1-5386-3835-4}, DOI = {10.1109/ICDM.2017.40}, PUBLISHER = {IEEE}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {17th IEEE International Conference on Data Mining (ICDM 2017)}, PAGES = {307--316}, ADDRESS = {New Orleans, LA, USA}, }
Endnote
%0 Conference Proceedings %A Marx, Alexander %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Telling Cause from Effect Using MDL-Based Local and Global Regression : %G eng %U http://hdl.handle.net/21.11116/0000-0000-63C4-3 %R 10.1109/ICDM.2017.40 %D 2017 %B 17th IEEE International Conference on Data Mining %Z date of event: 2017-11-18 - 2017-11-21 %C New Orleans, LA, USA %B 17th IEEE International Conference on Data Mining %P 307 - 316 %I IEEE %@ 978-1-5386-3835-4
[236]
A. Marx and J. Vreeken, “Telling Cause from Effect using MDL-based Local and Global Regression,” 2017. [Online]. Available: http://arxiv.org/abs/1709.08915. (arXiv: 1709.08915)
Abstract
We consider the fundamental problem of inferring the causal direction between two univariate numeric random variables $X$ and $Y$ from observational data. The two-variable case is especially difficult to solve since it is not possible to use standard conditional independence tests between the variables. To tackle this problem, we follow an information theoretic approach based on Kolmogorov complexity and use the Minimum Description Length (MDL) principle to provide a practical solution. In particular, we propose a compression scheme to encode local and global functional relations using MDL-based regression. We infer $X$ causes $Y$ in case it is shorter to describe $Y$ as a function of $X$ than the inverse direction. In addition, we introduce Slope, an efficient linear-time algorithm that through thorough empirical evaluation on both synthetic and real world data we show outperforms the state of the art by a wide margin.
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@online{Marx_arXiv1709.08915, TITLE = {Telling Cause from Effect using {MDL}-based Local and Global Regression}, AUTHOR = {Marx, Alexander and Vreeken, Jilles}, URL = {http://arxiv.org/abs/1709.08915}, DOI = {10.1109/ICDM.2017.40}, EPRINT = {1709.08915}, EPRINTTYPE = {arXiv}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, ABSTRACT = {We consider the fundamental problem of inferring the causal direction between two univariate numeric random variables $X$ and $Y$ from observational data. The two-variable case is especially difficult to solve since it is not possible to use standard conditional independence tests between the variables. To tackle this problem, we follow an information theoretic approach based on Kolmogorov complexity and use the Minimum Description Length (MDL) principle to provide a practical solution. In particular, we propose a compression scheme to encode local and global functional relations using MDL-based regression. We infer $X$ causes $Y$ in case it is shorter to describe $Y$ as a function of $X$ than the inverse direction. In addition, we introduce Slope, an efficient linear-time algorithm that through thorough empirical evaluation on both synthetic and real world data we show outperforms the state of the art by a wide margin.}, }
Endnote
%0 Report %A Marx, Alexander %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Telling Cause from Effect using MDL-based Local and Global Regression : %U http://hdl.handle.net/21.11116/0000-0002-9F18-1 %R 10.1109/ICDM.2017.40 %U http://arxiv.org/abs/1709.08915 %D 2017 %X We consider the fundamental problem of inferring the causal direction between two univariate numeric random variables $X$ and $Y$ from observational data. The two-variable case is especially difficult to solve since it is not possible to use standard conditional independence tests between the variables. To tackle this problem, we follow an information theoretic approach based on Kolmogorov complexity and use the Minimum Description Length (MDL) principle to provide a practical solution. In particular, we propose a compression scheme to encode local and global functional relations using MDL-based regression. We infer $X$ causes $Y$ in case it is shorter to describe $Y$ as a function of $X$ than the inverse direction. In addition, we introduce Slope, an efficient linear-time algorithm that through thorough empirical evaluation on both synthetic and real world data we show outperforms the state of the art by a wide margin. %K Statistics, Machine Learning, stat.ML
[237]
A. Marx and J. Vreeken, “Causal Inference on Multivariate Mixed-Type Data by Minimum Description Length,” 2017. [Online]. Available: http://arxiv.org/abs/1702.06385. (arXiv: 1702.06385)