# Publications

2022
[1]
H. Arnaout, T.-K. Tran, D. Stepanova, M. H. Gad-Elrab, S. Razniewski, and G. Weikum, “Utilizing Language Model Probes for Knowledge Graph Repair,” in Wiki Workshop 2022, Virtual Event, 2022.
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BibTeX
@inproceedings{Arnaout_Wiki2022, TITLE = {Utilizing Language Model Probes for Knowledge Graph Repair}, AUTHOR = {Arnaout, Hiba and Tran, Trung-Kien and Stepanova, Daria and Gad-Elrab, Mohamed Hassan and Razniewski, Simon and Weikum, Gerhard}, LANGUAGE = {eng}, URL = {https://wikiworkshop.org/2022/}, YEAR = {2022}, BOOKTITLE = {Wiki Workshop 2022}, ADDRESS = {Virtual Event}, }
Endnote
%0 Conference Proceedings %A Arnaout, Hiba %A Tran, Trung-Kien %A Stepanova, Daria %A Gad-Elrab, Mohamed Hassan %A Razniewski, Simon %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations 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 Utilizing Language Model Probes for Knowledge Graph Repair : %G eng %U http://hdl.handle.net/21.11116/0000-000A-63F4-3 %U https://wikiworkshop.org/2022/ %D 2022 %B Wiki Workshop 2022 %Z date of event: 2022-04-25 - 2022-04-25 %C Virtual Event %B Wiki Workshop 2022
[2]
P. Christmann, R. Saha Roy, and G. Weikum, “Conversational Question Answering on Heterogeneous Sources,” 2022. [Online]. Available: https://arxiv.org/abs/2204.11677v1. (arXiv: 2204.11677)
Abstract
Conversational question answering (ConvQA) tackles sequential information needs where contexts in follow-up questions are left implicit. Current ConvQA systems operate over homogeneous sources of information: either a knowledge base (KB), or a text corpus, or a collection of tables. This paper addresses the novel issue of jointly tapping into all of these together, this way boosting answer coverage and confidence. We present CONVINSE, an end-to-end pipeline for ConvQA over heterogeneous sources, operating in three stages: i) learning an explicit structured representation of an incoming question and its conversational context, ii) harnessing this frame-like representation to uniformly capture relevant evidences from KB, text, and tables, and iii) running a fusion-in-decoder model to generate the answer. We construct and release the first benchmark, ConvMix, for ConvQA over heterogeneous sources, comprising 3000 real-user conversations with 16000 questions, along with entity annotations, completed question utterances, and question paraphrases. Experiments demonstrate the viability and advantages of our method, compared to state-of-the-art baselines.
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@online{Christmann2022, TITLE = {Conversational Question Answering on Heterogeneous Sources}, AUTHOR = {Christmann, Phlipp and Saha Roy, Rishiraj and Weikum, Gerhard}, LANGUAGE = {eng}, URL = {https://arxiv.org/abs/2204.11677v1}, EPRINT = {2204.11677}, EPRINTTYPE = {arXiv}, YEAR = {2022}, ABSTRACT = {Conversational question answering (ConvQA) tackles sequential information needs where contexts in follow-up questions are left implicit. Current ConvQA systems operate over homogeneous sources of information: either a knowledge base (KB), or a text corpus, or a collection of tables. This paper addresses the novel issue of jointly tapping into all of these together, this way boosting answer coverage and confidence. We present CONVINSE, an end-to-end pipeline for ConvQA over heterogeneous sources, operating in three stages: i) learning an explicit structured representation of an incoming question and its conversational context, ii) harnessing this frame-like representation to uniformly capture relevant evidences from KB, text, and tables, and iii) running a fusion-in-decoder model to generate the answer. We construct and release the first benchmark, ConvMix, for ConvQA over heterogeneous sources, comprising 3000 real-user conversations with 16000 questions, along with entity annotations, completed question utterances, and question paraphrases. Experiments demonstrate the viability and advantages of our method, compared to state-of-the-art baselines.}, }
Endnote
%0 Report %A Christmann, Phlipp %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 Conversational Question Answering on Heterogeneous Sources : %G eng %U http://hdl.handle.net/21.11116/0000-000A-6148-8 %U https://arxiv.org/abs/2204.11677v1 %D 2022 %X Conversational question answering (ConvQA) tackles sequential information needs where contexts in follow-up questions are left implicit. Current ConvQA systems operate over homogeneous sources of information: either a knowledge base (KB), or a text corpus, or a collection of tables. This paper addresses the novel issue of jointly tapping into all of these together, this way boosting answer coverage and confidence. We present CONVINSE, an end-to-end pipeline for ConvQA over heterogeneous sources, operating in three stages: i) learning an explicit structured representation of an incoming question and its conversational context, ii) harnessing this frame-like representation to uniformly capture relevant evidences from KB, text, and tables, and iii) running a fusion-in-decoder model to generate the answer. We construct and release the first benchmark, ConvMix, for ConvQA over heterogeneous sources, comprising 3000 real-user conversations with 16000 questions, along with entity annotations, completed question utterances, and question paraphrases. Experiments demonstrate the viability and advantages of our method, compared to state-of-the-art baselines. %K Computer Science, Information Retrieval, cs.IR,Computer Science, Computation and Language, cs.CL
[3]
P. Christmann, R. Saha Roy, and G. Weikum, “Beyond NED: Fast and Effective Search Space Reduction for Complex Question Answering over Knowledge Bases,” in WSDM ’22, Fifteenth ACM International Conference on Web Search and Data Mining, Tempe, AZ, USA (Virutal Event), 2022.
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@inproceedings{Christmann_WSDM22, TITLE = {Beyond {NED}: {F}ast and Effective Search Space Reduction for Complex Question Answering over Knowledge Bases}, AUTHOR = {Christmann, Phlipp and Saha Roy, Rishiraj and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-9132-0}, DOI = {10.1145/3488560.3498488}, PUBLISHER = {ACM}, YEAR = {2022}, BOOKTITLE = {WSDM '22, Fifteenth ACM International Conference on Web Search and Data Mining}, PAGES = {172--180}, ADDRESS = {Tempe, AZ, USA (Virutal Event)}, }
Endnote
%0 Conference Proceedings %A Christmann, Phlipp %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 Beyond NED: Fast and Effective Search Space Reduction for Complex Question Answering over Knowledge Bases : %G eng %U http://hdl.handle.net/21.11116/0000-000A-27C6-B %R 10.1145/3488560.3498488 %D 2022 %B Fifteenth ACM International Conference on Web Search and Data Mining %Z date of event: 2022-02-21 - 2022-02-25 %C Tempe, AZ, USA (Virutal Event) %B WSDM '22 %P 172 - 180 %I ACM %@ 978-1-4503-9132-0
[4]
C. X. Chu, “Knowledge Extraction from Fictional Texts,” Universität des Saarlandes, Saarbrücken, 2022.
Abstract
Knowledge extraction from text is a key task in natural language processing, which involves many sub-tasks, such as taxonomy induction, named entity recognition and typing, relation extraction, knowledge canonicalization and so on. By constructing structured knowledge from natural language text, knowledge extraction becomes a key asset for search engines, question answering and other downstream applications. However, current knowledge extraction methods mostly focus on prominent real-world entities with Wikipedia and mainstream news articles as sources. The constructed knowledge bases, therefore, lack information about long-tail domains, with fiction and fantasy as archetypes. Fiction and fantasy are core parts of our human culture, spanning from literature to movies, TV series, comics and video games. With thousands of fictional universes which have been created, knowledge from fictional domains are subject of search-engine queries - by fans as well as cultural analysts. Unlike the real-world domain, knowledge extraction on such specific domains like fiction and fantasy has to tackle several key challenges: - Training data: Sources for fictional domains mostly come from books and fan-built content, which is sparse and noisy, and contains difficult structures of texts, such as dialogues and quotes. Training data for key tasks such as taxonomy induction, named entity typing or relation extraction are also not available. - Domain characteristics and diversity: Fictional universes can be highly sophisticated, containing entities, social structures and sometimes languages that are completely different from the real world. State-of-the-art methods for knowledge extraction make assumptions on entity-class, subclass and entity-entity relations that are often invalid for fictional domains. With different genres of fictional domains, another requirement is to transfer models across domains. - Long fictional texts: While state-of-the-art models have limitations on the input sequence length, it is essential to develop methods that are able to deal with very long texts (e.g. entire books), to capture multiple contexts and leverage widely spread cues. This dissertation addresses the above challenges, by developing new methodologies that advance the state of the art on knowledge extraction in fictional domains. - The first contribution is a method, called TiFi, for constructing type systems (taxonomy induction) for fictional domains. By tapping noisy fan-built content from online communities such as Wikia, TiFi induces taxonomies through three main steps: category cleaning, edge cleaning and top-level construction. Exploiting a variety of features from the original input, TiFi is able to construct taxonomies for a diverse range of fictional domains with high precision. - The second contribution is a comprehensive approach, called ENTYFI, for named entity recognition and typing in long fictional texts. Built on 205 automatically induced high-quality type systems for popular fictional domains, ENTYFI exploits the overlap and reuse of these fictional domains on unseen texts. By combining different typing modules with a consolidation stage, ENTYFI is able to do fine-grained entity typing in long fictional texts with high precision and recall. - The third contribution is an end-to-end system, called KnowFi, for extracting relations between entities in very long texts such as entire books. KnowFi leverages background knowledge from 142 popular fictional domains to identify interesting relations and to collect distant training samples. KnowFi devises a similarity-based ranking technique to reduce false positives in training samples and to select potential text passages that contain seed pairs of entities. By training a hierarchical neural network for all relations, KnowFi is able to infer relations between entity pairs across long fictional texts, and achieves gains over the best prior methods for relation extraction.
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@phdthesis{Chuphd2022, TITLE = {Knowledge Extraction from Fictional Texts}, AUTHOR = {Chu, Cuong Xuan}, LANGUAGE = {eng}, URL = {nbn:de:bsz:291--ds-361070}, DOI = {10.22028/D291-36107}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2022}, DATE = {2022}, ABSTRACT = {Knowledge extraction from text is a key task in natural language processing, which involves many sub-tasks, such as taxonomy induction, named entity recognition and typing, relation extraction, knowledge canonicalization and so on. By constructing structured knowledge from natural language text, knowledge extraction becomes a key asset for search engines, question answering and other downstream applications. However, current knowledge extraction methods mostly focus on prominent real-world entities with Wikipedia and mainstream news articles as sources. The constructed knowledge bases, therefore, lack information about long-tail domains, with fiction and fantasy as archetypes. Fiction and fantasy are core parts of our human culture, spanning from literature to movies, TV series, comics and video games. With thousands of fictional universes which have been created, knowledge from fictional domains are subject of search-engine queries -- by fans as well as cultural analysts. Unlike the real-world domain, knowledge extraction on such specific domains like fiction and fantasy has to tackle several key challenges: -- Training data: Sources for fictional domains mostly come from books and fan-built content, which is sparse and noisy, and contains difficult structures of texts, such as dialogues and quotes. Training data for key tasks such as taxonomy induction, named entity typing or relation extraction are also not available. -- Domain characteristics and diversity: Fictional universes can be highly sophisticated, containing entities, social structures and sometimes languages that are completely different from the real world. State-of-the-art methods for knowledge extraction make assumptions on entity-class, subclass and entity-entity relations that are often invalid for fictional domains. With different genres of fictional domains, another requirement is to transfer models across domains. -- Long fictional texts: While state-of-the-art models have limitations on the input sequence length, it is essential to develop methods that are able to deal with very long texts (e.g. entire books), to capture multiple contexts and leverage widely spread cues. This dissertation addresses the above challenges, by developing new methodologies that advance the state of the art on knowledge extraction in fictional domains. -- The first contribution is a method, called TiFi, for constructing type systems (taxonomy induction) for fictional domains. By tapping noisy fan-built content from online communities such as Wikia, TiFi induces taxonomies through three main steps: category cleaning, edge cleaning and top-level construction. Exploiting a variety of features from the original input, TiFi is able to construct taxonomies for a diverse range of fictional domains with high precision. -- The second contribution is a comprehensive approach, called ENTYFI, for named entity recognition and typing in long fictional texts. Built on 205 automatically induced high-quality type systems for popular fictional domains, ENTYFI exploits the overlap and reuse of these fictional domains on unseen texts. By combining different typing modules with a consolidation stage, ENTYFI is able to do fine-grained entity typing in long fictional texts with high precision and recall. -- The third contribution is an end-to-end system, called KnowFi, for extracting relations between entities in very long texts such as entire books. KnowFi leverages background knowledge from 142 popular fictional domains to identify interesting relations and to collect distant training samples. KnowFi devises a similarity-based ranking technique to reduce false positives in training samples and to select potential text passages that contain seed pairs of entities. By training a hierarchical neural network for all relations, KnowFi is able to infer relations between entity pairs across long fictional texts, and achieves gains over the best prior methods for relation extraction.}, }
Endnote
%0 Thesis %A Chu, Cuong Xuan %Y Weikum, Gerhard %A referee: Theobald, Martin %+ 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 Knowledge Extraction from Fictional Texts : %G eng %U http://hdl.handle.net/21.11116/0000-000A-9598-2 %R 10.22028/D291-36107 %U nbn:de:bsz:291--ds-361070 %I Universit&#228;t des Saarlandes %C Saarbr&#252;cken %D 2022 %P 129 p. %V phd %9 phd %X Knowledge extraction from text is a key task in natural language processing, which involves many sub-tasks, such as taxonomy induction, named entity recognition and typing, relation extraction, knowledge canonicalization and so on. By constructing structured knowledge from natural language text, knowledge extraction becomes a key asset for search engines, question answering and other downstream applications. However, current knowledge extraction methods mostly focus on prominent real-world entities with Wikipedia and mainstream news articles as sources. The constructed knowledge bases, therefore, lack information about long-tail domains, with fiction and fantasy as archetypes. Fiction and fantasy are core parts of our human culture, spanning from literature to movies, TV series, comics and video games. With thousands of fictional universes which have been created, knowledge from fictional domains are subject of search-engine queries - by fans as well as cultural analysts. Unlike the real-world domain, knowledge extraction on such specific domains like fiction and fantasy has to tackle several key challenges: - Training data: Sources for fictional domains mostly come from books and fan-built content, which is sparse and noisy, and contains difficult structures of texts, such as dialogues and quotes. Training data for key tasks such as taxonomy induction, named entity typing or relation extraction are also not available. - Domain characteristics and diversity: Fictional universes can be highly sophisticated, containing entities, social structures and sometimes languages that are completely different from the real world. State-of-the-art methods for knowledge extraction make assumptions on entity-class, subclass and entity-entity relations that are often invalid for fictional domains. With different genres of fictional domains, another requirement is to transfer models across domains. - Long fictional texts: While state-of-the-art models have limitations on the input sequence length, it is essential to develop methods that are able to deal with very long texts (e.g. entire books), to capture multiple contexts and leverage widely spread cues. This dissertation addresses the above challenges, by developing new methodologies that advance the state of the art on knowledge extraction in fictional domains. - The first contribution is a method, called TiFi, for constructing type systems (taxonomy induction) for fictional domains. By tapping noisy fan-built content from online communities such as Wikia, TiFi induces taxonomies through three main steps: category cleaning, edge cleaning and top-level construction. Exploiting a variety of features from the original input, TiFi is able to construct taxonomies for a diverse range of fictional domains with high precision. - The second contribution is a comprehensive approach, called ENTYFI, for named entity recognition and typing in long fictional texts. Built on 205 automatically induced high-quality type systems for popular fictional domains, ENTYFI exploits the overlap and reuse of these fictional domains on unseen texts. By combining different typing modules with a consolidation stage, ENTYFI is able to do fine-grained entity typing in long fictional texts with high precision and recall. - The third contribution is an end-to-end system, called KnowFi, for extracting relations between entities in very long texts such as entire books. KnowFi leverages background knowledge from 142 popular fictional domains to identify interesting relations and to collect distant training samples. KnowFi devises a similarity-based ranking technique to reduce false positives in training samples and to select potential text passages that contain seed pairs of entities. By training a hierarchical neural network for all relations, KnowFi is able to infer relations between entity pairs across long fictional texts, and achieves gains over the best prior methods for relation extraction. %U https://publikationen.sulb.uni-saarland.de/handle/20.500.11880/32914
[5]
S. Ghosh, S. Razniewski, and G. Weikum, “Answering Count Queries with Explanatory Evidence,” 2022. [Online]. Available: https://arxiv.org/abs/2204.05039. (arXiv: 2204.05039)
Abstract
A challenging case in web search and question answering are count queries, such as \textit{"number of songs by John Lennon"}. Prior methods merely answer these with a single, and sometimes puzzling number or return a ranked list of text snippets with different numbers. This paper proposes a methodology for answering count queries with inference, contextualization and explanatory evidence. Unlike previous systems, our method infers final answers from multiple observations, supports semantic qualifiers for the counts, and provides evidence by enumerating representative instances. Experiments with a wide variety of queries show the benefits of our method. To promote further research on this underexplored topic, we release an annotated dataset of 5k queries with 200k relevant text spans.
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@online{Ghosh2204.05039, TITLE = {Answering Count Queries with Explanatory Evidence}, AUTHOR = {Ghosh, Shrestha and Razniewski, Simon and Weikum, Gerhard}, LANGUAGE = {eng}, URL = {https://arxiv.org/abs/2204.05039}, EPRINT = {2204.05039}, EPRINTTYPE = {arXiv}, YEAR = {2022}, ABSTRACT = {A challenging case in web search and question answering are count queries, such as \textit{"number of songs by John Lennon"}. Prior methods merely answer these with a single, and sometimes puzzling number or return a ranked list of text snippets with different numbers. This paper proposes a methodology for answering count queries with inference, contextualization and explanatory evidence. Unlike previous systems, our method infers final answers from multiple observations, supports semantic qualifiers for the counts, and provides evidence by enumerating representative instances. Experiments with a wide variety of queries show the benefits of our method. To promote further research on this underexplored topic, we release an annotated dataset of 5k queries with 200k relevant text spans.}, }
Endnote
%0 Report %A Ghosh, Shrestha %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 Answering Count Queries with Explanatory Evidence : %G eng %U http://hdl.handle.net/21.11116/0000-000A-9E36-8 %U https://arxiv.org/abs/2204.05039 %D 2022 %X A challenging case in web search and question answering are count queries, such as \textit{"number of songs by John Lennon"}. Prior methods merely answer these with a single, and sometimes puzzling number or return a ranked list of text snippets with different numbers. This paper proposes a methodology for answering count queries with inference, contextualization and explanatory evidence. Unlike previous systems, our method infers final answers from multiple observations, supports semantic qualifiers for the counts, and provides evidence by enumerating representative instances. Experiments with a wide variety of queries show the benefits of our method. To promote further research on this underexplored topic, we release an annotated dataset of 5k queries with 200k relevant text spans. %K Computer Science, Information Retrieval, cs.IR
[6]
V. T. Ho, D. Stepanova, D. Milchevski, J. Strötgen, and G. Weikum, “Enhancing Knowledge Bases with Quantity Facts,” in WWW ’22, ACM Web Conference, Virtual Event, Lyon, France, 2022.
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@inproceedings{Ho_WWW22, TITLE = {Enhancing Knowledge Bases with Quantity Facts}, AUTHOR = {Ho, Vinh Thinh and Stepanova, Daria and Milchevski, Dragan and Str{\"o}tgen, Jannik and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-9096-5}, DOI = {10.1145/3485447.3511932}, PUBLISHER = {ACM}, YEAR = {2022}, BOOKTITLE = {WWW '22, ACM Web Conference}, EDITOR = {Laforest, Fr{\'e}d{\'e}rique and Troncy, Rapha{\"e}l and Simperl, Elena and Agarwal, Deepak and Gionis, Aristides and Herman, Ivan and M{\'e}dini, Lionel}, PAGES = {893--901}, ADDRESS = {Virtual Event, Lyon, France}, }
Endnote
%0 Conference Proceedings %A Ho, Vinh Thinh %A Stepanova, Daria %A Milchevski, Dragan %A Str&#246;tgen, Jannik %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Enhancing Knowledge Bases with Quantity Facts : %G eng %U http://hdl.handle.net/21.11116/0000-000A-614E-2 %R 10.1145/3485447.3511932 %D 2022 %B ACM Web Conference %Z date of event: 2022-04-25 - 2022-04-29 %C Virtual Event, Lyon, France %B WWW '22 %E Laforest, Fr&#233;d&#233;rique; Troncy, Rapha&#235;l; Simperl, Elena; Agarwal, Deepak; Gionis, Aristides; Herman, Ivan; M&#233;dini, Lionel %P 893 - 901 %I ACM %@ 978-1-4503-9096-5
[7]
P. Lahoti, K. Gummadi, and G. Weikum, “Detecting and Mitigating Test-time Failure Risks via Model-agnostic Uncertainty Learning,” in 21st IEEE International Conference on Data Mining (ICDM 2021), Auckland, New Zealand (Virtual Conference), 2022.
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@inproceedings{Gummadi_ICDM21, TITLE = {Detecting and Mitigating Test-time Failure Risks via Model-agnostic Uncertainty Learning}, AUTHOR = {Lahoti, Preethi and Gummadi, Krishna and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-6654-2398-4}, DOI = {10.1109/ICDM51629.2021.00141}, PUBLISHER = {IEEE}, YEAR = {2021}, DATE = {2022}, BOOKTITLE = {21st IEEE International Conference on Data Mining (ICDM 2021)}, EDITOR = {Bailey, James and Miettinen, Pauli and Koh, Yun Sing and Tao, Dacheng and Wu, Xindong}, PAGES = {1174--1179}, ADDRESS = {Auckland, New Zealand (Virtual Conference)}, }
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 Detecting and Mitigating Test-time Failure Risks via Model-agnostic Uncertainty Learning : %G eng %U http://hdl.handle.net/21.11116/0000-000A-5E15-6 %R 10.1109/ICDM51629.2021.00141 %D 2022 %B 21st IEEE International Conference on Data Mining %Z date of event: 2021-12-07 - 2021-12-10 %C Auckland, New Zealand (Virtual Conference) %B 21st IEEE International Conference on Data Mining %E Bailey, James; Miettinen, Pauli; Koh, Yun Sing; Tao, Dacheng; Wu, Xindong %P 1174 - 1179 %I IEEE %@ 978-1-6654-2398-4
[8]
A. Marx and J. Fischer, “Estimating Mutual Information via Geodesic kNN,” in Proceedings of the SIAM International Conference on Data Mining (SDM 2022), Alexandria, VA, USA. (Accepted/in press)
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@inproceedings{Marx_SDM2022, TITLE = {{Estimating Mutual Information via Geodesic $k$NN}}, AUTHOR = {Marx, Alexander and Fischer, Jonas}, LANGUAGE = {eng}, DOI = {10.1137/1.9781611976700.44}, PUBLISHER = {SIAM}, YEAR = {2022}, PUBLREMARK = {Accepted}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of the SIAM International Conference on Data Mining (SDM 2022)}, ADDRESS = {Alexandria, VA, USA}, }
Endnote
%0 Conference Proceedings %A Marx, Alexander %A Fischer, Jonas %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Estimating Mutual Information via Geodesic kNN : %G eng %U http://hdl.handle.net/21.11116/0000-0009-B19D-E %R 10.1137/1.9781611976700.44 %D 2021 %B SIAM International Conference on Data Mining %Z date of event: 2022-04-28 - 2022-04-30 %C Alexandria, VA, USA %B Proceedings of the SIAM International Conference on Data Mining %I SIAM
[9]
R. Pradeep, Y. Liu, X. Zhang, Y. Li, A. Yates, and J. Lin, “Squeezing Water from a Stone: A Bag of Tricks for Further Improving Cross-Encoder Effectiveness for Reranking,” in Advances in Information Retrieval (ECIR 2022), Stavanger, Norway, 2022.
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@inproceedings{Pradeep_ECIR2022, TITLE = {Squeezing Water from a Stone: {A} Bag of Tricks for Further Improving Cross-Encoder Effectiveness for Reranking}, AUTHOR = {Pradeep, Ronak and Liu, Yuqi and Zhang, Xinyu and Li, Yilin and Yates, Andrew and Lin, Jimmy}, LANGUAGE = {eng}, ISBN = {978-3-030-99736-6}, DOI = {10.1007/978-3-030-99736-6_44}, PUBLISHER = {Springer}, YEAR = {2022}, DATE = {2022}, BOOKTITLE = {Advances in Information Retrieval (ECIR 2022)}, EDITOR = {Hagen, Matthias and Verbene, Suzan and Macdonald, Craig and Seifert, Christin and Balog, Krisztian and N{\o}rv{\aa}g, Kjetil and Setty, Vinay}, PAGES = {655--670}, SERIES = {Lecture Notes in Computer Science}, VOLUME = {13185}, ADDRESS = {Stavanger, Norway}, }
Endnote
%0 Conference Proceedings %A Pradeep, Ronak %A Liu, Yuqi %A Zhang, Xinyu %A Li, Yilin %A Yates, Andrew %A Lin, Jimmy %+ External Organizations External Organizations External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Squeezing Water from a Stone: A Bag of Tricks for Further Improving Cross-Encoder Effectiveness for Reranking : %G eng %U http://hdl.handle.net/21.11116/0000-000A-9E28-8 %R 10.1007/978-3-030-99736-6_44 %D 2022 %B 44th European Conference on IR Research %Z date of event: 2022-04-10 - 2022-04-14 %C Stavanger, Norway %B Advances in Information Retrieval %E Hagen, Matthias; Verbene, Suzan; Macdonald, Craig; Seifert, Christin; Balog, Krisztian; N&#248;rv&#229;g, Kjetil; Setty, Vinay %P 655 - 670 %I Springer %@ 978-3-030-99736-6 %B Lecture Notes in Computer Science %N 13185
[10]
M. Puri, A. Varde, and G. de Melo, “Commonsense Based Text Mining on Urban Policy,” Language Resources and Evaluation, 2022.
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@article{Puri2022, TITLE = {Commonsense Based Text Mining on Urban Policy}, AUTHOR = {Puri, Manish and Varde, Aparna and de Melo, Gerard}, LANGUAGE = {eng}, ISSN = {1574-020X; 1572-0218; 1572-8412; 1574-0218; 0010-4817}, DOI = {10.1007/s10579-022-09584-6}, PUBLISHER = {Springer}, ADDRESS = {New York, NY}, YEAR = {2022}, JOURNAL = {Language Resources and Evaluation}, }
Endnote
%0 Journal Article %A Puri, Manish %A Varde, Aparna %A de Melo, Gerard %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Commonsense Based Text Mining on Urban Policy : %G eng %U http://hdl.handle.net/21.11116/0000-000A-20AC-0 %R 10.1007/s10579-022-09584-6 %7 2022 %D 2022 %J Language Resources and Evaluation %O Computers and the Humanities Lang Resources & Evaluation %I Springer %C New York, NY %@ false %U https://rdcu.be/cJwGl
[11]
S. Singhania, S. Razniewski, and G. Weikum, “Predicting Document Coverage for Relation Extraction,” Transactions of the Association of Computational Linguistics, vol. 10, 2022.
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@article{Singhania2022, TITLE = {Predicting Document Coverage for Relation Extraction}, AUTHOR = {Singhania, Sneha and Razniewski, Simon and Weikum, Gerhard}, LANGUAGE = {eng}, ISSN = {2307-387X}, DOI = {10.1162/tacl_a_00456}, PUBLISHER = {ACL}, ADDRESS = {Cambridge, MA}, YEAR = {2022}, JOURNAL = {Transactions of the Association of Computational Linguistics}, VOLUME = {10}, PAGES = {207--223}, }
Endnote
%0 Journal Article %A Singhania, Sneha %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 Predicting Document Coverage for Relation Extraction : %G eng %U http://hdl.handle.net/21.11116/0000-000A-27B8-B %R 10.1162/tacl_a_00456 %7 2022 %D 2022 %J Transactions of the Association of Computational Linguistics %V 10 %& 207 %P 207 - 223 %I ACL %C Cambridge, MA %@ false
[12]
A. Tigunova, “Extracting personal information from conversations,” Universität des Saarlandes, Saarbrücken, 2022.
Abstract
Personal knowledge is a versatile resource that is valuable for a wide range of downstream applications. Background facts about users can allow chatbot assistants to produce more topical and empathic replies. In the context of recommendation and retrieval models, personal facts can be used to customize the ranking results for individual users. A Personal Knowledge Base, populated with personal facts, such as demographic information, interests and interpersonal relationships, is a unique endpoint for storing and querying personal knowledge. Such knowledge bases are easily interpretable and can provide users with full control over their own personal knowledge, including revising stored facts and managing access by downstream services for personalization purposes. To alleviate users from extensive manual effort to build such personal knowledge base, we can leverage automated extraction methods applied to the textual content of the users, such as dialogue transcripts or social media posts. Mainstream extraction methods specialize on well-structured data, such as biographical texts or encyclopedic articles, which are rare for most people. In turn, conversational data is abundant but challenging to process and requires specialized methods for extraction of personal facts. In this dissertation we address the acquisition of personal knowledge from conversational data. We propose several novel deep learning models for inferring speakers’ personal attributes: • Demographic attributes, age, gender, profession and family status, are inferred by HAMs - hierarchical neural classifiers with attention mechanism. Trained HAMs can be transferred between different types of conversational data and provide interpretable predictions. • Long-tailed personal attributes, hobby and profession, are predicted with CHARM - a zero-shot learning model, overcoming the lack of labeled training samples for rare attribute values. By linking conversational utterances to external sources, CHARM is able to predict attribute values which it never saw during training. • Interpersonal relationships are inferred with PRIDE - a hierarchical transformer-based model. To accurately predict fine-grained relationships, PRIDE leverages personal traits of the speakers and the style of conversational utterances. Experiments with various conversational texts, including Reddit discussions and movie scripts, demonstrate the viability of our methods and their superior performance compared to state-of-the-art baselines.
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BibTeX
@phdthesis{Tiguphd2021, TITLE = {Extracting personal information from conversations}, AUTHOR = {Tigunova, Anna}, LANGUAGE = {eng}, URL = {nbn:de:bsz:291--ds-356280}, DOI = {10.22028/D291-35628}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2022}, DATE = {2022}, ABSTRACT = {Personal knowledge is a versatile resource that is valuable for a wide range of downstream applications. Background facts about users can allow chatbot assistants to produce more topical and empathic replies. In the context of recommendation and retrieval models, personal facts can be used to customize the ranking results for individual users. A Personal Knowledge Base, populated with personal facts, such as demographic information, interests and interpersonal relationships, is a unique endpoint for storing and querying personal knowledge. Such knowledge bases are easily interpretable and can provide users with full control over their own personal knowledge, including revising stored facts and managing access by downstream services for personalization purposes. To alleviate users from extensive manual effort to build such personal knowledge base, we can leverage automated extraction methods applied to the textual content of the users, such as dialogue transcripts or social media posts. Mainstream extraction methods specialize on well-structured data, such as biographical texts or encyclopedic articles, which are rare for most people. In turn, conversational data is abundant but challenging to process and requires specialized methods for extraction of personal facts. In this dissertation we address the acquisition of personal knowledge from conversational data. We propose several novel deep learning models for inferring speakers{\textquoteright} personal attributes: \mbox{$\bullet$} Demographic attributes, age, gender, profession and family status, are inferred by HAMs -- hierarchical neural classifiers with attention mechanism. Trained HAMs can be transferred between different types of conversational data and provide interpretable predictions. \mbox{$\bullet$} Long-tailed personal attributes, hobby and profession, are predicted with CHARM -- a zero-shot learning model, overcoming the lack of labeled training samples for rare attribute values. By linking conversational utterances to external sources, CHARM is able to predict attribute values which it never saw during training. \mbox{$\bullet$} Interpersonal relationships are inferred with PRIDE -- a hierarchical transformer-based model. To accurately predict fine-grained relationships, PRIDE leverages personal traits of the speakers and the style of conversational utterances. Experiments with various conversational texts, including Reddit discussions and movie scripts, demonstrate the viability of our methods and their superior performance compared to state-of-the-art baselines.}, }
Endnote
%0 Thesis %A Tigunova, Anna %Y Weikum, Gerhard %A referee: Yates, Andrew %A referee: Demberg,, Vera %+ 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 Extracting personal information from conversations : %G eng %U http://hdl.handle.net/21.11116/0000-000A-3C9E-2 %R 10.22028/D291-35628 %U nbn:de:bsz:291--ds-356280 %I Universit&#228;t des Saarlandes %C Saarbr&#252;cken %D 2022 %P 126 p. %V phd %9 phd %X Personal knowledge is a versatile resource that is valuable for a wide range of downstream applications. Background facts about users can allow chatbot assistants to produce more topical and empathic replies. In the context of recommendation and retrieval models, personal facts can be used to customize the ranking results for individual users. A Personal Knowledge Base, populated with personal facts, such as demographic information, interests and interpersonal relationships, is a unique endpoint for storing and querying personal knowledge. Such knowledge bases are easily interpretable and can provide users with full control over their own personal knowledge, including revising stored facts and managing access by downstream services for personalization purposes. To alleviate users from extensive manual effort to build such personal knowledge base, we can leverage automated extraction methods applied to the textual content of the users, such as dialogue transcripts or social media posts. Mainstream extraction methods specialize on well-structured data, such as biographical texts or encyclopedic articles, which are rare for most people. In turn, conversational data is abundant but challenging to process and requires specialized methods for extraction of personal facts. In this dissertation we address the acquisition of personal knowledge from conversational data. We propose several novel deep learning models for inferring speakers&#8217; personal attributes: &#8226; Demographic attributes, age, gender, profession and family status, are inferred by HAMs - hierarchical neural classifiers with attention mechanism. Trained HAMs can be transferred between different types of conversational data and provide interpretable predictions. &#8226; Long-tailed personal attributes, hobby and profession, are predicted with CHARM - a zero-shot learning model, overcoming the lack of labeled training samples for rare attribute values. By linking conversational utterances to external sources, CHARM is able to predict attribute values which it never saw during training. &#8226; Interpersonal relationships are inferred with PRIDE - a hierarchical transformer-based model. To accurately predict fine-grained relationships, PRIDE leverages personal traits of the speakers and the style of conversational utterances. Experiments with various conversational texts, including Reddit discussions and movie scripts, demonstrate the viability of our methods and their superior performance compared to state-of-the-art baselines. %U https://publikationen.sulb.uni-saarland.de/handle/20.500.11880/32546
[13]
A. Varde, “Computational Estimation by Scientific Data Mining with Classical Methods to Automate Learning Strategies of Scientists,” ACM Transactions on Knowledge Discovery from Data, vol. 16, no. 5, 2022.
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BibTeX
@article{Varde2022, TITLE = {Computational Estimation by Scientific Data Mining with Classical Methods to Automate Learning Strategies of Scientists}, AUTHOR = {Varde, Aparna}, LANGUAGE = {eng}, DOI = {10.1145/3502736}, PUBLISHER = {ACM}, ADDRESS = {New York, NY}, YEAR = {2022}, JOURNAL = {ACM Transactions on Knowledge Discovery from Data}, VOLUME = {16}, NUMBER = {5}, PAGES = {1--52}, EID = {86}, }
Endnote
%0 Journal Article %A Varde, Aparna %+ Databases and Information Systems, MPI for Informatics, Max Planck Society %T Computational Estimation by Scientific Data Mining with Classical Methods to Automate Learning Strategies of Scientists : %G eng %U http://hdl.handle.net/21.11116/0000-000A-9D92-0 %R 10.1145/3502736 %7 2022 %D 2022 %J ACM Transactions on Knowledge Discovery from Data %V 16 %N 5 %& 1 %P 1 - 52 %Z sequence number: 86 %I ACM %C New York, NY
[14]
A. Varde, A. Pandey, and X. Du, “Prediction Tool on Fine Particle Pollutants and Air Quality for Environmental Engineering,” SN Computer Science, vol. 3, no. 3, 2022.
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@article{Varde2022, TITLE = {Prediction Tool on Fine Particle Pollutants and Air Quality for Environmental Engineering}, AUTHOR = {Varde, Aparna and Pandey, Abidha and Du, Xu}, LANGUAGE = {eng}, ISSN = {2661-8907}, DOI = {10.1007/s42979-022-01068-2}, PUBLISHER = {Springer Nature}, ADDRESS = {Singapore}, YEAR = {2022}, JOURNAL = {SN Computer Science}, VOLUME = {3}, NUMBER = {3}, EID = {184}, }
Endnote
%0 Journal Article %A Varde, Aparna %A Pandey, Abidha %A Du, Xu %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T Prediction Tool on Fine Particle Pollutants and Air Quality for Environmental Engineering : %G eng %U http://hdl.handle.net/21.11116/0000-000A-2F55-3 %R 10.1007/s42979-022-01068-2 %7 2022 %D 2022 %J SN Computer Science %V 3 %N 3 %Z sequence number: 184 %I Springer Nature %C Singapore %@ false
2021
[15]
D. I. Adelani, J. Abbott, G. Neubig, D. D’souza, J. Kreutzer, C. Lignos, C. Palen-Michel, H. Buzaaba, S. Rijhwani, S. Ruder, S. Mayhew, I. A. Azime, S. H. Muhammad, C. C. Emezue, J. Nakatumba-Nabende, P. Ogayo, A. Anuoluwapo, C. Gitau, D. Mbaye, J. Alabi, S. M. Yimam, T. R. Gwadabe, I. Ezeani, R. A. Niyongabo, J. Mukiibi, V. Otiende, I. Orife, D. David, S. Ngom, T. Adewumi, P. Rayson, M. Adeyemi, G. Muriuki, E. Anebi, C. Chukwuneke, N. Odu, E. P. Wairagala, S. Oyerinde, C. Siro, T. S. Bateesa, T. Oloyede, Y. Wambui, V. Akinode, D. Nabagereka, M. Katusiime, A. Awokoya, M. MBOUP, D. Gebreyohannes, H. Tilaye, K. Nwaike, D. Wolde, A. Faye, B. Sibanda, O. Ahia, B. F. P. Dossou, K. Ogueji, T. I. DIOP, A. Diallo, A. Akinfaderin, T. Marengereke, and S. Osei, “MasakhaNER: Named Entity Recognition for African Languages,” Transactions of the Association for Computational Linguistics, vol. 9, 2021.
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@article{Adelani2021, TITLE = {{MasakhaNER}: {N}amed Entity Recognition for {A}frican Languages}, AUTHOR = {Adelani, David Ifeoluwa and Abbott, Jade and Neubig, Graham and D{\textquoteright}souza, Daniel and Kreutzer, Julia and Lignos, Constantine and Palen-Michel, Chester and Buzaaba, Happy and Rijhwani, Shruti and Ruder, Sebastian and Mayhew, Stephen and Azime, Israel Abebe and Muhammad, Shamsuddeen H. and Emezue, Chris Chinenye and Nakatumba-Nabende, Joyce and Ogayo, Perez and Anuoluwapo, Aremu and Gitau, Catherine and Mbaye, Derguene and Alabi, Jesujoba and Yimam, Seid Muhie and Gwadabe, Tajuddeen Rabiu and Ezeani, Ignatius and Niyongabo, Rubungo Andre and Mukiibi, Jonathan and Otiende, Verrah and Orife, Iroro and David, Davis and Ngom, Samba and Adewumi, Tosin and Rayson, Paul and Adeyemi, Mofetoluwa and Muriuki, Gerald and Anebi, Emmanuel and Chukwuneke, Chiamaka and Odu, Nkiruka and Wairagala, Eric Peter and Oyerinde, Samuel and Siro, Clemencia and Bateesa, Tobius Saul and Oloyede, Temilola and Wambui, Yvonne and Akinode, Victor and Nabagereka, Deborah and Katusiime, Maurice and Awokoya, Ayodele and MBOUP, Mouhamadane and Gebreyohannes, Dibora and Tilaye, Henok and Nwaike, Kelechi and Wolde, Degaga and Faye, Abdoulaye and Sibanda, Blessing and Ahia, Orevaoghene and Dossou, Bonaventure F. P. and Ogueji, Kelechi and DIOP, Thierno Ibrahima and Diallo, Abdoulaye and Akinfaderin, Adewale and Marengereke, Tendai and Osei, Salomey}, LANGUAGE = {eng}, ISSN = {2307-387X}, DOI = {10.1162/tacl_a_00416}, PUBLISHER = {ACL}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, JOURNAL = {Transactions of the Association for Computational Linguistics}, VOLUME = {9}, PAGES = {1116--1131}, }
Endnote
%0 Journal Article %A Adelani, David Ifeoluwa %A Abbott, Jade %A Neubig, Graham %A D&#8217;souza, Daniel %A Kreutzer, Julia %A Lignos, Constantine %A Palen-Michel, Chester %A Buzaaba, Happy %A Rijhwani, Shruti %A Ruder, Sebastian %A Mayhew, Stephen %A Azime, Israel Abebe %A Muhammad, Shamsuddeen H. %A Emezue, Chris Chinenye %A Nakatumba-Nabende, Joyce %A Ogayo, Perez %A Anuoluwapo, Aremu %A Gitau, Catherine %A Mbaye, Derguene %A Alabi, Jesujoba %A Yimam, Seid Muhie %A Gwadabe, Tajuddeen Rabiu %A Ezeani, Ignatius %A Niyongabo, Rubungo Andre %A Mukiibi, Jonathan %A Otiende, Verrah %A Orife, Iroro %A David, Davis %A Ngom, Samba %A Adewumi, Tosin %A Rayson, Paul %A Adeyemi, Mofetoluwa %A Muriuki, Gerald %A Anebi, Emmanuel %A Chukwuneke, Chiamaka %A Odu, Nkiruka %A Wairagala, Eric Peter %A Oyerinde, Samuel %A Siro, Clemencia %A Bateesa, Tobius Saul %A Oloyede, Temilola %A Wambui, Yvonne %A Akinode, Victor %A Nabagereka, Deborah %A Katusiime, Maurice %A Awokoya, Ayodele %A MBOUP, Mouhamadane %A Gebreyohannes, Dibora %A Tilaye, Henok %A Nwaike, Kelechi %A Wolde, Degaga %A Faye, Abdoulaye %A Sibanda, Blessing %A Ahia, Orevaoghene %A Dossou, Bonaventure F. P. %A Ogueji, Kelechi %A DIOP, Thierno Ibrahima %A Diallo, Abdoulaye %A Akinfaderin, Adewale %A Marengereke, Tendai %A Osei, Salomey %+ External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations %T MasakhaNER: Named Entity Recognition for African Languages : %G eng %U http://hdl.handle.net/21.11116/0000-000A-115A-E %R 10.1162/tacl_a_00416 %7 2021 %D 2021 %J Transactions of the Association for Computational Linguistics %V 9 %& 1116 %P 1116 - 1131 %I ACL %@ false
[16]
J. Ali, P. Lahoti, and K. P. Gummadi, “Accounting for Model Uncertainty in Algorithmic Discrimination,” in AIES ’21, Fourth AAAI/ACM Conference on Artificial Intelligence, Ethics and Society, Virtual Conference, 2021.
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@inproceedings{Ali_AIES2021, TITLE = {Accounting for Model Uncertainty in Algorithmic Discrimination}, AUTHOR = {Ali, Junaid and Lahoti, Preethi and Gummadi, Krishna P.}, LANGUAGE = {eng}, ISBN = {978-1-4503-8473-5}, DOI = {10.1145/3461702.3462630}, PUBLISHER = {ACM}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {AIES '21, Fourth AAAI/ACM Conference on Artificial Intelligence, Ethics and Society}, EDITOR = {Fourcade, Marion and Kuipers, Benjamin and Lazar, Seth and Mulligan, Deirdre}, PAGES = {336--345}, ADDRESS = {Virtual Conference}, }
Endnote
%0 Conference Proceedings %A Ali, Junaid %A Lahoti, Preethi %A Gummadi, Krishna P. %+ Computer Graphics, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Accounting for Model Uncertainty in Algorithmic Discrimination : %G eng %U http://hdl.handle.net/21.11116/0000-0008-72E3-7 %R 10.1145/3461702.3462630 %D 2021 %B Fourth AAAI/ACM Conference on Artificial Intelligence, Ethics and Society %Z date of event: 2021-05-19 - 2021-05-21 %C Virtual Conference %B AIES '21 %E Fourcade, Marion; Kuipers, Benjamin; Lazar, Seth; Mulligan, Deirdre %P 336 - 345 %I ACM %@ 978-1-4503-8473-5
[17]
H. Arnaout, S. Razniewski, G. Weikum, and J. Z. Pan, “Negative Knowledge for Open-world Wikidata,” in The Web Conference (WWW 2021), Ljubljana, Slovenia, 2021.
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@inproceedings{Arnaout_WWW21, TITLE = {Negative Knowledge for Open-world {W}ikidata}, AUTHOR = {Arnaout, Hiba and Razniewski, Simon and Weikum, Gerhard and Pan, Jeff Z.}, LANGUAGE = {eng}, ISBN = {978-1-4503-8313-4}, DOI = {10.1145/3442442.3452339}, PUBLISHER = {ACM}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {The Web Conference (WWW 2021)}, EDITOR = {Leskovec, Jure and Grobelnik, Marko and Najork, Mark and Tan, Jie and Zia, Leila}, PAGES = {544--551}, ADDRESS = {Ljubljana, Slovenia}, }
Endnote
%0 Conference Proceedings %A Arnaout, Hiba %A Razniewski, Simon %A Weikum, Gerhard %A Pan, Jeff Z. %+ 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 Negative Knowledge for Open-world Wikidata : %G eng %U http://hdl.handle.net/21.11116/0000-0009-6351-C %R 10.1145/3442442.3452339 %D 2021 %B The Web Conference %Z date of event: 2021-04-19 - 2021-04-23 %C Ljubljana, Slovenia %B The Web Conference %E Leskovec, Jure; Grobelnik, Marko; Najork, Mark; Tan, Jie; Zia, Leila %P 544 - 551 %I ACM %@ 978-1-4503-8313-4
[18]
H. Arnaout, S. Razniewski, G. Weikum, and J. Z. Pan, “Negative Statements Considered Useful,” Journal of Web Semantics, vol. 71, 2021.
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@article{Arnaout2021, TITLE = {Negative Statements Considered Useful}, AUTHOR = {Arnaout, Hiba and Razniewski, Simon and Weikum, Gerhard and Pan, Jeff Z.}, LANGUAGE = {eng}, DOI = {10.1016/j.websem.2021.100661}, PUBLISHER = {Elsevier}, ADDRESS = {Amsterdam}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, DATE = {2021}, JOURNAL = {Journal of Web Semantics}, VOLUME = {71}, EID = {100661}, }
Endnote
%0 Journal Article %A Arnaout, Hiba %A Razniewski, Simon %A Weikum, Gerhard %A Pan, Jeff Z. %+ 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 Negative Statements Considered Useful : %G eng %U http://hdl.handle.net/21.11116/0000-0009-A586-5 %R 10.1016/j.websem.2021.100661 %7 2021 %D 2021 %J Journal of Web Semantics %V 71 %Z sequence number: 100661 %I Elsevier %C Amsterdam
[19]
H. Arnaout, S. Razniewski, G. Weikum, and J. Z. Pan, “Wikinegata: a Knowledge Base with Interesting Negative Statements,” Proceedings of the VLDB Endowment (Proc. VLDB 2021), vol. 14, no. 12, 2021.
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@article{Arnaout2021_PVLDB, TITLE = {Wikinegata: a Knowledge Base with Interesting Negative Statements}, AUTHOR = {Arnaout, Hiba and Razniewski, Simon and Weikum, Gerhard and Pan, Jeff Z.}, LANGUAGE = {eng}, PUBLISHER = {VLDB Endowment Inc.}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, JOURNAL = {Proceedings of the VLDB Endowment (Proc. VLDB)}, VOLUME = {14}, NUMBER = {12}, PAGES = {2807--2810}, BOOKTITLE = {Proceedings of the 47th International Conference on Very Large Data Bases (VLDB 2021)}, EDITOR = {Dong, Xin Luna and Naumann, Felix}, }
Endnote
%0 Journal Article %A Arnaout, Hiba %A Razniewski, Simon %A Weikum, Gerhard %A Pan, Jeff Z. %+ 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 Wikinegata: a Knowledge Base with Interesting Negative Statements : %G eng %U http://hdl.handle.net/21.11116/0000-0009-6319-C %7 2021 %D 2021 %J Proceedings of the VLDB Endowment %O PVLDB %V 14 %N 12 %& 2807 %P 2807 - 2810 %I VLDB Endowment Inc. %B Proceedings of the 47th International Conference on Very Large Data Bases %O VLDB 2021 Copenhagen, Denmark, 16-20 August 2021
[20]
A. B. Biswas, H. Arnaout, and S. Razniewski, “Neguess: Wikidata-entity Guessing Game with Negative Clues,” in Proceedings of the ISWC 2021 Posters, Demos and Industry Tracks (ISWC-Posters-Demos-Industry 2021), Virtual Conference, 2021.
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@inproceedings{Biswas_ISWC21, TITLE = {Neguess: {W}ikidata-entity Guessing Game with Negative Clues}, AUTHOR = {Biswas, Aditya Bikram and Arnaout, Hiba and Razniewski, Simon}, LANGUAGE = {eng}, ISSN = {1613-0073}, URL = {http://ceur-ws.org/Vol-2980/paper350.pdf; urn:nbn:de:0074-2980-6}, PUBLISHER = {CEUR-WS.org}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of the ISWC 2021 Posters, Demos and Industry Tracks (ISWC-Posters-Demos-Industry 2021)}, EDITOR = {Seneviratne, Oshani and Pesquita, Catia and Sequeda, Juan and Etcheverry, Lorena}, EID = {350}, SERIES = {CEUR Workshop Proceedings}, VOLUME = {2980}, ADDRESS = {Virtual Conference}, }
Endnote
%0 Conference Proceedings %A Biswas, Aditya Bikram %A Arnaout, Hiba %A Razniewski, Simon %+ 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 Neguess: Wikidata-entity Guessing Game with Negative Clues : %G eng %U http://hdl.handle.net/21.11116/0000-0009-65AD-3 %U http://ceur-ws.org/Vol-2980/paper350.pdf %D 2021 %B 20th International Semantic Web Conference %Z date of event: 2021-10-24 - 2021-10-28 %C Virtual Conference %B Proceedings of the ISWC 2021 Posters, Demos and Industry Tracks %E Seneviratne, Oshani; Pesquita, Catia; Sequeda, Juan; Etcheverry, Lorena %Z sequence number: 350 %I CEUR-WS.org %B CEUR Workshop Proceedings %N 2980 %@ false
[21]
K. Budhathoki, M. Boley, and J. Vreeken, “Discovering Reliable Causal Rules,” in Proceedings of the SIAM International Conference on Data Mining (SDM 2021), Virtual Conference, 2021.
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@inproceedings{budhathoki:21:dice, TITLE = {Discovering Reliable Causal Rules}, AUTHOR = {Budhathoki, Kailash and Boley, Mario and Vreeken, Jilles}, LANGUAGE = {eng}, ISBN = {978-1-61197-670-0}, DOI = {10.1137/1.9781611976700.1}, PUBLISHER = {SIAM}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of the SIAM International Conference on Data Mining (SDM 2021)}, EDITOR = {Demeniconi, Carlotta and Davidson, Ian}, PAGES = {1--9}, ADDRESS = {Virtual Conference}, }
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 External Organizations %T Discovering Reliable Causal Rules : %G eng %U http://hdl.handle.net/21.11116/0000-0008-2571-F %R 10.1137/1.9781611976700.1 %D 2021 %B SIAM International Conference on Data Mining %Z date of event: 2021-04-29 - 2021-05-01 %C Virtual Conference %B Proceedings of the SIAM International Conference on Data Mining %E Demeniconi, Carlotta; Davidson, Ian %P 1 - 9 %I SIAM %@ 978-1-61197-670-0
[22]
E. Chang, X. Shen, D. Zhu, V. Demberg, and H. Su, “Neural Data-to-Text Generation with LM-based Text Augmentation,” in EACL 2021, 16th Conference of the European Chapter of the Association for Computational Linguistics, Online. (Accepted/in press)
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@inproceedings{chang2021neural, TITLE = {Neural Data-to-Text Generation with {LM}-based Text Augmentation}, AUTHOR = {Chang, Ernie and Shen, Xiaoyu and Zhu, Dawei and Demberg, Vera and Su, Hui}, LANGUAGE = {eng}, YEAR = {2021}, PUBLREMARK = {Accepted}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {EACL 2021, 16th Conference of the European Chapter of the Association for Computational Linguistics}, ADDRESS = {Online}, }
Endnote
%0 Conference Proceedings %A Chang, Ernie %A Shen, Xiaoyu %A Zhu, Dawei %A Demberg, Vera %A Su, Hui %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations %T Neural Data-to-Text Generation with LM-based Text Augmentation : %G eng %U http://hdl.handle.net/21.11116/0000-0008-149E-0 %D 2021 %B 16th Conference of the European Chapter of the Association for Computational Linguistics %Z date of event: 2021-04-19 - 2021-04-23 %C Online %B EACL 2021
[23]
P. Christmann, R. Saha Roy, and G. Weikum, “Beyond NED: Fast and Effective Search Space Reduction for Complex Question Answering over Knowledge Bases,” 2021. [Online]. Available: https://arxiv.org/abs/2108.08597. (arXiv: 2108.08597)
Abstract
Answering complex questions over knowledge bases (KB-QA) faces huge input data with billions of facts, involving millions of entities and thousands of predicates. For efficiency, QA systems first reduce the answer search space by identifying a set of facts that is likely to contain all answers and relevant cues. The most common technique or doing this is to apply named entity disambiguation (NED) systems to the question, and retrieve KB facts for the disambiguated entities. This work presents CLOCQ, an efficient method that prunes irrelevant parts of the search space using KB-aware signals. CLOCQ uses a top-k query processor over score-ordered lists of KB items that combine signals about lexical matching, relevance to the question, coherence among candidate items, and connectivity in the KB graph. Experiments with two recent QA benchmarks for complex questions demonstrate the superiority of CLOCQ over state-of-the-art baselines with respect to answer presence, size of the search space, and runtimes.
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@online{Christmann_2108.08597, TITLE = {Beyond {NED}: {F}ast and Effective Search Space Reduction for Complex Question Answering over Knowledge Bases}, AUTHOR = {Christmann, Phlipp and Saha Roy, Rishiraj and Weikum, Gerhard}, LANGUAGE = {eng}, URL = {https://arxiv.org/abs/2108.08597}, EPRINT = {2108.08597}, EPRINTTYPE = {arXiv}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Answering complex questions over knowledge bases (KB-QA) faces huge input data with billions of facts, involving millions of entities and thousands of predicates. For efficiency, QA systems first reduce the answer search space by identifying a set of facts that is likely to contain all answers and relevant cues. The most common technique or doing this is to apply named entity disambiguation (NED) systems to the question, and retrieve KB facts for the disambiguated entities. This work presents CLOCQ, an efficient method that prunes irrelevant parts of the search space using KB-aware signals. CLOCQ uses a top-k query processor over score-ordered lists of KB items that combine signals about lexical matching, relevance to the question, coherence among candidate items, and connectivity in the KB graph. Experiments with two recent QA benchmarks for complex questions demonstrate the superiority of CLOCQ over state-of-the-art baselines with respect to answer presence, size of the search space, and runtimes.}, }
Endnote
%0 Report %A Christmann, Phlipp %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 Beyond NED: Fast and Effective Search Space Reduction for Complex Question Answering over Knowledge Bases : %G eng %U http://hdl.handle.net/21.11116/0000-0009-6360-B %U https://arxiv.org/abs/2108.08597 %D 2021 %X Answering complex questions over knowledge bases (KB-QA) faces huge input data with billions of facts, involving millions of entities and thousands of predicates. For efficiency, QA systems first reduce the answer search space by identifying a set of facts that is likely to contain all answers and relevant cues. The most common technique or doing this is to apply named entity disambiguation (NED) systems to the question, and retrieve KB facts for the disambiguated entities. This work presents CLOCQ, an efficient method that prunes irrelevant parts of the search space using KB-aware signals. CLOCQ uses a top-k query processor over score-ordered lists of KB items that combine signals about lexical matching, relevance to the question, coherence among candidate items, and connectivity in the KB graph. Experiments with two recent QA benchmarks for complex questions demonstrate the superiority of CLOCQ over state-of-the-art baselines with respect to answer presence, size of the search space, and runtimes. %K Computer Science, Information Retrieval, cs.IR,Computer Science, Computation and Language, cs.CL
[24]
L. De Stefani, E. Terolli, and E. Upfal, “Tiered Sampling: An Efficient Method for Counting Sparse Motifs in Massive Graph Streams,” ACM Transactions on Knowledge Discovery from Data, vol. 15, no. 5, 2021.
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@article{DeStefani2021, TITLE = {Tiered Sampling: {A}n Efficient Method for Counting Sparse Motifs in Massive Graph Streams}, AUTHOR = {De Stefani, Lorenzo and Terolli, Erisa and Upfal, Eli}, LANGUAGE = {eng}, ISSN = {1556-4681}, DOI = {10.1145/3441299}, PUBLISHER = {ACM}, ADDRESS = {New York, NY}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, DATE = {2021}, JOURNAL = {ACM Transactions on Knowledge Discovery from Data}, VOLUME = {15}, NUMBER = {5}, PAGES = {1--52}, EID = {79}, }
Endnote
%0 Journal Article %A De Stefani, Lorenzo %A Terolli, Erisa %A Upfal, Eli %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Tiered Sampling: An Efficient Method for Counting Sparse Motifs in Massive Graph Streams : %G eng %U http://hdl.handle.net/21.11116/0000-0008-ED51-2 %R 10.1145/3441299 %7 2021 %D 2021 %J ACM Transactions on Knowledge Discovery from Data %V 15 %N 5 %& 1 %P 1 - 52 %Z sequence number: 79 %I ACM %C New York, NY %@ false
[25]
J. Fischer, F. B. Ardakani, K. Kattler, J. Walter, and M. H. Schulz, “CpG Content-dependent Associations between Transcription Factors and Histone Modifications,” PLoS One, vol. 16, no. 4, 2021.
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@article{fischer:21:cpgtfhm, TITLE = {{CpG} content-dependent associations between transcription factors and histone modifications}, AUTHOR = {Fischer, Jonas and Ardakani, Fatemeh Behjati and Kattler, Kathrin and Walter, J{\"o}rn and Schulz, Marcel Holger}, LANGUAGE = {eng}, ISSN = {1932-6203}, DOI = {10.1371/journal.pone.0249985}, PUBLISHER = {Public Library of Science}, ADDRESS = {San Francisco, CA}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, JOURNAL = {PLoS One}, VOLUME = {16}, NUMBER = {4}, EID = {0249985}, }
Endnote
%0 Journal Article %A Fischer, Jonas %A Ardakani, Fatemeh Behjati %A Kattler, Kathrin %A Walter, J&#246;rn %A Schulz, Marcel Holger %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society External Organizations External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society %T CpG Content-dependent Associations between Transcription Factors and Histone Modifications : %G eng %U http://hdl.handle.net/21.11116/0000-0008-5602-5 %R 10.1371/journal.pone.0249985 %7 2021 %D 2021 %J PLoS One %V 16 %N 4 %Z sequence number: 0249985 %I Public Library of Science %C San Francisco, CA %@ false
[26]
J. Fischer, A. Oláh, and J. Vreeken, “What’s in the Box? Exploring the Inner Life of Neural Networks with Robust Rules,” in Proceedings of the 38th International Conference on Machine Learning (ICML 2021), Virtual Event, 2021.
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@inproceedings{Fischer_ICML2021, TITLE = {What{\textquoteright}s in the Box? Exploring the Inner Life of Neural Networks with Robust Rules}, AUTHOR = {Fischer, Jonas and Ol{\'a}h, Anna and Vreeken, Jilles}, LANGUAGE = {eng}, PUBLISHER = {MLR Press}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of the 38th International Conference on Machine Learning (ICML 2021)}, EDITOR = {Meila, Marina and Zhang, Tong}, PAGES = {3352--3362}, EID = {26}, SERIES = {Proceedings of the Machine Learning}, VOLUME = {139}, ADDRESS = {Virtual Event}, }
Endnote
%0 Conference Proceedings %A Fischer, Jonas %A Ol&#225;h, Anna %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T What&#8217;s in the Box? Exploring the Inner Life of Neural Networks with Robust Rules : %G eng %U http://hdl.handle.net/21.11116/0000-0009-49F8-E %D 2021 %B 38th International Conference on Machine Learning %Z date of event: 2021-07-18 - 2021-07-24 %C Virtual Event %B Proceedings of the 38th International Conference on Machine Learning %E Meila, Marina; Zhang, Tong %P 3352 - 3362 %Z sequence number: 26 %I MLR Press %B Proceedings of the Machine Learning %N 139
[27]
J. Fischer and R. Burkholz, “Plant ‘n’ Seek: Can You Find the Winning Ticket?,” 2021. [Online]. Available: https://arxiv.org/abs/2111.11153. (arXiv: 2111.11153)
Abstract
The lottery ticket hypothesis has sparked the rapid development of pruning algorithms that perform structure learning by identifying a sparse subnetwork of a large randomly initialized neural network. The existence of such 'winning tickets' has been proven theoretically but at suboptimal sparsity levels. Contemporary pruning algorithms have furthermore been struggling to identify sparse lottery tickets for complex learning tasks. Is this suboptimal sparsity merely an artifact of existence proofs and algorithms or a general limitation of the pruning approach? And, if very sparse tickets exist, are current algorithms able to find them or are further improvements needed to achieve effective network compression? To answer these questions systematically, we derive a framework to plant and hide target architectures within large randomly initialized neural networks. For three common challenges in machine learning, we hand-craft extremely sparse network topologies, plant them in large neural networks, and evaluate state-of-the-art lottery ticket pruning methods. We find that current limitations of pruning algorithms to identify extremely sparse tickets are likely of algorithmic rather than fundamental nature and anticipate that our planting framework will facilitate future developments of efficient pruning algorithms, as we have addressed the issue of missing baselines in the field raised by Frankle et al.
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@online{FischerarXiv2111.11153, TITLE = {Plant 'n' Seek: Can You Find the Winning Ticket?}, AUTHOR = {Fischer, Jonas and Burkholz, Rebekka}, LANGUAGE = {eng}, URL = {https://arxiv.org/abs/2111.11153}, EPRINT = {2111.11153}, EPRINTTYPE = {arXiv}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, ABSTRACT = {The lottery ticket hypothesis has sparked the rapid development of pruning algorithms that perform structure learning by identifying a sparse subnetwork of a large randomly initialized neural network. The existence of such 'winning tickets' has been proven theoretically but at suboptimal sparsity levels. Contemporary pruning algorithms have furthermore been struggling to identify sparse lottery tickets for complex learning tasks. Is this suboptimal sparsity merely an artifact of existence proofs and algorithms or a general limitation of the pruning approach? And, if very sparse tickets exist, are current algorithms able to find them or are further improvements needed to achieve effective network compression? To answer these questions systematically, we derive a framework to plant and hide target architectures within large randomly initialized neural networks. For three common challenges in machine learning, we hand-craft extremely sparse network topologies, plant them in large neural networks, and evaluate state-of-the-art lottery ticket pruning methods. We find that current limitations of pruning algorithms to identify extremely sparse tickets are likely of algorithmic rather than fundamental nature and anticipate that our planting framework will facilitate future developments of efficient pruning algorithms, as we have addressed the issue of missing baselines in the field raised by Frankle et al.}, }
Endnote
%0 Report %A Fischer, Jonas %A Burkholz, Rebekka %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Plant 'n' Seek: Can You Find the Winning Ticket? : %G eng %U http://hdl.handle.net/21.11116/0000-0009-B124-6 %U https://arxiv.org/abs/2111.11153 %D 2021 %X The lottery ticket hypothesis has sparked the rapid development of pruning algorithms that perform structure learning by identifying a sparse subnetwork of a large randomly initialized neural network. The existence of such 'winning tickets' has been proven theoretically but at suboptimal sparsity levels. Contemporary pruning algorithms have furthermore been struggling to identify sparse lottery tickets for complex learning tasks. Is this suboptimal sparsity merely an artifact of existence proofs and algorithms or a general limitation of the pruning approach? And, if very sparse tickets exist, are current algorithms able to find them or are further improvements needed to achieve effective network compression? To answer these questions systematically, we derive a framework to plant and hide target architectures within large randomly initialized neural networks. For three common challenges in machine learning, we hand-craft extremely sparse network topologies, plant them in large neural networks, and evaluate state-of-the-art lottery ticket pruning methods. We find that current limitations of pruning algorithms to identify extremely sparse tickets are likely of algorithmic rather than fundamental nature and anticipate that our planting framework will facilitate future developments of efficient pruning algorithms, as we have addressed the issue of missing baselines in the field raised by Frankle et al. %K Computer Science, Learning, cs.LG,Computer Science, Artificial Intelligence, cs.AI,Statistics, Machine Learning, stat.ML
[28]
J. Fischer and R. Burkholz, “Towards Strong Pruning for Lottery Tickets with Non-Zero Biases,” 2021. [Online]. Available: https://arxiv.org/abs/2110.11150. (arXiv: 2110.11150)
Abstract
The strong lottery ticket hypothesis holds the promise that pruning randomly initialized deep neural networks could offer a computationally efficient alternative to deep learning with stochastic gradient descent. Common parameter initialization schemes and existence proofs, however, are focused on networks with zero biases, thus foregoing the potential universal approximation property of pruning. To fill this gap, we extend multiple initialization schemes and existence proofs to non-zero biases, including explicit 'looks-linear' approaches for ReLU activation functions. These do not only enable truly orthogonal parameter initialization but also reduce potential pruning errors. In experiments on standard benchmark data sets, we further highlight the practical benefits of non-zero bias initialization schemes, and present theoretically inspired extensions for state-of-the-art strong lottery ticket pruning.
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@online{Fischer_arXiv2110.11150, TITLE = {Towards Strong Pruning for Lottery Tickets with Non-Zero Biases}, AUTHOR = {Fischer, Jonas and Burkholz, Rebekka}, LANGUAGE = {eng}, URL = {https://arxiv.org/abs/2110.11150}, EPRINT = {2110.11150}, EPRINTTYPE = {arXiv}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, ABSTRACT = {The strong lottery ticket hypothesis holds the promise that pruning randomly initialized deep neural networks could offer a computationally efficient alternative to deep learning with stochastic gradient descent. Common parameter initialization schemes and existence proofs, however, are focused on networks with zero biases, thus foregoing the potential universal approximation property of pruning. To fill this gap, we extend multiple initialization schemes and existence proofs to non-zero biases, including explicit 'looks-linear' approaches for ReLU activation functions. These do not only enable truly orthogonal parameter initialization but also reduce potential pruning errors. In experiments on standard benchmark data sets, we further highlight the practical benefits of non-zero bias initialization schemes, and present theoretically inspired extensions for state-of-the-art strong lottery ticket pruning.}, }
Endnote
%0 Report %A Fischer, Jonas %A Burkholz, Rebekka %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Towards Strong Pruning for Lottery Tickets with Non-Zero Biases : %G eng %U http://hdl.handle.net/21.11116/0000-0009-B12A-0 %U https://arxiv.org/abs/2110.11150 %D 2021 %X The strong lottery ticket hypothesis holds the promise that pruning randomly initialized deep neural networks could offer a computationally efficient alternative to deep learning with stochastic gradient descent. Common parameter initialization schemes and existence proofs, however, are focused on networks with zero biases, thus foregoing the potential universal approximation property of pruning. To fill this gap, we extend multiple initialization schemes and existence proofs to non-zero biases, including explicit 'looks-linear' approaches for ReLU activation functions. These do not only enable truly orthogonal parameter initialization but also reduce potential pruning errors. In experiments on standard benchmark data sets, we further highlight the practical benefits of non-zero bias initialization schemes, and present theoretically inspired extensions for state-of-the-art strong lottery ticket pruning. %K Computer Science, Learning, cs.LG,Computer Science, Artificial Intelligence, cs.AI
[29]
J. Fischer and J. Vreeken, “Differentiable Pattern Set Mining,” in KDD ’21, 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Virtual Event, Singapore, 2021.
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@inproceedings{Fischer_KDD2021, TITLE = {Differentiable Pattern Set Mining}, AUTHOR = {Fischer, Jonas and Vreeken, Jilles}, LANGUAGE = {eng}, ISBN = {978-1-4503-8332-5}, DOI = {10.1145/3447548.3467348}, PUBLISHER = {ACM}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {KDD '21, 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining}, EDITOR = {Zhu, Fieda and Ooi, Beng, Chin and Miao, Chunyan and Cong, Gao and Tang, Jiliang and Derr, Tyler}, PAGES = {383--392}, ADDRESS = {Virtual Event, Singapore}, }
Endnote
%0 Conference Proceedings %A Fischer, Jonas %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Differentiable Pattern Set Mining : %G eng %U http://hdl.handle.net/21.11116/0000-0009-652F-2 %R 10.1145/3447548.3467348 %D 2021 %B 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining %Z date of event: 2021-08-14 - 2021-08-18 %C Virtual Event, Singapore %B KDD '21 %E Zhu, Fieda; Ooi, Beng, Chin; Miao, Chunyan; Cong, Gao; Tang, Jiliang; Derr, Tyler %P 383 - 392 %I ACM %@ 978-1-4503-8332-5
[30]
M. H. Gad-Elrab, “Explainable methods for knowledge graph refinement and exploration via symbolic reasoning,” Universität des Saarlandes, Saarbrücken, 2021.
Abstract
Knowledge Graphs (KGs) have applications in many domains such as Finance, Manufacturing, and Healthcare. While recent efforts have created large KGs, their content is far from complete and sometimes includes invalid statements. Therefore, it is crucial to refine the constructed KGs to enhance their coverage and accuracy via KG completion and KG validation. It is also vital to provide human-comprehensible explanations for such refinements, so that humans have trust in the KG quality. Enabling KG exploration, by search and browsing, is also essential for users to understand the KG value and limitations towards down-stream applications. However, the large size of KGs makes KG exploration very challenging. While the type taxonomy of KGs is a useful asset along these lines, it remains insufficient for deep exploration. In this dissertation we tackle the aforementioned challenges of KG refinement and KG exploration by combining logical reasoning over the KG with other techniques such as KG embedding models and text mining. Through such combination, we introduce methods that provide human-understandable output. Concretely, we introduce methods to tackle KG incompleteness by learning exception-aware rules over the existing KG. Learned rules are then used in inferring missing links in the KG accurately. Furthermore, we propose a framework for constructing human-comprehensible explanations for candidate facts from both KG and text. Extracted explanations are used to insure the validity of KG facts. Finally, to facilitate KG exploration, we introduce a method that combines KG embeddings with rule mining to compute informative entity clusters with explanations.
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@phdthesis{Elrabphd2021, TITLE = {Explainable methods for knowledge graph refinement and exploration via symbolic reasoning}, AUTHOR = {Gad-Elrab, Mohamed Hassan}, LANGUAGE = {eng}, DOI = {10.22028/D291-34423}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, DATE = {2021}, ABSTRACT = {Knowledge Graphs (KGs) have applications in many domains such as Finance, Manufacturing, and Healthcare. While recent efforts have created large KGs, their content is far from complete and sometimes includes invalid statements. Therefore, it is crucial to refine the constructed KGs to enhance their coverage and accuracy via KG completion and KG validation. It is also vital to provide human-comprehensible explanations for such refinements, so that humans have trust in the KG quality. Enabling KG exploration, by search and browsing, is also essential for users to understand the KG value and limitations towards down-stream applications. However, the large size of KGs makes KG exploration very challenging. While the type taxonomy of KGs is a useful asset along these lines, it remains insufficient for deep exploration. In this dissertation we tackle the aforementioned challenges of KG refinement and KG exploration by combining logical reasoning over the KG with other techniques such as KG embedding models and text mining. Through such combination, we introduce methods that provide human-understandable output. Concretely, we introduce methods to tackle KG incompleteness by learning exception-aware rules over the existing KG. Learned rules are then used in inferring missing links in the KG accurately. Furthermore, we propose a framework for constructing human-comprehensible explanations for candidate facts from both KG and text. Extracted explanations are used to insure the validity of KG facts. Finally, to facilitate KG exploration, we introduce a method that combines KG embeddings with rule mining to compute informative entity clusters with explanations.}, }
Endnote
%0 Thesis %A Gad-Elrab, Mohamed Hassan %Y Weikum, Gerhard %A referee: Theobald, Martin %A referee: Stepanova, Daria %A referee: Razniewski, Simon %+ 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 Databases and Information Systems, MPI for Informatics, Max Planck Society %T Explainable methods for knowledge graph refinement and exploration via symbolic reasoning : %G eng %U http://hdl.handle.net/21.11116/0000-0009-427E-0 %R 10.22028/D291-34423 %I Universit&#228;t des Saarlandes %C Saarbr&#252;cken %D 2021 %P 176 p. %V phd %9 phd %X Knowledge Graphs (KGs) have applications in many domains such as Finance, Manufacturing, and Healthcare. While recent efforts have created large KGs, their content is far from complete and sometimes includes invalid statements. Therefore, it is crucial to refine the constructed KGs to enhance their coverage and accuracy via KG completion and KG validation. It is also vital to provide human-comprehensible explanations for such refinements, so that humans have trust in the KG quality. Enabling KG exploration, by search and browsing, is also essential for users to understand the KG value and limitations towards down-stream applications. However, the large size of KGs makes KG exploration very challenging. While the type taxonomy of KGs is a useful asset along these lines, it remains insufficient for deep exploration. In this dissertation we tackle the aforementioned challenges of KG refinement and KG exploration by combining logical reasoning over the KG with other techniques such as KG embedding models and text mining. Through such combination, we introduce methods that provide human-understandable output. Concretely, we introduce methods to tackle KG incompleteness by learning exception-aware rules over the existing KG. Learned rules are then used in inferring missing links in the KG accurately. Furthermore, we propose a framework for constructing human-comprehensible explanations for candidate facts from both KG and text. Extracted explanations are used to insure the validity of KG facts. Finally, to facilitate KG exploration, we introduce a method that combines KG embeddings with rule mining to compute informative entity clusters with explanations. %K knowledge graphs symbolic learning embedding models rule learning Big Data %U https://publikationen.sulb.uni-saarland.de/handle/20.500.11880/31629
[31]
A. Ghazimatin, “Enhancing explainability and scrutability of recommender systems,” Universität des Saarlandes, Saarbrücken, 2021.
Abstract
Our increasing reliance on complex algorithms for recommendations calls for models and methods for explainable, scrutable, and trustworthy AI. While explainability is required for understanding the relationships between model inputs and outputs, a scrutable system allows us to modify its behavior as desired. These properties help bridge the gap between our expectations and the algorithm’s behavior and accordingly boost our trust in AI. Aiming to cope with information overload, recommender systems play a crucial role in ﬁltering content (such as products, news, songs, and movies) and shaping a personalized experience for their users. Consequently, there has been a growing demand from the information consumers to receive proper explanations for their personalized recommendations. These explanations aim at helping users understand why certain items are recommended to them and how their previous inputs to the system relate to the generation of such recommendations. Besides, in the event of receiving undesirable content, explanations could possibly contain valuable information as to how the system’s behavior can be modiﬁed accordingly. In this thesis, we present our contributions towards explainability and scrutability of recommender systems: • We introduce a user-centric framework, FAIRY, for discovering and ranking post-hoc explanations for the social feeds generated by black-box platforms. These explanations reveal relationships between users’ proﬁles and their feed items and are extracted from the local interaction graphs of users. FAIRY employs a learning-to-rank (LTR) method to score candidate explanations based on their relevance and surprisal. • We propose a method, PRINCE, to facilitate provider-side explainability in graph-based recommender systems that use personalized PageRank at their core. PRINCE explanations are comprehensible for users, because they present subsets of the user’s prior actions responsible for the received recommendations. PRINCE operates in a counterfactual setup and builds on a polynomial-time algorithm for ﬁnding the smallest counterfactual explanations. • We propose a human-in-the-loop framework, ELIXIR, for enhancing scrutability and subsequently the recommendation models by leveraging user feedback on explanations. ELIXIR enables recommender systems to collect user feedback on pairs of recommendations and explanations. The feedback is incorporated into the model by imposing a soft constraint for learning user-speciﬁc item representations. We evaluate all proposed models and methods with real user studies and demonstrate their beneﬁts at achieving explainability and scrutability in recommender systems.
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BibTeX
@phdthesis{Ghazphd2021, TITLE = {Enhancing explainability and scrutability of recommender systems}, AUTHOR = {Ghazimatin, Azin}, LANGUAGE = {eng}, URL = {nbn:de:bsz:291--ds-355166}, DOI = {10.22028/D291-35516}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, DATE = {2021}, ABSTRACT = {Our increasing reliance on complex algorithms for recommendations calls for models and methods for explainable, scrutable, and trustworthy AI. While explainability is required for understanding the relationships between model inputs and outputs, a scrutable system allows us to modify its behavior as desired. These properties help bridge the gap between our expectations and the algorithm{\textquoteright}s behavior and accordingly boost our trust in AI. Aiming to cope with information overload, recommender systems play a crucial role in {fi}ltering content (such as products, news, songs, and movies) and shaping a personalized experience for their users. Consequently, there has been a growing demand from the information consumers to receive proper explanations for their personalized recommendations. These explanations aim at helping users understand why certain items are recommended to them and how their previous inputs to the system relate to the generation of such recommendations. Besides, in the event of receiving undesirable content, explanations could possibly contain valuable information as to how the system{\textquoteright}s behavior can be modi{fi}ed accordingly. In this thesis, we present our contributions towards explainability and scrutability of recommender systems: \mbox{$\bullet$} We introduce a user-centric framework, FAIRY, for discovering and ranking post-hoc explanations for the social feeds generated by black-box platforms. These explanations reveal relationships between users{\textquoteright} pro{fi}les and their feed items and are extracted from the local interaction graphs of users. FAIRY employs a learning-to-rank (LTR) method to score candidate explanations based on their relevance and surprisal. \mbox{$\bullet$} We propose a method, PRINCE, to facilitate provider-side explainability in graph-based recommender systems that use personalized PageRank at their core. PRINCE explanations are comprehensible for users, because they present subsets of the user{\textquoteright}s prior actions responsible for the received recommendations. PRINCE operates in a counterfactual setup and builds on a polynomial-time algorithm for {fi}nding the smallest counterfactual explanations. \mbox{$\bullet$} We propose a human-in-the-loop framework, ELIXIR, for enhancing scrutability and subsequently the recommendation models by leveraging user feedback on explanations. ELIXIR enables recommender systems to collect user feedback on pairs of recommendations and explanations. The feedback is incorporated into the model by imposing a soft constraint for learning user-speci{fi}c item representations. We evaluate all proposed models and methods with real user studies and demonstrate their bene{fi}ts at achieving explainability and scrutability in recommender systems.}, }
Endnote
%0 Thesis %A Ghazimatin, Azin %Y Weikum, Gerhard %A referee: Saha Roy, Rishiraj %A referee: Amer-Yahia, Sihem %+ 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 Enhancing explainability and scrutability of recommender systems : %G eng %U http://hdl.handle.net/21.11116/0000-000A-3C99-7 %R 10.22028/D291-35516 %U nbn:de:bsz:291--ds-355166 %I Universit&#228;t des Saarlandes %C Saarbr&#252;cken %D 2021 %P 136 p. %V phd %9 phd %X Our increasing reliance on complex algorithms for recommendations calls for models and methods for explainable, scrutable, and trustworthy AI. While explainability is required for understanding the relationships between model inputs and outputs, a scrutable system allows us to modify its behavior as desired. These properties help bridge the gap between our expectations and the algorithm&#8217;s behavior and accordingly boost our trust in AI. Aiming to cope with information overload, recommender systems play a crucial role in &#64257;ltering content (such as products, news, songs, and movies) and shaping a personalized experience for their users. Consequently, there has been a growing demand from the information consumers to receive proper explanations for their personalized recommendations. These explanations aim at helping users understand why certain items are recommended to them and how their previous inputs to the system relate to the generation of such recommendations. Besides, in the event of receiving undesirable content, explanations could possibly contain valuable information as to how the system&#8217;s behavior can be modi&#64257;ed accordingly. In this thesis, we present our contributions towards explainability and scrutability of recommender systems: &#8226; We introduce a user-centric framework, FAIRY, for discovering and ranking post-hoc explanations for the social feeds generated by black-box platforms. These explanations reveal relationships between users&#8217; pro&#64257;les and their feed items and are extracted from the local interaction graphs of users. FAIRY employs a learning-to-rank (LTR) method to score candidate explanations based on their relevance and surprisal. &#8226; We propose a method, PRINCE, to facilitate provider-side explainability in graph-based recommender systems that use personalized PageRank at their core. PRINCE explanations are comprehensible for users, because they present subsets of the user&#8217;s prior actions responsible for the received recommendations. PRINCE operates in a counterfactual setup and builds on a polynomial-time algorithm for &#64257;nding the smallest counterfactual explanations. &#8226; We propose a human-in-the-loop framework, ELIXIR, for enhancing scrutability and subsequently the recommendation models by leveraging user feedback on explanations. ELIXIR enables recommender systems to collect user feedback on pairs of recommendations and explanations. The feedback is incorporated into the model by imposing a soft constraint for learning user-speci&#64257;c item representations. We evaluate all proposed models and methods with real user studies and demonstrate their bene&#64257;ts at achieving explainability and scrutability in recommender systems. %U https://publikationen.sulb.uni-saarland.de/handle/20.500.11880/32590
[32]
A. Ghazimatin, S. Pramanik, R. Saha Roy, and G. Weikum, “ELIXIR: Learning from User Feedback on Explanations to Improve Recommender Models,” 2021. [Online]. Available: https://arxiv.org/abs/2102.09388. (arXiv: 2102.09388)
Abstract
System-provided explanations for recommendations are an important component towards transparent and trustworthy AI. In state-of-the-art research, this is a one-way signal, though, to improve user acceptance. In this paper, we turn the role of explanations around and investigate how they can contribute to enhancing the quality of generated recommendations themselves. We devise a human-in-the-loop framework, called ELIXIR, where user feedback on explanations is leveraged for pairwise learning of user preferences. ELIXIR leverages feedback on pairs of recommendations and explanations to learn user-specific latent preference vectors, overcoming sparseness by label propagation with item-similarity-based neighborhoods. Our framework is instantiated using generalized graph recommendation via Random Walk with Restart. Insightful experiments with a real user study show significant improvements in movie and book recommendations over item-level feedback.
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@online{Ghazimatin_2102.09388, TITLE = {{ELIXIR}: {L}earning from User Feedback on Explanations to Improve Recommender Models}, AUTHOR = {Ghazimatin, Azin and Pramanik, Soumajit and Saha Roy, Rishiraj and Weikum, Gerhard}, LANGUAGE = {eng}, URL = {https://arxiv.org/abs/2102.09388}, EPRINT = {2102.09388}, EPRINTTYPE = {arXiv}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, ABSTRACT = {System-provided explanations for recommendations are an important component towards transparent and trustworthy AI. In state-of-the-art research, this is a one-way signal, though, to improve user acceptance. In this paper, we turn the role of explanations around and investigate how they can contribute to enhancing the quality of generated recommendations themselves. We devise a human-in-the-loop framework, called ELIXIR, where user feedback on explanations is leveraged for pairwise learning of user preferences. ELIXIR leverages feedback on pairs of recommendations and explanations to learn user-specific latent preference vectors, overcoming sparseness by label propagation with item-similarity-based neighborhoods. Our framework is instantiated using generalized graph recommendation via Random Walk with Restart. Insightful experiments with a real user study show significant improvements in movie and book recommendations over item-level feedback.}, }
Endnote
%0 Report %A Ghazimatin, Azin %A Pramanik, Soumajit %A Saha Roy, Rishiraj %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 ELIXIR: Learning from User Feedback on Explanations to Improve Recommender Models : %G eng %U http://hdl.handle.net/21.11116/0000-0008-0309-B %U https://arxiv.org/abs/2102.09388 %D 2021 %X System-provided explanations for recommendations are an important component towards transparent and trustworthy AI. In state-of-the-art research, this is a one-way signal, though, to improve user acceptance. In this paper, we turn the role of explanations around and investigate how they can contribute to enhancing the quality of generated recommendations themselves. We devise a human-in-the-loop framework, called ELIXIR, where user feedback on explanations is leveraged for pairwise learning of user preferences. ELIXIR leverages feedback on pairs of recommendations and explanations to learn user-specific latent preference vectors, overcoming sparseness by label propagation with item-similarity-based neighborhoods. Our framework is instantiated using generalized graph recommendation via Random Walk with Restart. Insightful experiments with a real user study show significant improvements in movie and book recommendations over item-level feedback. %K Computer Science, Information Retrieval, cs.IR,Computer Science, Artificial Intelligence, cs.AI,Computer Science, Learning, cs.LG
[33]
A. Ghazimatin, S. Pramanik, R. Saha Roy, and G. Weikum, “ELIXIR: Learning from User Feedback on Explanations to Improve Recommender Models,” in The Web Conference 2021 (WWW 2021), Ljubljana, Slovenia, 2021.
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@inproceedings{Ghazimatin_WWW21, TITLE = {{ELIXIR}: {L}earning from User Feedback on Explanations to Improve Recommender Models}, AUTHOR = {Ghazimatin, Azin and Pramanik, Soumajit and Saha Roy, Rishiraj and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-8312-7}, DOI = {10.1145/3442381.3449848}, PUBLISHER = {ACM}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {The Web Conference 2021 (WWW 2021)}, EDITOR = {Leskovec, Jure and Grobelnik, Marko and Najork, Marc and Tang, Jie and Zia, Leila}, PAGES = {3850--3860}, ADDRESS = {Ljubljana, Slovenia}, }
Endnote
%0 Conference Proceedings %A Ghazimatin, Azin %A Pramanik, Soumajit %A Saha Roy, Rishiraj %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 ELIXIR: Learning from User Feedback on Explanations to Improve Recommender Models : %G eng %U http://hdl.handle.net/21.11116/0000-0008-0303-1 %R 10.1145/3442381.3449848 %D 2021 %B 30th The Web Conference %Z date of event: 2021-04-19 - 2021-04-23 %C Ljubljana, Slovenia %B The Web Conference 2021 %E Leskovec, Jure; Grobelnik, Marko; Najork, Marc; Tang, Jie; Zia, Leila %P 3850 - 3860 %I ACM %@ 978-1-4503-8312-7
[34]
A. Guimarães and G. Weikum, “X-Posts Explained: Analyzing and Predicting Controversial Contributions in Thematically Diverse Reddit Forums,” in Proceedings of the Fifteenth International Conference on Web and Social Media (ICWSM 2021), Atlanta, GA, USA, 2021.
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@inproceedings{Guimaraes_ICWSM2021, TITLE = {X-Posts Explained: {A}nalyzing and Predicting Controversial Contributions in Thematically Diverse {R}eddit Forums}, AUTHOR = {Guimar{\~a}es, Anna and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-57735-869-5}, URL = {https://ojs.aaai.org/index.php/ICWSM/article/view/18050}, PUBLISHER = {AAAI}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of the Fifteenth International Conference on Web and Social Media (ICWSM 2021)}, PAGES = {163--172}, ADDRESS = {Atlanta, GA, USA}, }
Endnote
%0 Conference Proceedings %A Guimar&#227;es, Anna %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T X-Posts Explained: Analyzing and Predicting Controversial Contributions in Thematically Diverse Reddit Forums : %G eng %U http://hdl.handle.net/21.11116/0000-0008-0345-7 %U https://ojs.aaai.org/index.php/ICWSM/article/view/18050 %D 2021 %B 15th International Conference on Web and Social Media %Z date of event: 2021-06-07 - 2021-06-10 %C Atlanta, GA, USA %B Proceedings of the Fifteenth International Conference on Web and Social Media %P 163 - 172 %I AAAI %@ 978-1-57735-869-5 %U https://ojs.aaai.org/index.php/ICWSM/article/view/18050/17853
[35]
G. Haratinezhad Torbati, A. Yates, and G. Weikum, “You Get What You Chat: Using Conversations to Personalize Search-based Recommendations,” 2021. [Online]. Available: https://arxiv.org/abs/2109.04716. (arXiv: 2109.04716)
Abstract
Prior work on personalized recommendations has focused on exploiting explicit signals from user-specific queries, clicks, likes, and ratings. This paper investigates tapping into a different source of implicit signals of interests and tastes: online chats between users. The paper develops an expressive model and effective methods for personalizing search-based entity recommendations. User models derived from chats augment different methods for re-ranking entity answers for medium-grained queries. The paper presents specific techniques to enhance the user models by capturing domain-specific vocabularies and by entity-based expansion. Experiments are based on a collection of online chats from a controlled user study covering three domains: books, travel, food. We evaluate different configurations and compare chat-based user models against concise user profiles from questionnaires. Overall, these two variants perform on par in terms of NCDG@20, but each has advantages in certain domains.
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@online{Haratinezhad2109.04716, TITLE = {You Get What You Chat: Using Conversations to Personalize Search-based Recommendations}, AUTHOR = {Haratinezhad Torbati, Ghazaleh and Yates, Andrew and Weikum, Gerhard}, LANGUAGE = {eng}, URL = {https://arxiv.org/abs/2109.04716}, EPRINT = {2109.04716}, EPRINTTYPE = {arXiv}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Prior work on personalized recommendations has focused on exploiting explicit signals from user-specific queries, clicks, likes, and ratings. This paper investigates tapping into a different source of implicit signals of interests and tastes: online chats between users. The paper develops an expressive model and effective methods for personalizing search-based entity recommendations. User models derived from chats augment different methods for re-ranking entity answers for medium-grained queries. The paper presents specific techniques to enhance the user models by capturing domain-specific vocabularies and by entity-based expansion. Experiments are based on a collection of online chats from a controlled user study covering three domains: books, travel, food. We evaluate different configurations and compare chat-based user models against concise user profiles from questionnaires. Overall, these two variants perform on par in terms of NCDG@20, but each has advantages in certain domains.}, }
Endnote
%0 Report %A Haratinezhad Torbati, Ghazaleh %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 %T You Get What You Chat: Using Conversations to Personalize Search-based Recommendations : %G eng %U http://hdl.handle.net/21.11116/0000-0009-64B9-6 %U https://arxiv.org/abs/2109.04716 %D 2021 %X Prior work on personalized recommendations has focused on exploiting explicit signals from user-specific queries, clicks, likes, and ratings. This paper investigates tapping into a different source of implicit signals of interests and tastes: online chats between users. The paper develops an expressive model and effective methods for personalizing search-based entity recommendations. User models derived from chats augment different methods for re-ranking entity answers for medium-grained queries. The paper presents specific techniques to enhance the user models by capturing domain-specific vocabularies and by entity-based expansion. Experiments are based on a collection of online chats from a controlled user study covering three domains: books, travel, food. We evaluate different configurations and compare chat-based user models against concise user profiles from questionnaires. Overall, these two variants perform on par in terms of NCDG@20, but each has advantages in certain domains. %K Computer Science, Information Retrieval, cs.IR
[36]
G. Haratinezhad Torbati, A. Yates, and G. Weikum, “Personalized Entity Search by Sparse and Scrutable User Profiles,” 2021. [Online]. Available: https://arxiv.org/abs/2109.04713. (arXiv: 2109.04713)
Abstract
Prior work on personalizing web search results has focused on considering query-and-click logs to capture users individual interests. For product search, extensive user histories about purchases and ratings have been exploited. However, for general entity search, such as for books on specific topics or travel destinations with certain features, personalization is largely underexplored. In this paper, we address personalization of book search, as an exemplary case of entity search, by exploiting sparse user profiles obtained through online questionnaires. We devise and compare a variety of re-ranking methods based on language models or neural learning. Our experiments show that even very sparse information about individuals can enhance the effectiveness of the search results.
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@online{Haratinezhad2109.04713, TITLE = {Personalized Entity Search by Sparse and Scrutable User Profiles}, AUTHOR = {Haratinezhad Torbati, Ghazaleh and Yates, Andrew and Weikum, Gerhard}, LANGUAGE = {eng}, URL = {https://arxiv.org/abs/2109.04713}, EPRINT = {2109.04713}, EPRINTTYPE = {arXiv}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Prior work on personalizing web search results has focused on considering query-and-click logs to capture users individual interests. For product search, extensive user histories about purchases and ratings have been exploited. However, for general entity search, such as for books on specific topics or travel destinations with certain features, personalization is largely underexplored. In this paper, we address personalization of book search, as an exemplary case of entity search, by exploiting sparse user profiles obtained through online questionnaires. We devise and compare a variety of re-ranking methods based on language models or neural learning. Our experiments show that even very sparse information about individuals can enhance the effectiveness of the search results.}, }
Endnote
%0 Report %A Haratinezhad Torbati, Ghazaleh %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 %T Personalized Entity Search by Sparse and Scrutable User Profiles : %G eng %U http://hdl.handle.net/21.11116/0000-0009-64AC-5 %U https://arxiv.org/abs/2109.04713 %D 2021 %X Prior work on personalizing web search results has focused on considering query-and-click logs to capture users individual interests. For product search, extensive user histories about purchases and ratings have been exploited. However, for general entity search, such as for books on specific topics or travel destinations with certain features, personalization is largely underexplored. In this paper, we address personalization of book search, as an exemplary case of entity search, by exploiting sparse user profiles obtained through online questionnaires. We devise and compare a variety of re-ranking methods based on language models or neural learning. Our experiments show that even very sparse information about individuals can enhance the effectiveness of the search results. %K Computer Science, Information Retrieval, cs.IR
[37]
M. Hedderich, J. Fischer, D. Klakow, and J. Vreeken, “Label-Descriptive Patterns and their Application to Characterizing Classification Errors,” 2021. [Online]. Available: https://arxiv.org/abs/2110.09599. (arXiv: 2110.09599)
Abstract
State-of-the-art deep learning methods achieve human-like performance on many tasks, but make errors nevertheless. Characterizing these errors in easily interpretable terms gives insight into whether a model is prone to making systematic errors, but also gives a way to act and improve the model. In this paper we propose a method that allows us to do so for arbitrary classifiers by mining a small set of patterns that together succinctly describe the input data that is partitioned according to correctness of prediction. We show this is an instance of the more general label description problem, which we formulate in terms of the Minimum Description Length principle. To discover good pattern sets we propose the efficient and hyperparameter-free Premise algorithm, which through an extensive set of experiments we show on both synthetic and real-world data performs very well in practice; unlike existing solutions it ably recovers ground truth patterns, even on highly imbalanced data over many unique items, or where patterns are only weakly associated to labels. Through two real-world case studies we confirm that Premise gives clear and actionable insight into the systematic errors made by modern NLP classifiers.
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@online{Hedderich_arXiv2110.09599, TITLE = {Label-Descriptive Patterns and their Application to Characterizing Classification Errors}, AUTHOR = {Hedderich, Michael and Fischer, Jonas and Klakow, Dietrich and Vreeken, Jilles}, LANGUAGE = {eng}, URL = {https://arxiv.org/abs/2110.09599}, EPRINT = {2110.09599}, EPRINTTYPE = {arXiv}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, ABSTRACT = {State-of-the-art deep learning methods achieve human-like performance on many tasks, but make errors nevertheless. Characterizing these errors in easily interpretable terms gives insight into whether a model is prone to making systematic errors, but also gives a way to act and improve the model. In this paper we propose a method that allows us to do so for arbitrary classifiers by mining a small set of patterns that together succinctly describe the input data that is partitioned according to correctness of prediction. We show this is an instance of the more general label description problem, which we formulate in terms of the Minimum Description Length principle. To discover good pattern sets we propose the efficient and hyperparameter-free Premise algorithm, which through an extensive set of experiments we show on both synthetic and real-world data performs very well in practice; unlike existing solutions it ably recovers ground truth patterns, even on highly imbalanced data over many unique items, or where patterns are only weakly associated to labels. Through two real-world case studies we confirm that Premise gives clear and actionable insight into the systematic errors made by modern NLP classifiers.}, }
Endnote
%0 Report %A Hedderich, Michael %A Fischer, Jonas %A Klakow, Dietrich %A Vreeken, Jilles %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T Label-Descriptive Patterns and their Application to Characterizing Classification Errors : %G eng %U http://hdl.handle.net/21.11116/0000-0009-B127-3 %U https://arxiv.org/abs/2110.09599 %D 2021 %X State-of-the-art deep learning methods achieve human-like performance on many tasks, but make errors nevertheless. Characterizing these errors in easily interpretable terms gives insight into whether a model is prone to making systematic errors, but also gives a way to act and improve the model. In this paper we propose a method that allows us to do so for arbitrary classifiers by mining a small set of patterns that together succinctly describe the input data that is partitioned according to correctness of prediction. We show this is an instance of the more general label description problem, which we formulate in terms of the Minimum Description Length principle. To discover good pattern sets we propose the efficient and hyperparameter-free Premise algorithm, which through an extensive set of experiments we show on both synthetic and real-world data performs very well in practice; unlike existing solutions it ably recovers ground truth patterns, even on highly imbalanced data over many unique items, or where patterns are only weakly associated to labels. Through two real-world case studies we confirm that Premise gives clear and actionable insight into the systematic errors made by modern NLP classifiers. %K Computer Science, Learning, cs.LG,Computer Science, Computation and Language, cs.CL
[38]
E. Heiter, J. Fischer, and J. Vreeken, “Factoring Out Prior Knowledge from Low-dimensional Embeddings,” 2021. [Online]. Available: https://arxiv.org/abs/2103.01828. (arXiv: 2103.01828)
Abstract
Low-dimensional embedding techniques such as tSNE and UMAP allow visualizing high-dimensional data and therewith facilitate the discovery of interesting structure. Although they are widely used, they visualize data as is, rather than in light of the background knowledge we have about the data. What we already know, however, strongly determines what is novel and hence interesting. In this paper we propose two methods for factoring out prior knowledge in the form of distance matrices from low-dimensional embeddings. To factor out prior knowledge from tSNE embeddings, we propose JEDI that adapts the tSNE objective in a principled way using Jensen-Shannon divergence. To factor out prior knowledge from any downstream embedding approach, we propose CONFETTI, in which we directly operate on the input distance matrices. Extensive experiments on both synthetic and real world data show that both methods work well, providing embeddings that exhibit meaningful structure that would otherwise remain hidden.
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@online{heiter:21:factoring, TITLE = {Factoring Out Prior Knowledge from Low-dimensional Embeddings}, AUTHOR = {Heiter, Edith and Fischer, Jonas and Vreeken, Jilles}, LANGUAGE = {eng}, URL = {https://arxiv.org/abs/2103.01828}, EPRINT = {2103.01828}, EPRINTTYPE = {arXiv}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Low-dimensional embedding techniques such as tSNE and UMAP allow visualizing high-dimensional data and therewith facilitate the discovery of interesting structure. Although they are widely used, they visualize data as is, rather than in light of the background knowledge we have about the data. What we already know, however, strongly determines what is novel and hence interesting. In this paper we propose two methods for factoring out prior knowledge in the form of distance matrices from low-dimensional embeddings. To factor out prior knowledge from tSNE embeddings, we propose JEDI that adapts the tSNE objective in a principled way using Jensen-Shannon divergence. To factor out prior knowledge from any downstream embedding approach, we propose CONFETTI, in which we directly operate on the input distance matrices. Extensive experiments on both synthetic and real world data show that both methods work well, providing embeddings that exhibit meaningful structure that would otherwise remain hidden.}, }
Endnote
%0 Report %A Heiter, Edith %A Fischer, Jonas %A Vreeken, Jilles %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Factoring Out Prior Knowledge from Low-dimensional Embeddings : %G eng %U http://hdl.handle.net/21.11116/0000-0008-16ED-5 %U https://arxiv.org/abs/2103.01828 %D 2021 %X Low-dimensional embedding techniques such as tSNE and UMAP allow visualizing high-dimensional data and therewith facilitate the discovery of interesting structure. Although they are widely used, they visualize data as is, rather than in light of the background knowledge we have about the data. What we already know, however, strongly determines what is novel and hence interesting. In this paper we propose two methods for factoring out prior knowledge in the form of distance matrices from low-dimensional embeddings. To factor out prior knowledge from tSNE embeddings, we propose JEDI that adapts the tSNE objective in a principled way using Jensen-Shannon divergence. To factor out prior knowledge from any downstream embedding approach, we propose CONFETTI, in which we directly operate on the input distance matrices. Extensive experiments on both synthetic and real world data show that both methods work well, providing embeddings that exhibit meaningful structure that would otherwise remain hidden. %K Computer Science, Learning, cs.LG,Statistics, Machine Learning, stat.ML
[39]
V. T. Ho, K. Pal, and G. Weikum, “QuTE: Answering Quantity Queries from Web Tables,” in SIGMOD ’21, International Conference on Management of Data, Xi’an, Shaanxi, China, 2021.
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@inproceedings{Thinh_SIG21, TITLE = {Qu{TE}: {A}nswering Quantity Queries from Web Tables}, AUTHOR = {Ho, Vinh Thinh and Pal, Koninika and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-8343-1}, DOI = {10.1145/3448016.3452763}, PUBLISHER = {ACM}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {SIGMOD '21, International Conference on Management of Data}, EDITOR = {Li, Guoliang and Li, Zhanhuai and Idreos, Stratos and Srivastava, Divesh}, PAGES = {2740--2744}, ADDRESS = {Xi'an, Shaanxi, China}, }
Endnote
%0 Conference Proceedings %A Ho, Vinh Thinh %A Pal, Koninika %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 QuTE: Answering Quantity Queries from Web Tables : %G eng %U http://hdl.handle.net/21.11116/0000-0008-052E-0 %R 10.1145/3448016.3452763 %D 2021 %B International Conference on Management of Data %Z date of event: 2021-06-19 - 2021-06-25 %C Xi'an, Shaanxi, China %B SIGMOD '21 %E Li, Guoliang; Li, Zhanhuai; Idreos, Stratos; Srivastava, Divesh %P 2740 - 2744 %I ACM %@ 978-1-4503-8343-1
[40]
V. T. Ho, K. Pal, S. Razniewski, K. Berberich, and G. Weikum, “Extracting Contextualized Quantity Facts from Web Tables,” in The Web Conference 2021 (WWW 2021), Ljubljana, Slovenia, 2021.
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@inproceedings{Thinh_WWW21, TITLE = {Extracting Contextualized Quantity Facts from Web Tables}, AUTHOR = {Ho, Vinh Thinh and Pal, Koninika and Razniewski, Simon and Berberich, Klaus and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-8312-7}, DOI = {10.1145/3442381.3450072}, PUBLISHER = {ACM}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {The Web Conference 2021 (WWW 2021)}, EDITOR = {Leskovec, Jure and Grobelnik, Marko and Najork, Mark and Tang, Jie and Zia, Leila}, PAGES = {4033--4042}, ADDRESS = {Ljubljana, Slovenia}, }
Endnote
%0 Conference Proceedings %A Ho, Vinh Thinh %A Pal, Koninika %A Razniewski, Simon %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 Extracting Contextualized Quantity Facts from Web Tables : %G eng %U http://hdl.handle.net/21.11116/0000-0008-04A0-E %R 10.1145/3442381.3450072 %D 2021 %B 30th The Web Conference %Z date of event: 2021-04-19 - 2021-04-23 %C Ljubljana, Slovenia %B The Web Conference 2021 %E Leskovec, Jure; Grobelnik, Marko; Najork, Mark; Tang, Jie; Zia, Leila %P 4033 - 4042 %I ACM %@ 978-1-4503-8312-7
[41]
K. Hui and K. Berberich, “Transitivity, Time Consumption, and Quality of Preference Judgments in Crowdsourcing,” 2021. [Online]. Available: https://arxiv.org/abs/2104.08926. (arXiv: 2104.08926)
Abstract
Preference judgments have been demonstrated as a better alternative to graded judgments to assess the relevance of documents relative to queries. Existing work has verified transitivity among preference judgments when collected from trained judges, which reduced the number of judgments dramatically. Moreover, strict preference judgments and weak preference judgments, where the latter additionally allow judges to state that two documents are equally relevant for a given query, are both widely used in literature. However, whether transitivity still holds when collected from crowdsourcing, i.e., whether the two kinds of preference judgments behave similarly remains unclear. In this work, we collect judgments from multiple judges using a crowdsourcing platform and aggregate them to compare the two kinds of preference judgments in terms of transitivity, time consumption, and quality. That is, we look into whether aggregated judgments are transitive, how long it takes judges to make them, and whether judges agree with each other and with judgments from TREC. Our key findings are that only strict preference judgments are transitive. Meanwhile, weak preference judgments behave differently in terms of transitivity, time consumption, as well as of quality of judgment.
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@online{Hui2104.08926, TITLE = {Transitivity, Time Consumption, and Quality of Preference Judgments in Crowdsourcing}, AUTHOR = {Hui, Kai and Berberich, Klaus}, LANGUAGE = {eng}, URL = {https://arxiv.org/abs/2104.08926}, EPRINT = {2104.08926}, EPRINTTYPE = {arXiv}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Preference judgments have been demonstrated as a better alternative to graded judgments to assess the relevance of documents relative to queries. Existing work has verified transitivity among preference judgments when collected from trained judges, which reduced the number of judgments dramatically. Moreover, strict preference judgments and weak preference judgments, where the latter additionally allow judges to state that two documents are equally relevant for a given query, are both widely used in literature. However, whether transitivity still holds when collected from crowdsourcing, i.e., whether the two kinds of preference judgments behave similarly remains unclear. In this work, we collect judgments from multiple judges using a crowdsourcing platform and aggregate them to compare the two kinds of preference judgments in terms of transitivity, time consumption, and quality. That is, we look into whether aggregated judgments are transitive, how long it takes judges to make them, and whether judges agree with each other and with judgments from TREC. Our key findings are that only strict preference judgments are transitive. Meanwhile, weak preference judgments behave differently in terms of transitivity, time consumption, as well as of quality of judgment.}, }
Endnote
%0 Report %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/21.11116/0000-0009-651A-9 %U https://arxiv.org/abs/2104.08926 %D 2021 %X Preference judgments have been demonstrated as a better alternative to graded judgments to assess the relevance of documents relative to queries. Existing work has verified transitivity among preference judgments when collected from trained judges, which reduced the number of judgments dramatically. Moreover, strict preference judgments and weak preference judgments, where the latter additionally allow judges to state that two documents are equally relevant for a given query, are both widely used in literature. However, whether transitivity still holds when collected from crowdsourcing, i.e., whether the two kinds of preference judgments behave similarly remains unclear. In this work, we collect judgments from multiple judges using a crowdsourcing platform and aggregate them to compare the two kinds of preference judgments in terms of transitivity, time consumption, and quality. That is, we look into whether aggregated judgments are transitive, how long it takes judges to make them, and whether judges agree with each other and with judgments from TREC. Our key findings are that only strict preference judgments are transitive. Meanwhile, weak preference judgments behave differently in terms of transitivity, time consumption, as well as of quality of judgment. %K Computer Science, Information Retrieval, cs.IR
[42]
Z. Jia, S. Pramanik, R. Saha Roy, and G. Weikum, “Complex Temporal Question Answering on Knowledge Graphs,” 2021. [Online]. Available: https://arxiv.org/abs/2109.08935. (arXiv: 2109.08935)
Abstract
Question answering over knowledge graphs (KG-QA) is a vital topic in IR. Questions with temporal intent are a special class of practical importance, but have not received much attention in research. This work presents EXAQT, the first end-to-end system for answering complex temporal questions that have multiple entities and predicates, and associated temporal conditions. EXAQT answers natural language questions over KGs in two stages, one geared towards high recall, the other towards precision at top ranks. The first step computes question-relevant compact subgraphs within the KG, and judiciously enhances them with pertinent temporal facts, using Group Steiner Trees and fine-tuned BERT models. The second step constructs relational graph convolutional networks (R-GCNs) from the first step's output, and enhances the R-GCNs with time-aware entity embeddings and attention over temporal relations. We evaluate EXAQT on TimeQuestions, a large dataset of 16k temporal questions we compiled from a variety of general purpose KG-QA benchmarks. Results show that EXAQT outperforms three state-of-the-art systems for answering complex questions over KGs, thereby justifying specialized treatment of temporal QA.
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@online{Jia2109.08935, TITLE = {Complex Temporal Question Answering on Knowledge Graphs}, AUTHOR = {Jia, Zhen and Pramanik, Soumajit and Saha Roy, Rishiraj and Weikum, Gerhard}, LANGUAGE = {eng}, URL = {https://arxiv.org/abs/2109.08935}, EPRINT = {2109.08935}, EPRINTTYPE = {arXiv}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Question answering over knowledge graphs (KG-QA) is a vital topic in IR. Questions with temporal intent are a special class of practical importance, but have not received much attention in research. This work presents EXAQT, the first end-to-end system for answering complex temporal questions that have multiple entities and predicates, and associated temporal conditions. EXAQT answers natural language questions over KGs in two stages, one geared towards high recall, the other towards precision at top ranks. The first step computes question-relevant compact subgraphs within the KG, and judiciously enhances them with pertinent temporal facts, using Group Steiner Trees and fine-tuned BERT models. The second step constructs relational graph convolutional networks (R-GCNs) from the first step's output, and enhances the R-GCNs with time-aware entity embeddings and attention over temporal relations. We evaluate EXAQT on TimeQuestions, a large dataset of 16k temporal questions we compiled from a variety of general purpose KG-QA benchmarks. Results show that EXAQT outperforms three state-of-the-art systems for answering complex questions over KGs, thereby justifying specialized treatment of temporal QA.}, }
Endnote
%0 Report %A Jia, Zhen %A Pramanik, Soumajit %A Saha Roy, Rishiraj %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 Complex Temporal Question Answering on Knowledge Graphs : %G eng %U http://hdl.handle.net/21.11116/0000-0009-64F7-0 %U https://arxiv.org/abs/2109.08935 %D 2021 %X Question answering over knowledge graphs (KG-QA) is a vital topic in IR. Questions with temporal intent are a special class of practical importance, but have not received much attention in research. This work presents EXAQT, the first end-to-end system for answering complex temporal questions that have multiple entities and predicates, and associated temporal conditions. EXAQT answers natural language questions over KGs in two stages, one geared towards high recall, the other towards precision at top ranks. The first step computes question-relevant compact subgraphs within the KG, and judiciously enhances them with pertinent temporal facts, using Group Steiner Trees and fine-tuned BERT models. The second step constructs relational graph convolutional networks (R-GCNs) from the first step's output, and enhances the R-GCNs with time-aware entity embeddings and attention over temporal relations. We evaluate EXAQT on TimeQuestions, a large dataset of 16k temporal questions we compiled from a variety of general purpose KG-QA benchmarks. Results show that EXAQT outperforms three state-of-the-art systems for answering complex questions over KGs, thereby justifying specialized treatment of temporal QA. %K Computer Science, Information Retrieval, cs.IR,Computer Science, Computation and Language, cs.CL
[43]
K. M. Jose, T. Nguyen, S. MacAvaney, J. Dalton, and A. Yates, “DiffIR: Exploring Differences in Ranking Models’ Behavior,” in SIGIR ’21, 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, Canada, 2021.
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@inproceedings{Jose_SIGIR21, TITLE = {{DiffIR}: {E}xploring Differences in Ranking Models' Behavior}, AUTHOR = {Jose, Kevin Martin and Nguyen, Thong and MacAvaney, Sean and Dalton, Jeffrey and Yates, Andrew}, LANGUAGE = {eng}, ISBN = {978-1-4503-8037-9}, DOI = {10.1145/3404835.3462784}, PUBLISHER = {ACM}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {SIGIR '21, 44th International ACM SIGIR Conference on Research and Development in Information Retrieval}, EDITOR = {Diaz, Fernando and Shah, Chirag and Suel, Torsten and Castells, Pablo and Jones, Rosie and Sakai, Tetsuya and Bellog{\'i}n, Alejandro and Yushioka, Massaharu}, PAGES = {2595--2599}, ADDRESS = {Virtual Event, Canada}, }
Endnote
%0 Conference Proceedings %A Jose, Kevin Martin %A Nguyen, Thong %A MacAvaney, Sean %A Dalton, Jeffrey %A Yates, Andrew %+ 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 DiffIR: Exploring Differences in Ranking Models' Behavior : %G eng %U http://hdl.handle.net/21.11116/0000-0009-666D-B %R 10.1145/3404835.3462784 %D 2021 %B 44th International ACM SIGIR Conference on Research and Development in Information Retrieval %Z date of event: 2021-07-11 - 2021-07-15 %C Virtual Event, Canada %B SIGIR '21 %E Diaz, Fernando; Shah, Chirag; Suel, Torsten; Castells, Pablo; Jones, Rosie; Sakai, Tetsuya; Bellog&#237;n, Alejandro; Yushioka, Massaharu %P 2595 - 2599 %I ACM %@ 978-1-4503-8037-9
[44]
M. Kaiser, R. Saha Roy, and G. Weikum, “Reinforcement Learning from Reformulations in Conversational Question Answering over Knowledge Graphs,” in SIGIR ’21, 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, Canada, 2021.
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@inproceedings{kaiser2021reinforcement, TITLE = {Reinforcement Learning from Reformulations in~Conversational Question Answering over Knowledge Graphs}, AUTHOR = {Kaiser, Magdalena and Saha Roy, Rishiraj and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-8037-9}, DOI = {10.1145/3404835.3462859}, PUBLISHER = {ACM}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {SIGIR '21, 44th International ACM SIGIR Conference on Research and Development in Information Retrieval}, EDITOR = {Diaz, Fernando and Shah, Chirag and Suel, Torsten and Castells, Pablo and Jones, Rosie and Sakai, Tetsuya and Bellog{\'i}n, Alejandro and Yushioka, Massaharu}, PAGES = {459--469}, ADDRESS = {Virtual Event, Canada}, }
Endnote
%0 Conference Proceedings %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 Reinforcement Learning from Reformulations in&#160;Conversational Question Answering over Knowledge Graphs : %G eng %U http://hdl.handle.net/21.11116/0000-0008-513E-8 %R 10.1145/3404835.3462859 %D 2021 %B 44th International ACM SIGIR Conference on Research and Development in Information Retrieval %Z date of event: 2021-07-11 - 2021-07-15 %C Virtual Event, Canada %B SIGIR '21 %E Diaz, Fernando; Shah, Chirag; Suel, Torsten; Castells, Pablo; Jones, Rosie; Sakai, Tetsuya; Bellog&#237;n, Alejandro; Yushioka, Massaharu %P 459 - 469 %I ACM %@ 978-1-4503-8037-9
[45]
M. Kaiser, R. Saha Roy, and G. Weikum, “Reinforcement Learning from Reformulations in Conversational Question Answering over Knowledge Graphs,” 2021. [Online]. Available: https://arxiv.org/abs/2105.04850. (arXiv: 2105.04850)
Abstract
The rise of personal assistants has made conversational question answering (ConvQA) a very popular mechanism for user-system interaction. State-of-the-art methods for ConvQA over knowledge graphs (KGs) can only learn from crisp question-answer pairs found in popular benchmarks. In reality, however, such training data is hard to come by: users would rarely mark answers explicitly as correct or wrong. In this work, we take a step towards a more natural learning paradigm - from noisy and implicit feedback via question reformulations. A reformulation is likely to be triggered by an incorrect system response, whereas a new follow-up question could be a positive signal on the previous turn's answer. We present a reinforcement learning model, termed CONQUER, that can learn from a conversational stream of questions and reformulations. CONQUER models the answering process as multiple agents walking in parallel on the KG, where the walks are determined by actions sampled using a policy network. This policy network takes the question along with the conversational context as inputs and is trained via noisy rewards obtained from the reformulation likelihood. To evaluate CONQUER, we create and release ConvRef, a benchmark with about 11k natural conversations containing around 205k reformulations. Experiments show that CONQUER successfully learns to answer conversational questions from noisy reward signals, significantly improving over a state-of-the-art baseline.
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@online{Kaiser_2105.04850, TITLE = {Reinforcement Learning from Reformulations in Conversational Question Answering over Knowledge Graphs}, AUTHOR = {Kaiser, Magdalena and Saha Roy, Rishiraj and Weikum, Gerhard}, LANGUAGE = {eng}, URL = {https://arxiv.org/abs/2105.04850}, EPRINT = {2105.04850}, EPRINTTYPE = {arXiv}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, ABSTRACT = {The rise of personal assistants has made conversational question answering (ConvQA) a very popular mechanism for user-system interaction. State-of-the-art methods for ConvQA over knowledge graphs (KGs) can only learn from crisp question-answer pairs found in popular benchmarks. In reality, however, such training data is hard to come by: users would rarely mark answers explicitly as correct or wrong. In this work, we take a step towards a more natural learning paradigm -- from noisy and implicit feedback via question reformulations. A reformulation is likely to be triggered by an incorrect system response, whereas a new follow-up question could be a positive signal on the previous turn's answer. We present a reinforcement learning model, termed CONQUER, that can learn from a conversational stream of questions and reformulations. CONQUER models the answering process as multiple agents walking in parallel on the KG, where the walks are determined by actions sampled using a policy network. This policy network takes the question along with the conversational context as inputs and is trained via noisy rewards obtained from the reformulation likelihood. To evaluate CONQUER, we create and release ConvRef, a benchmark with about 11k natural conversations containing around 205k reformulations. Experiments show that CONQUER successfully learns to answer conversational questions from noisy reward signals, significantly improving over a state-of-the-art baseline.}, }
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 Reinforcement Learning from Reformulations in Conversational Question Answering over Knowledge Graphs : %G eng %U http://hdl.handle.net/21.11116/0000-0009-67C9-1 %U https://arxiv.org/abs/2105.04850 %D 2021 %X The rise of personal assistants has made conversational question answering (ConvQA) a very popular mechanism for user-system interaction. State-of-the-art methods for ConvQA over knowledge graphs (KGs) can only learn from crisp question-answer pairs found in popular benchmarks. In reality, however, such training data is hard to come by: users would rarely mark answers explicitly as correct or wrong. In this work, we take a step towards a more natural learning paradigm - from noisy and implicit feedback via question reformulations. A reformulation is likely to be triggered by an incorrect system response, whereas a new follow-up question could be a positive signal on the previous turn's answer. We present a reinforcement learning model, termed CONQUER, that can learn from a conversational stream of questions and reformulations. CONQUER models the answering process as multiple agents walking in parallel on the KG, where the walks are determined by actions sampled using a policy network. This policy network takes the question along with the conversational context as inputs and is trained via noisy rewards obtained from the reformulation likelihood. To evaluate CONQUER, we create and release ConvRef, a benchmark with about 11k natural conversations containing around 205k reformulations. Experiments show that CONQUER successfully learns to answer conversational questions from noisy reward signals, significantly improving over a state-of-the-art baseline. %K Computer Science, Information Retrieval, cs.IR,Computer Science, Computation and Language, cs.CL
[46]
J. Kalofolias, P. Welke, and J. Vreeken, “SUSAN: The Structural Similarity Random Walk Kernel,” in Proceedings of the SIAM International Conference on Data Mining (SDM 2021), Virtual Conference, 2021.
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@inproceedings{kalofolias:21:susan, TITLE = {{SUSAN}: The Structural Similarity Random Walk Kernel}, AUTHOR = {Kalofolias, Janis and Welke, Pascal and Vreeken, Jilles}, LANGUAGE = {eng}, ISBN = {978-1-61197-670-0}, DOI = {10.1137/1.9781611976700.34}, PUBLISHER = {SIAM}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of the SIAM International Conference on Data Mining (SDM 2021)}, EDITOR = {Demeniconi, Carlotta and Davidson, Ian}, PAGES = {298--306}, ADDRESS = {Virtual Conference}, }
Endnote
%0 Conference Proceedings %A Kalofolias, Janis %A Welke, Pascal %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 SUSAN: The Structural Similarity Random Walk Kernel : %G eng %U http://hdl.handle.net/21.11116/0000-0008-26C9-B %R 10.1137/1.9781611976700.34 %D 2021 %B SIAM International Conference on Data Mining %Z date of event: 2021-04-29 - 2021-05-01 %C Virtual Conference %B Proceedings of the SIAM International Conference on Data Mining %E Demeniconi, Carlotta; Davidson, Ian %P 298 - 306 %I SIAM %@ 978-1-61197-670-0
[47]
M. Kamp, J. Fischer, and J. Vreeken, “Federated Learning from Small Datasets,” 2021. [Online]. Available: https://arxiv.org/abs/2110.03469. (arXiv: 2110.03469)
Abstract
Federated learning allows multiple parties to collaboratively train a joint model without sharing local data. This enables applications of machine learning in settings of inherently distributed, undisclosable data such as in the medical domain. In practice, joint training is usually achieved by aggregating local models, for which local training objectives have to be in expectation similar to the joint (global) objective. Often, however, local datasets are so small that local objectives differ greatly from the global objective, resulting in federated learning to fail. We propose a novel approach that intertwines model aggregations with permutations of local models. The permutations expose each local model to a daisy chain of local datasets resulting in more efficient training in data-sparse domains. This enables training on extremely small local datasets, such as patient data across hospitals, while retaining the training efficiency and privacy benefits of federated learning.
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@online{Kamp2110.03469, TITLE = {Federated Learning from Small Datasets}, AUTHOR = {Kamp, Michael and Fischer, Jonas and Vreeken, Jilles}, LANGUAGE = {eng}, URL = {https://arxiv.org/abs/2110.03469}, EPRINT = {2110.03469}, EPRINTTYPE = {arXiv}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Federated learning allows multiple parties to collaboratively train a joint model without sharing local data. This enables applications of machine learning in settings of inherently distributed, undisclosable data such as in the medical domain. In practice, joint training is usually achieved by aggregating local models, for which local training objectives have to be in expectation similar to the joint (global) objective. Often, however, local datasets are so small that local objectives differ greatly from the global objective, resulting in federated learning to fail. We propose a novel approach that intertwines model aggregations with permutations of local models. The permutations expose each local model to a daisy chain of local datasets resulting in more efficient training in data-sparse domains. This enables training on extremely small local datasets, such as patient data across hospitals, while retaining the training efficiency and privacy benefits of federated learning.}, }
Endnote
%0 Report %A Kamp, Michael %A Fischer, Jonas %A Vreeken, Jilles %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Federated Learning from Small Datasets : %G eng %U http://hdl.handle.net/21.11116/0000-0009-653B-4 %U https://arxiv.org/abs/2110.03469 %D 2021 %X Federated learning allows multiple parties to collaboratively train a joint model without sharing local data. This enables applications of machine learning in settings of inherently distributed, undisclosable data such as in the medical domain. In practice, joint training is usually achieved by aggregating local models, for which local training objectives have to be in expectation similar to the joint (global) objective. Often, however, local datasets are so small that local objectives differ greatly from the global objective, resulting in federated learning to fail. We propose a novel approach that intertwines model aggregations with permutations of local models. The permutations expose each local model to a daisy chain of local datasets resulting in more efficient training in data-sparse domains. This enables training on extremely small local datasets, such as patient data across hospitals, while retaining the training efficiency and privacy benefits of federated learning. %K Computer Science, Learning, cs.LG,Computer Science, Artificial Intelligence, cs.AI,Computer Science, Distributed, Parallel, and Cluster Computing, cs.DC
[48]
P. Lahoti, K. Gummadi, and G. Weikum, “Detecting and Mitigating Test-time Failure Risks via Model-agnostic Uncertainty Learning,” 2021. [Online]. Available: https://arxiv.org/abs/2109.04432. (arXiv: 2109.04432)
Abstract
Reliably predicting potential failure risks of machine learning (ML) systems when deployed with production data is a crucial aspect of trustworthy AI. This paper introduces Risk Advisor, a novel post-hoc meta-learner for estimating failure risks and predictive uncertainties of any already-trained black-box classification model. In addition to providing a risk score, the Risk Advisor decomposes the uncertainty estimates into aleatoric and epistemic uncertainty components, thus giving informative insights into the sources of uncertainty inducing the failures. Consequently, Risk Advisor can distinguish between failures caused by data variability, data shifts and model limitations and advise on mitigation actions (e.g., collecting more data to counter data shift). Extensive experiments on various families of black-box classification models and on real-world and synthetic datasets covering common ML failure scenarios show that the Risk Advisor reliably predicts deployment-time failure risks in all the scenarios, and outperforms strong baselines.
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@online{Lahoti2109.04432, TITLE = {Detecting and Mitigating Test-time Failure Risks via Model-agnostic Uncertainty Learning}, AUTHOR = {Lahoti, Preethi and Gummadi, Krishna and Weikum, Gerhard}, LANGUAGE = {eng}, URL = {https://arxiv.org/abs/2109.04432}, EPRINT = {2109.04432}, EPRINTTYPE = {arXiv}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Reliably predicting potential failure risks of machine learning (ML) systems when deployed with production data is a crucial aspect of trustworthy AI. This paper introduces Risk Advisor, a novel post-hoc meta-learner for estimating failure risks and predictive uncertainties of any already-trained black-box classification model. In addition to providing a risk score, the Risk Advisor decomposes the uncertainty estimates into aleatoric and epistemic uncertainty components, thus giving informative insights into the sources of uncertainty inducing the failures. Consequently, Risk Advisor can distinguish between failures caused by data variability, data shifts and model limitations and advise on mitigation actions (e.g., collecting more data to counter data shift). Extensive experiments on various families of black-box classification models and on real-world and synthetic datasets covering common ML failure scenarios show that the Risk Advisor reliably predicts deployment-time failure risks in all the scenarios, and outperforms strong baselines.}, }
Endnote
%0 Report %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 Detecting and Mitigating Test-time Failure Risks via Model-agnostic Uncertainty Learning : %G eng %U http://hdl.handle.net/21.11116/0000-0009-6491-2 %U https://arxiv.org/abs/2109.04432 %D 2021 %X Reliably predicting potential failure risks of machine learning (ML) systems when deployed with production data is a crucial aspect of trustworthy AI. This paper introduces Risk Advisor, a novel post-hoc meta-learner for estimating failure risks and predictive uncertainties of any already-trained black-box classification model. In addition to providing a risk score, the Risk Advisor decomposes the uncertainty estimates into aleatoric and epistemic uncertainty components, thus giving informative insights into the sources of uncertainty inducing the failures. Consequently, Risk Advisor can distinguish between failures caused by data variability, data shifts and model limitations and advise on mitigation actions (e.g., collecting more data to counter data shift). Extensive experiments on various families of black-box classification models and on real-world and synthetic datasets covering common ML failure scenarios show that the Risk Advisor reliably predicts deployment-time failure risks in all the scenarios, and outperforms strong baselines. %K Computer Science, Learning, cs.LG,Computer Science, Information Retrieval, cs.IR,Statistics, Machine Learning, stat.ML
[49]
S. MacAvaney, A. Yates, S. Feldman, D. Downey, A. Cohan, and N. Goharian, “Simplified Data Wrangling with ir_datasets,” in SIGIR ’21, 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, Canada, 2021.
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@inproceedings{MacAvaney_SIGIR21, TITLE = {Simplified Data Wrangling with ir{\textunderscore}datasets}, AUTHOR = {MacAvaney, Sean and Yates, Andrew and Feldman, Sergey and Downey, Doug and Cohan, Arman and Goharian, Nazli}, LANGUAGE = {eng}, ISBN = {978-1-4503-8037-9}, DOI = {10.1145/3404835.3463254}, PUBLISHER = {ACM}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {SIGIR '21, 44th International ACM SIGIR Conference on Research and Development in Information Retrieval}, EDITOR = {Diaz, Fernando and Shah, Chirag and Suel, Torsten and Castells, Pablo and Jones, Rosie and Sakai, Tetsuya and Bellog{\'i}n, Alejandro and Yushioka, Massaharu}, PAGES = {2429--2436}, ADDRESS = {Virtual Event, Canada}, }
Endnote
%0 Conference Proceedings %A MacAvaney, Sean %A Yates, Andrew %A Feldman, Sergey %A Downey, Doug %A Cohan, Arman %A Goharian, Nazli %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations External Organizations %T Simplified Data Wrangling with ir_datasets : %G eng %U http://hdl.handle.net/21.11116/0000-0009-665F-B %R 10.1145/3404835.3463254 %D 2021 %B 44th International ACM SIGIR Conference on Research and Development in Information Retrieval %Z date of event: 2021-07-11 - 2021-07-15 %C Virtual Event, Canada %B SIGIR '21 %E Diaz, Fernando; Shah, Chirag; Suel, Torsten; Castells, Pablo; Jones, Rosie; Sakai, Tetsuya; Bellog&#237;n, Alejandro; Yushioka, Massaharu %P 2429 - 2436 %I ACM %@ 978-1-4503-8037-9
[50]
S. MacAvaney, A. Yates, S. Feldman, D. Downey, A. Cohan, and N. Goharian, “Simplified Data Wrangling with ir_datasets,” 2021. [Online]. Available: https://arxiv.org/abs/2103.02280. (arXiv: 2103.02280)
Abstract
Managing the data for Information Retrieval (IR) experiments can be challenging. Dataset documentation is scattered across the Internet and once one obtains a copy of the data, there are numerous different data formats to work with. Even basic formats can have subtle dataset-specific nuances that need to be considered for proper use. To help mitigate these challenges, we introduce a new robust and lightweight tool (ir_datasets) for acquiring, managing, and performing typical operations over datasets used in IR. We primarily focus on textual datasets used for ad-hoc search. This tool provides both a Python and command line interface to numerous IR datasets and benchmarks. To our knowledge, this is the most extensive tool of its kind. Integrations with popular IR indexing and experimentation toolkits demonstrate the tool's utility. We also provide documentation of these datasets through the ir_datasets catalog: https://ir-datasets.com/. The catalog acts as a hub for information on datasets used in IR, providing core information about what data each benchmark provides as well as links to more detailed information. We welcome community contributions and intend to continue to maintain and grow this tool.
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@online{MacAvaney_2103.02280, TITLE = {Simplified Data Wrangling with ir{\textunderscore}datasets}, AUTHOR = {MacAvaney, Sean and Yates, Andrew and Feldman, Sergey and Downey, Doug and Cohan, Arman and Goharian, Nazli}, LANGUAGE = {eng}, URL = {https://arxiv.org/abs/2103.02280}, EPRINT = {2103.02280}, EPRINTTYPE = {arXiv}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Managing the data for Information Retrieval (IR) experiments can be challenging. Dataset documentation is scattered across the Internet and once one obtains a copy of the data, there are numerous different data formats to work with. Even basic formats can have subtle dataset-specific nuances that need to be considered for proper use. To help mitigate these challenges, we introduce a new robust and lightweight tool (ir_datasets) for acquiring, managing, and performing typical operations over datasets used in IR. We primarily focus on textual datasets used for ad-hoc search. This tool provides both a Python and command line interface to numerous IR datasets and benchmarks. To our knowledge, this is the most extensive tool of its kind. Integrations with popular IR indexing and experimentation toolkits demonstrate the tool's utility. We also provide documentation of these datasets through the ir_datasets catalog: https://ir-datasets.com/. The catalog acts as a hub for information on datasets used in IR, providing core information about what data each benchmark provides as well as links to more detailed information. We welcome community contributions and intend to continue to maintain and grow this tool.}, }
Endnote
%0 Report %A MacAvaney, Sean %A Yates, Andrew %A Feldman, Sergey %A Downey, Doug %A Cohan, Arman %A Goharian, Nazli %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations External Organizations %T Simplified Data Wrangling with ir_datasets : %G eng %U http://hdl.handle.net/21.11116/0000-0009-6679-D %U https://arxiv.org/abs/2103.02280 %D 2021 %X Managing the data for Information Retrieval (IR) experiments can be challenging. Dataset documentation is scattered across the Internet and once one obtains a copy of the data, there are numerous different data formats to work with. Even basic formats can have subtle dataset-specific nuances that need to be considered for proper use. To help mitigate these challenges, we introduce a new robust and lightweight tool (ir_datasets) for acquiring, managing, and performing typical operations over datasets used in IR. We primarily focus on textual datasets used for ad-hoc search. This tool provides both a Python and command line interface to numerous IR datasets and benchmarks. To our knowledge, this is the most extensive tool of its kind. Integrations with popular IR indexing and experimentation toolkits demonstrate the tool's utility. We also provide documentation of these datasets through the ir_datasets catalog: https://ir-datasets.com/. The catalog acts as a hub for information on datasets used in IR, providing core information about what data each benchmark provides as well as links to more detailed information. We welcome community contributions and intend to continue to maintain and grow this tool. %K Computer Science, Information Retrieval, cs.IR,Computer Science, Computation and Language, cs.CL
[51]
I. Mackie, J. Dalton, and A. Yates, “How Deep is your Learning: the DL-HARD Annotated Deep Learning Dataset,” 2021. [Online]. Available: https://arxiv.org/abs/2105.07975. (arXiv: 2105.07975)
Abstract
Deep Learning Hard (DL-HARD) is a new annotated dataset designed to more effectively evaluate neural ranking models on complex topics. It builds on TREC Deep Learning (DL) topics by extensively annotating them with question intent categories, answer types, wikified entities, topic categories, and result type metadata from a commercial web search engine. Based on this data, we introduce a framework for identifying challenging queries. DL-HARD contains fifty topics from the official DL 2019/2020 evaluation benchmark, half of which are newly and independently assessed. We perform experiments using the official submitted runs to DL on DL-HARD and find substantial differences in metrics and the ranking of participating systems. Overall, DL-HARD is a new resource that promotes research on neural ranking methods by focusing on challenging and complex topics.
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@online{Mackie_2105.07975, TITLE = {How Deep is your Learning: the {DL}-{HARD} Annotated Deep Learning Dataset}, AUTHOR = {Mackie, Iain and Dalton, Jeffery and Yates, Andrew}, LANGUAGE = {eng}, URL = {https://arxiv.org/abs/2105.07975}, EPRINT = {2105.07975}, EPRINTTYPE = {arXiv}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Deep Learning Hard (DL-HARD) is a new annotated dataset designed to more effectively evaluate neural ranking models on complex topics. It builds on TREC Deep Learning (DL) topics by extensively annotating them with question intent categories, answer types, wikified entities, topic categories, and result type metadata from a commercial web search engine. Based on this data, we introduce a framework for identifying challenging queries. DL-HARD contains fifty topics from the official DL 2019/2020 evaluation benchmark, half of which are newly and independently assessed. We perform experiments using the official submitted runs to DL on DL-HARD and find substantial differences in metrics and the ranking of participating systems. Overall, DL-HARD is a new resource that promotes research on neural ranking methods by focusing on challenging and complex topics.}, }
Endnote
%0 Report %A Mackie, Iain %A Dalton, Jeffery %A Yates, Andrew %+ External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T How Deep is your Learning: the DL-HARD Annotated Deep Learning Dataset : %G eng %U http://hdl.handle.net/21.11116/0000-0009-67AB-3 %U https://arxiv.org/abs/2105.07975 %D 2021 %X Deep Learning Hard (DL-HARD) is a new annotated dataset designed to more effectively evaluate neural ranking models on complex topics. It builds on TREC Deep Learning (DL) topics by extensively annotating them with question intent categories, answer types, wikified entities, topic categories, and result type metadata from a commercial web search engine. Based on this data, we introduce a framework for identifying challenging queries. DL-HARD contains fifty topics from the official DL 2019/2020 evaluation benchmark, half of which are newly and independently assessed. We perform experiments using the official submitted runs to DL on DL-HARD and find substantial differences in metrics and the ranking of participating systems. Overall, DL-HARD is a new resource that promotes research on neural ranking methods by focusing on challenging and complex topics. %K Computer Science, Information Retrieval, cs.IR
[52]
I. Mackie, J. Dalton, and A. Yates, “How Deep is your Learning: the DL-HARD Annotated Deep Learning Dataset,” in SIGIR ’21, 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, Canada, 2021.
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@inproceedings{Mackie_SIGIR21, TITLE = {How Deep is your Learning: the {DL}-{HARD} Annotated Deep Learning Dataset}, AUTHOR = {Mackie, Iain and Dalton, Jeffrey and Yates, Andrew}, LANGUAGE = {eng}, ISBN = {978-1-4503-8037-9}, DOI = {10.1145/3404835.3463262}, PUBLISHER = {ACM}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {SIGIR '21, 44th International ACM SIGIR Conference on Research and Development in Information Retrieval}, EDITOR = {Diaz, Fernando and Shah, Chirag and Suel, Torsten and Castells, Pablo and Jones, Rosie and Sakai, Tetsuya and Bellog{\'i}n, Alejandro and Yushioka, Massaharu}, PAGES = {2335--2341}, ADDRESS = {Virtual Event, Canada}, }
Endnote
%0 Conference Proceedings %A Mackie, Iain %A Dalton, Jeffrey %A Yates, Andrew %+ External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T How Deep is your Learning: the DL-HARD Annotated Deep Learning Dataset : %G eng %U http://hdl.handle.net/21.11116/0000-0009-6640-C %R 10.1145/3404835.3463262 %D 2021 %B 44th International ACM SIGIR Conference on Research and Development in Information Retrieval %Z date of event: 2021-07-11 - 2021-07-15 %C Virtual Event, Canada %B SIGIR '21 %E Diaz, Fernando; Shah, Chirag; Suel, Torsten; Castells, Pablo; Jones, Rosie; Sakai, Tetsuya; Bellog&#237;n, Alejandro; Yushioka, Massaharu %P 2335 - 2341 %I ACM %@ 978-1-4503-8037-9
[53]
P. Mandros, “Discovering robust dependencies from data,” Universität des Saarlandes, Saarbrücken, 2021.
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@phdthesis{Panphd2020, TITLE = {Discovering robust dependencies from data}, AUTHOR = {Mandros, Panagiotis}, LANGUAGE = {eng}, DOI = {10.22028/D291-34291}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, DATE = {2021}, }
Endnote
%0 Thesis %A Mandros, Panagiotis %Y Vreeken, Jilles %A referee: Weikum, Gerhard %A referee: Webb, Geoffrey %+ 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 Discovering robust dependencies from data : %G eng %U http://hdl.handle.net/21.11116/0000-0008-E4CF-E %R 10.22028/D291-34291 %I Universit&#228;t des Saarlandes %C Saarbr&#252;cken %D 2021 %P 194 p. %V phd %9 phd %U https://publikationen.sulb.uni-saarland.de/handle/20.500.11880/31535
[54]
A. Marx, “Information-Theoretic Causal Discovery,” Universität des Saarlandes, Saarbrücken, 2021.
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@phdthesis{Marxphd2020, TITLE = {Information-Theoretic Causal Discovery}, AUTHOR = {Marx, Alexander}, LANGUAGE = {eng}, DOI = {10.22028/D291-34290}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, DATE = {2021}, }
Endnote
%0 Thesis %A Marx, Alexander %Y Vreeken, Jilles %A referee: Weikum, Gerhard %A referee: Ommen, Thijs van %+ 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 Information-Theoretic Causal Discovery : %G eng %U http://hdl.handle.net/21.11116/0000-0008-EECA-9 %R 10.22028/D291-34290 %I Universit&#228;t des Saarlandes %C Saarbr&#252;cken %D 2021 %P 195 p. %V phd %9 phd %U https://publikationen.sulb.uni-saarland.de/handle/20.500.11880/31480
[55]
A. Marx, A. Gretton, and J. M. Mooij, “A Weaker Faithfulness Assumption based on Triple Interactions,” 2021. [Online]. Available: https://arxiv.org/abs/2010.14265. (arXiv: 2010.14265)
Abstract
One of the core assumptions in causal discovery is the faithfulness assumption---i.e. assuming that independencies found in the data are due to separations in the true causal graph. This assumption can, however, be violated in many ways, including xor connections, deterministic functions or cancelling paths. In this work, we propose a weaker assumption that we call 2-adjacency faithfulness. In contrast to adjacency faithfulness, which assumes that there is no conditional independence between each pair of variables that are connected in the causal graph, we only require no conditional independence between a node and a subset of its Markov blanket that can contain up to two nodes. Equivalently, we adapt orientation faithfulness to this setting. We further propose a sound orientation rule for causal discovery that applies under weaker assumptions. As a proof of concept, we derive a modified Grow and Shrink algorithm that recovers the Markov blanket of a target node and prove its correctness under strictly weaker assumptions than the standard faithfulness assumption.
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@online{Marxarxiv21, TITLE = {A Weaker Faithfulness Assumption based on Triple Interactions}, AUTHOR = {Marx, Alexander and Gretton, Arthur and Mooij, Joris M.}, LANGUAGE = {eng}, URL = {https://arxiv.org/abs/2010.14265}, EPRINT = {2010.14265}, EPRINTTYPE = {arXiv}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, ABSTRACT = {One of the core assumptions in causal discovery is the faithfulness assumption---i.e. assuming that independencies found in the data are due to separations in the true causal graph. This assumption can, however, be violated in many ways, including xor connections, deterministic functions or cancelling paths. In this work, we propose a weaker assumption that we call 2-adjacency faithfulness. In contrast to adjacency faithfulness, which assumes that there is no conditional independence between each pair of variables that are connected in the causal graph, we only require no conditional independence between a node and a subset of its Markov blanket that can contain up to two nodes. Equivalently, we adapt orientation faithfulness to this setting. We further propose a sound orientation rule for causal discovery that applies under weaker assumptions. As a proof of concept, we derive a modified Grow and Shrink algorithm that recovers the Markov blanket of a target node and prove its correctness under strictly weaker assumptions than the standard faithfulness assumption.}, }
Endnote
%0 Report %A Marx, Alexander %A Gretton, Arthur %A Mooij, Joris M. %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T A Weaker Faithfulness Assumption based on Triple Interactions : %G eng %U http://hdl.handle.net/21.11116/0000-0008-0BCE-5 %U https://arxiv.org/abs/2010.14265 %D 2021 %X One of the core assumptions in causal discovery is the faithfulness assumption---i.e. assuming that independencies found in the data are due to separations in the true causal graph. This assumption can, however, be violated in many ways, including xor connections, deterministic functions or cancelling paths. In this work, we propose a weaker assumption that we call 2-adjacency faithfulness. In contrast to adjacency faithfulness, which assumes that there is no conditional independence between each pair of variables that are connected in the causal graph, we only require no conditional independence between a node and a subset of its Markov blanket that can contain up to two nodes. Equivalently, we adapt orientation faithfulness to this setting. We further propose a sound orientation rule for causal discovery that applies under weaker assumptions. As a proof of concept, we derive a modified Grow and Shrink algorithm that recovers the Markov blanket of a target node and prove its correctness under strictly weaker assumptions than the standard faithfulness assumption. %K Statistics, Machine Learning, stat.ML,Computer Science, Artificial Intelligence, cs.AI,Computer Science, Learning, cs.LG
[56]
A. Marx, L. Yang, and M. van Leeuwen, “Estimating Conditional Mutual Information for Discrete-Continuous Mixtures using Multidimensional Adaptive Histograms,” in Proceedings of the SIAM International Conference on Data Mining (SDM 2021), Virtual Conference, 2021.
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@inproceedings{marx:20:myl, TITLE = {Estimating Conditional Mutual Information for Discrete-Continuous Mixtures using Multidimensional Adaptive Histograms}, AUTHOR = {Marx, Alexander and Yang, Lincen and van Leeuwen, Matthijs}, LANGUAGE = {eng}, ISBN = {978-1-61197-670-0}, DOI = {10.1137/1.9781611976700.44}, PUBLISHER = {SIAM}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of the SIAM International Conference on Data Mining (SDM 2021)}, PAGES = {387--395}, ADDRESS = {Virtual Conference}, }
Endnote
%0 Conference Proceedings %A Marx, Alexander %A Yang, Lincen %A van Leeuwen, Matthijs %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T Estimating Conditional Mutual Information for Discrete-Continuous Mixtures using Multidimensional Adaptive Histograms : %G eng %U http://hdl.handle.net/21.11116/0000-0008-0BC7-C %R 10.1137/1.9781611976700.44 %D 2021 %B SIAM International Conference on Data Mining %Z date of event: 2021-04-29 - 2021-05-01 %C Virtual Conference %B Proceedings of the SIAM International Conference on Data Mining %P 387 - 395 %I SIAM %@ 978-1-61197-670-0
[57]
A. Marx and J. Fischer, “Estimating Mutual Information via Geodesic kNN,” 2021. [Online]. Available: https://arxiv.org/abs/2110.13883. (arXiv: 2110.13883)
Abstract
Estimating mutual information (MI) between two continuous random variables $X$ and $Y$ allows to capture non-linear dependencies between them, non-parametrically. As such, MI estimation lies at the core of many data science applications. Yet, robustly estimating MI for high-dimensional $X$ and $Y$ is still an open research question. In this paper, we formulate this problem through the lens of manifold learning. That is, we leverage the common assumption that the information of $X$ and $Y$ is captured by a low-dimensional manifold embedded in the observed high-dimensional space and transfer it to MI estimation. As an extension to state-of-the-art $k$NN estimators, we propose to determine the $k$-nearest neighbours via geodesic distances on this manifold rather than form the ambient space, which allows us to estimate MI even in the high-dimensional setting. An empirical evaluation of our method, G-KSG, against the state-of-the-art shows that it yields good estimations of the MI in classical benchmark, and manifold tasks, even for high dimensional datasets, which none of the existing methods can provide.
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@online{Marx_arXiv2110.13883, TITLE = {{Estimating Mutual Information via Geodesic $k$NN}}, AUTHOR = {Marx, Alexander and Fischer, Jonas}, LANGUAGE = {eng}, URL = {https://arxiv.org/abs/2110.13883}, EPRINT = {2110.13883}, EPRINTTYPE = {arXiv}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Estimating mutual information (MI) between two continuous random variables $X$ and $Y$ allows to capture non-linear dependencies between them, non-parametrically. As such, MI estimation lies at the core of many data science applications. Yet, robustly estimating MI for high-dimensional $X$ and $Y$ is still an open research question. In this paper, we formulate this problem through the lens of manifold learning. That is, we leverage the common assumption that the information of $X$ and $Y$ is captured by a low-dimensional manifold embedded in the observed high-dimensional space and transfer it to MI estimation. As an extension to state-of-the-art $k$NN estimators, we propose to determine the $k$-nearest neighbours via geodesic distances on this manifold rather than form the ambient space, which allows us to estimate MI even in the high-dimensional setting. An empirical evaluation of our method, G-KSG, against the state-of-the-art shows that it yields good estimations of the MI in classical benchmark, and manifold tasks, even for high dimensional datasets, which none of the existing methods can provide.}, }
Endnote
%0 Report %A Marx, Alexander %A Fischer, Jonas %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Estimating Mutual Information via Geodesic kNN : %G eng %U http://hdl.handle.net/21.11116/0000-0009-B130-8 %U https://arxiv.org/abs/2110.13883 %D 2021 %X Estimating mutual information (MI) between two continuous random variables $X$ and $Y$ allows to capture non-linear dependencies between them, non-parametrically. As such, MI estimation lies at the core of many data science applications. Yet, robustly estimating MI for high-dimensional $X$ and $Y$ is still an open research question. In this paper, we formulate this problem through the lens of manifold learning. That is, we leverage the common assumption that the information of $X$ and $Y$ is captured by a low-dimensional manifold embedded in the observed high-dimensional space and transfer it to MI estimation. As an extension to state-of-the-art $k$NN estimators, we propose to determine the $k$-nearest neighbours via geodesic distances on this manifold rather than form the ambient space, which allows us to estimate MI even in the high-dimensional setting. An empirical evaluation of our method, G-KSG, against the state-of-the-art shows that it yields good estimations of the MI in classical benchmark, and manifold tasks, even for high dimensional datasets, which none of the existing methods can provide. %K Computer Science, Information Theory, cs.IT,Mathematics, Information Theory, math.IT
[58]
O. Mian, A. Marx, and J. Vreeken, “Discovering Fully Oriented Causal Networks,” in Thirty-Fifth AAAI Conference on Artificial Intelligence, Vancouver, Canada. (Accepted/in press)
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@inproceedings{mian:20:globe, TITLE = {Discovering Fully Oriented Causal Networks}, AUTHOR = {Mian, Osman and Marx, Alexander and Vreeken, Jilles}, LANGUAGE = {eng}, PUBLISHER = {AAAI}, YEAR = {2021}, PUBLREMARK = {Accepted}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Thirty-Fifth AAAI Conference on Artificial Intelligence}, ADDRESS = {Vancouver, Canada}, }
Endnote
%0 Conference Proceedings %A Mian, Osman %A Marx, Alexander %A Vreeken, Jilles %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Discovering Fully Oriented Causal Networks : %G eng %U http://hdl.handle.net/21.11116/0000-0008-0BCB-8 %D 2021 %B The Thirty-Fifth Conference on Artificial Intelligence %Z date of event: 2021-02-02 - 2021-02-09 %C Vancouver, Canada %B Thirty-Fifth AAAI Conference on Artificial Intelligence %I AAAI
[59]
P. Mirza, M. Abouhamra, and G. Weikum, “AligNarr: Aligning Narratives on Movies,” in The 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2019), Virtual, 2021.
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@inproceedings{Mirza_ACL-short.54, TITLE = {{AligNarr}: {A}ligning Narratives on Movies}, AUTHOR = {Mirza, Paramita and Abouhamra, Mostafa and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-954085-53-4}, URL = {https://aclanthology.org/2021.acl-short.54}, DOI = {10.18653/v1/2021.acl-short.54}, PUBLISHER = {ACL}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {The 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2019)}, EDITOR = {Xia, Fei and Li, Wenjie and Navigli, Roberto}, PAGES = {427--433}, ADDRESS = {Virtual}, }
Endnote
%0 Conference Proceedings %A Mirza, Paramita %A Abouhamra, Mostafa %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 AligNarr: Aligning Narratives on Movies : %G eng %U http://hdl.handle.net/21.11116/0000-0009-4A1F-3 %U https://aclanthology.org/2021.acl-short.54 %R 10.18653/v1/2021.acl-short.54 %D 2021 %B The 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing %Z date of event: 2021-08-01 - 2021-08-06 %C Virtual %B The 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing %E Xia, Fei; Li, Wenjie; Navigli, Roberto %P 427 - 433 %I ACL %@ 978-1-954085-53-4
[60]
S. Nag Chowdhury, “Exploiting Image-Text Synergy for Contextual Image Captioning,” in LANTERN 2021, The First Workshop Beyond Vision and LANguage: inTEgrating Real-world kNowledge, Virtual. (Accepted/in press)
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@inproceedings{Chod_ECAL2021, TITLE = {Exploiting Image-Text Synergy for Contextual Image Captioning}, AUTHOR = {Nag Chowdhury, Sreyasi}, LANGUAGE = {eng}, YEAR = {2021}, PUBLREMARK = {Accepted}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {LANTERN 2021, The First Workshop Beyond Vision and LANguage: inTEgrating Real-world kNowledge}, ADDRESS = {Virtual}, }
Endnote
%0 Conference Proceedings %A Nag Chowdhury, Sreyasi %+ Databases and Information Systems, MPI for Informatics, Max Planck Society %T Exploiting Image-Text Synergy for Contextual Image Captioning : %G eng %U http://hdl.handle.net/21.11116/0000-0008-0E60-D %D 2021 %B The First Workshop Beyond Vision and LANguage: inTEgrating Real-world kNowledge %Z date of event: 2021-04-20 - 2021-04-20 %C Virtual %B LANTERN 2021
[61]
S. Nag Chowdhury, “Towards Leveraging Commonsense Knowledge for Autonomous Driving,” in The Semantic Web -- ISWC 2021, Virtual Conference. (Accepted/in press)
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@inproceedings{NagChowdhury_ISWC2021, TITLE = {Towards Leveraging Commonsense Knowledge for Autonomous Driving}, AUTHOR = {Nag Chowdhury, Sreyasi}, LANGUAGE = {eng}, PUBLISHER = {Springer}, YEAR = {2021}, PUBLREMARK = {Accepted}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {The Semantic Web -- ISWC 2021}, SERIES = {Lecture Notes in Computer Science}, ADDRESS = {Virtual Conference}, }
Endnote
%0 Conference Proceedings %A Nag Chowdhury, Sreyasi %+ Databases and Information Systems, MPI for Informatics, Max Planck Society %T Towards Leveraging Commonsense Knowledge for Autonomous Driving : %G eng %U http://hdl.handle.net/21.11116/0000-0009-42CD-6 %D 2021 %B 20th International Semantic Web Conference %Z date of event: 2021-10-24 - 2021-10-28 %C Virtual Conference %B The Semantic Web -- ISWC 2021 %I Springer %B Lecture Notes in Computer Science
[62]
S. Nag Chowdhury, S. Razniewski, and G. Weikum, “SANDI: Story-and-Images Alignment,” in The 16th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2021), Online, 2021.
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@inproceedings{Thinh_EACL21, TITLE = {{SANDI}: {S}tory-and-Images Alignment}, AUTHOR = {Nag Chowdhury, Sreyasi and Razniewski, Simon and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-954085-02-2}, URL = {https://aclanthology.org/2021.eacl-main.85}, PUBLISHER = {ACL}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {The 16th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2021)}, EDITOR = {Merlo, Paola and Tiedemann, Jorg and Tsarfaty, Reut}, PAGES = {989--999}, ADDRESS = {Online}, }
Endnote
%0 Conference Proceedings %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 SANDI: Story-and-Images Alignment : %G eng %U http://hdl.handle.net/21.11116/0000-0008-04A2-C %U https://aclanthology.org/2021.eacl-main.85 %D 2021 %B 16th Conference of the European Chapter of the Association for Computational Linguistics %Z date of event: 2021-04-19 - 2021-04-23 %C Online %B The 16th Conference of the European Chapter of the Association for Computational Linguistics %E Merlo, Paola; Tiedemann, Jorg; Tsarfaty, Reut %P 989 - 999 %I ACL %@ 978-1-954085-02-2
[63]
S. Naseri, J. Dalton, A. Yates, and J. Allan, “CEQE: Contextualized Embeddings for Query Expansion,” 2021. [Online]. Available: https://arxiv.org/abs/2103.05256. (arXiv: 2103.05256)
Abstract
In this work we leverage recent advances in context-sensitive language models to improve the task of query expansion. Contextualized word representation models, such as ELMo and BERT, are rapidly replacing static embedding models. We propose a new model, Contextualized Embeddings for Query Expansion (CEQE), that utilizes query-focused contextualized embedding vectors. We study the behavior of contextual representations generated for query expansion in ad-hoc document retrieval. We conduct our experiments on probabilistic retrieval models as well as in combination with neural ranking models. We evaluate CEQE on two standard TREC collections: Robust and Deep Learning. We find that CEQE outperforms static embedding-based expansion methods on multiple collections (by up to 18% on Robust and 31% on Deep Learning on average precision) and also improves over proven probabilistic pseudo-relevance feedback (PRF) models. We further find that multiple passes of expansion and reranking result in continued gains in effectiveness with CEQE-based approaches outperforming other approaches. The final model incorporating neural and CEQE-based expansion score achieves gains of up to 5% in P@20 and 2% in AP on Robust over the state-of-the-art transformer-based re-ranking model, Birch.
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@online{Naseri_2103.05256, TITLE = {{CEQE}: Contextualized Embeddings for Query Expansion}, AUTHOR = {Naseri, Shahrzad and Dalton, Jeffrey and Yates, Andrew and Allan, James}, LANGUAGE = {eng}, URL = {https://arxiv.org/abs/2103.05256}, EPRINT = {2103.05256}, EPRINTTYPE = {arXiv}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, ABSTRACT = {In this work we leverage recent advances in context-sensitive language models to improve the task of query expansion. Contextualized word representation models, such as ELMo and BERT, are rapidly replacing static embedding models. We propose a new model, Contextualized Embeddings for Query Expansion (CEQE), that utilizes query-focused contextualized embedding vectors. We study the behavior of contextual representations generated for query expansion in ad-hoc document retrieval. We conduct our experiments on probabilistic retrieval models as well as in combination with neural ranking models. We evaluate CEQE on two standard TREC collections: Robust and Deep Learning. We find that CEQE outperforms static embedding-based expansion methods on multiple collections (by up to 18% on Robust and 31% on Deep Learning on average precision) and also improves over proven probabilistic pseudo-relevance feedback (PRF) models. We further find that multiple passes of expansion and reranking result in continued gains in effectiveness with CEQE-based approaches outperforming other approaches. The final model incorporating neural and CEQE-based expansion score achieves gains of up to 5% in P@20 and 2% in AP on Robust over the state-of-the-art transformer-based re-ranking model, Birch.}, }
Endnote
%0 Report %A Naseri, Shahrzad %A Dalton, Jeffrey %A Yates, Andrew %A Allan, James %+ External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T CEQE: Contextualized Embeddings for Query Expansion : %G eng %U http://hdl.handle.net/21.11116/0000-0009-6779-C %U https://arxiv.org/abs/2103.05256 %D 2021 %X In this work we leverage recent advances in context-sensitive language models to improve the task of query expansion. Contextualized word representation models, such as ELMo and BERT, are rapidly replacing static embedding models. We propose a new model, Contextualized Embeddings for Query Expansion (CEQE), that utilizes query-focused contextualized embedding vectors. We study the behavior of contextual representations generated for query expansion in ad-hoc document retrieval. We conduct our experiments on probabilistic retrieval models as well as in combination with neural ranking models. We evaluate CEQE on two standard TREC collections: Robust and Deep Learning. We find that CEQE outperforms static embedding-based expansion methods on multiple collections (by up to 18% on Robust and 31% on Deep Learning on average precision) and also improves over proven probabilistic pseudo-relevance feedback (PRF) models. We further find that multiple passes of expansion and reranking result in continued gains in effectiveness with CEQE-based approaches outperforming other approaches. The final model incorporating neural and CEQE-based expansion score achieves gains of up to 5% in P@20 and 2% in AP on Robust over the state-of-the-art transformer-based re-ranking model, Birch. %K Computer Science, Information Retrieval, cs.IR
[64]
S. Naseri, J. Dalton, A. Yates, and J. Allan, “CEQE: Contextualized Embeddings for Query Expansion,” in Advances in Information Retrieval (ECIR 2021), Lucca, Italy (Online Event), 2021.
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@inproceedings{Naseri_ECIR2021, TITLE = {{CEQE}: {C}ontextualized Embeddings for Query Expansion}, AUTHOR = {Naseri, Shahrzad and Dalton, Jeff and Yates, Andrew and Allan, James}, LANGUAGE = {eng}, ISBN = {978-3-030-72112-1}, DOI = {10.1007/978-3-030-72113-8_31}, PUBLISHER = {Springer}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, DATE = {2021}, BOOKTITLE = {Advances in Information Retrieval (ECIR 2021)}, EDITOR = {Hiemstra, Djoerd and Moens, Marie-Francine and Mothe, Josiane and Perego, Raffaele and Potthast, Martin and Sebastiani, Fabrizio}, PAGES = {467--482}, SERIES = {Lecture Notes in Computer Science}, VOLUME = {12656}, ADDRESS = {Lucca, Italy (Online Event)}, }
Endnote
%0 Conference Proceedings %A Naseri, Shahrzad %A Dalton, Jeff %A Yates, Andrew %A Allan, James %+ External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T CEQE: Contextualized Embeddings for Query Expansion : %G eng %U http://hdl.handle.net/21.11116/0000-0009-6628-8 %R 10.1007/978-3-030-72113-8_31 %D 2021 %B 43rd European Conference on IR Research %Z date of event: 2021-03-28 - 2021-04-01 %C Lucca, Italy (Online Event) %B Advances in Information Retrieval %E Hiemstra, Djoerd; Moens, Marie-Francine; Mothe, Josiane; Perego, Raffaele; Potthast, Martin; Sebastiani, Fabrizio %P 467 - 482 %I Springer %@ 978-3-030-72112-1 %B Lecture Notes in Computer Science %N 12656
[65]
T.-P. Nguyen, S. Razniewski, and G. Weikum, “Advanced Semantics for Commonsense Knowledge Extraction,” in The Web Conference 2021 (WWW 2021), Ljubljana, Slovenia, 2021.
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@inproceedings{Nguyen_WWW21, TITLE = {Advanced Semantics for Commonsense Knowledge Extraction}, AUTHOR = {Nguyen, Tuan-Phong and Razniewski, Simon and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-8312-7}, DOI = {10.1145/3442381.3449827}, PUBLISHER = {ACM}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {The Web Conference 2021 (WWW 2021)}, EDITOR = {Leskovec, Jure and Grobelnik, Marko and Najork, Marc and Tang, Jie and Zia, Leila}, PAGES = {2636--2647}, ADDRESS = {Ljubljana, Slovenia}, }
Endnote
%0 Conference Proceedings %A Nguyen, Tuan-Phong %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 Advanced Semantics for Commonsense Knowledge Extraction : %G eng %U http://hdl.handle.net/21.11116/0000-0008-0196-D %R 10.1145/3442381.3449827 %D 2021 %B 30th The Web Conference %Z date of event: 2021-04-30 - %C Ljubljana, Slovenia %B The Web Conference 2021 %E Leskovec, Jure; Grobelnik, Marko; Najork, Marc; Tang, Jie; Zia, Leila %P 2636 - 2647 %I ACM %@ 978-1-4503-8312-7
[66]
T.-P. Nguyen, S. Razniewski, and G. Weikum, “Inside ASCENT: Exploring a Deep Commonsense Knowledge Base and its Usage in Question Answering,” 2021. [Online]. Available: https://arxiv.org/abs/2105.13662. (arXiv: 2105.13662)
Abstract
ASCENT is a fully automated methodology for extracting and consolidating commonsense assertions from web contents (Nguyen et al., WWW 2021). It advances traditional triple-based commonsense knowledge representation by capturing semantic facets like locations and purposes, and composite concepts, i.e., subgroups and related aspects of subjects. In this demo, we present a web portal that allows users to understand its construction process, explore its content, and observe its impact in the use case of question answering. The demo website and an introductory video are both available online.
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@online{Nguyen_2105.13662, TITLE = {Inside {ASCENT}: {E}xploring a Deep Commonsense Knowledge Base and its Usage in Question Answering}, AUTHOR = {Nguyen, Tuan-Phong and Razniewski, Simon and Weikum, Gerhard}, LANGUAGE = {eng}, URL = {https://arxiv.org/abs/2105.13662}, EPRINT = {2105.13662}, EPRINTTYPE = {arXiv}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, ABSTRACT = {ASCENT is a fully automated methodology for extracting and consolidating commonsense assertions from web contents (Nguyen et al., WWW 2021). It advances traditional triple-based commonsense knowledge representation by capturing semantic facets like locations and purposes, and composite concepts, i.e., subgroups and related aspects of subjects. In this demo, we present a web portal that allows users to understand its construction process, explore its content, and observe its impact in the use case of question answering. The demo website and an introductory video are both available online.}, }
Endnote
%0 Report %A Nguyen, Tuan-Phong %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 Inside ASCENT: Exploring a Deep Commonsense Knowledge Base and its Usage in Question Answering : %G eng %U http://hdl.handle.net/21.11116/0000-0009-4A2E-2 %U https://arxiv.org/abs/2105.13662 %D 2021 %X ASCENT is a fully automated methodology for extracting and consolidating commonsense assertions from web contents (Nguyen et al., WWW 2021). It advances traditional triple-based commonsense knowledge representation by capturing semantic facets like locations and purposes, and composite concepts, i.e., subgroups and related aspects of subjects. In this demo, we present a web portal that allows users to understand its construction process, explore its content, and observe its impact in the use case of question answering. The demo website and an introductory video are both available online. %K Computer Science, Artificial Intelligence, cs.AI,Computer Science, Computation and Language, cs.CL %U https://youtu.be/qMkJXqu_Yd4
[67]
S. Pramanik, J. Alabi, R. Saha Roy, and G. Weikum, “UNIQORN: Unified Question Answering over RDF Knowledge Graphs and Natural Language Text,” 2021. [Online]. Available: https://arxiv.org/abs/2108.08614. (arXiv: 2108.08614)
Abstract
Question answering over knowledge graphs and other RDF data has been greatly advanced, with a number of good systems providing crisp answers for natural language questions or telegraphic queries. Some of these systems incorporate textual sources as additional evidence for the answering process, but cannot compute answers that are present in text alone. Conversely, systems from the IR and NLP communities have addressed QA over text, but barely utilize semantic data and knowledge. This paper presents the first QA system that can seamlessly operate over RDF datasets and text corpora, or both together, in a unified framework. Our method, called UNIQORN, builds a context graph on the fly, by retrieving question-relevant triples from the RDF data and/or the text corpus, where the latter case is handled by automatic information extraction. The resulting graph is typically rich but highly noisy. UNIQORN copes with this input by advanced graph algorithms for Group Steiner Trees, that identify the best answer candidates in the context graph. Experimental results on several benchmarks of complex questions with multiple entities and relations, show that UNIQORN, an unsupervised method with only five parameters, produces results comparable to the state-of-the-art on KGs, text corpora, and heterogeneous sources. The graph-based methodology provides user-interpretable evidence for the complete answering process.
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@online{Pramanik_2108.08614, TITLE = {{UNIQORN}: {U}nified Question Answering over {RDF} Knowledge Graphs and Natural Language Text}, AUTHOR = {Pramanik, Soumajit and Alabi, Jesujoba and Saha Roy, Rishiraj and Weikum, Gerhard}, LANGUAGE = {eng}, URL = {https://arxiv.org/abs/2108.08614}, EPRINT = {2108.08614}, EPRINTTYPE = {arXiv}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Question answering over knowledge graphs and other RDF data has been greatly advanced, with a number of good systems providing crisp answers for natural language questions or telegraphic queries. Some of these systems incorporate textual sources as additional evidence for the answering process, but cannot compute answers that are present in text alone. Conversely, systems from the IR and NLP communities have addressed QA over text, but barely utilize semantic data and knowledge. This paper presents the first QA system that can seamlessly operate over RDF datasets and text corpora, or both together, in a unified framework. Our method, called UNIQORN, builds a context graph on the fly, by retrieving question-relevant triples from the RDF data and/or the text corpus, where the latter case is handled by automatic information extraction. The resulting graph is typically rich but highly noisy. UNIQORN copes with this input by advanced graph algorithms for Group Steiner Trees, that identify the best answer candidates in the context graph. Experimental results on several benchmarks of complex questions with multiple entities and relations, show that UNIQORN, an unsupervised method with only five parameters, produces results comparable to the state-of-the-art on KGs, text corpora, and heterogeneous sources. The graph-based methodology provides user-interpretable evidence for the complete answering process.}, }
Endnote
[68]
S. Razniewski, H. Arnaout, S. Ghosh, and F. Suchanek, “On the Limits of Machine Knowledge: Completeness, Recall and Negation in Web-scale Knowledge Bases,” Proceedings of the VLDB Endowment (Proc. VLDB 2021), vol. 14, no. 12, 2021.
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@article{Razniewski2021_PVLDB, TITLE = {On the Limits of Machine Knowledge: {C}ompleteness, Recall and Negation in Web-scale Knowledge Bases}, AUTHOR = {Razniewski, Simon and Arnaout, Hiba and Ghosh, Shrestha and Suchanek, Fabian}, LANGUAGE = {eng}, PUBLISHER = {VLDB Endowment Inc.}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, JOURNAL = {Proceedings of the VLDB Endowment (Proc. VLDB)}, VOLUME = {14}, NUMBER = {12}, PAGES = {3175--3177}, BOOKTITLE = {Proceedings of the 47th International Conference on Very Large Data Bases (VLDB 2021)}, EDITOR = {Dong, Xin Luna and Naumann, Felix}, }
Endnote
%0 Journal Article %A Razniewski, Simon %A Arnaout, Hiba %A Ghosh, Shrestha %A Suchanek, Fabian %+ 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 On the Limits of Machine Knowledge: Completeness, Recall and Negation in Web-scale Knowledge Bases : %G eng %U http://hdl.handle.net/21.11116/0000-0009-6544-9 %7 2021 %D 2021 %J Proceedings of the VLDB Endowment %O PVLDB %V 14 %N 12 %& 3175 %P 3175 - 3177 %I VLDB Endowment Inc. %B Proceedings of the 47th International Conference on Very Large Data Bases %O VLDB 2021 Copenhagen, Denmark, 16-20 August 2021
[69]
S. Razniewski, N. Tandon, and A. S. Varde, “Information to Wisdom: Commonsense Knowledge Extraction and Compilation,” in WSDM ’21, 14th International Conference on Web Search and Data Mining, Virtual Event, Israel, 2021.
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@inproceedings{Razniewski_WSDM21, TITLE = {Information to Wisdom: {C}ommonsense Knowledge Extraction and Compilation}, AUTHOR = {Razniewski, Simon and Tandon, Niket and Varde, Aparna S.}, LANGUAGE = {eng}, ISBN = {978-1-4503-8297-7}, DOI = {10.1145/3437963.3441664}, PUBLISHER = {ACM}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {WSDM '21, 14th International Conference on Web Search and Data Mining}, EDITOR = {Lewin-Eytan, Liane and Carmel, David and Yom-Tov, Elad and Agichtein, Eugene and Gabrilovich, Evgeniy}, PAGES = {1143--1146}, ADDRESS = {Virtual Event, Israel}, }
Endnote
%0 Conference Proceedings %A Razniewski, Simon %A Tandon, Niket %A Varde, Aparna S. %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Information to Wisdom: Commonsense Knowledge Extraction and Compilation : %G eng %U http://hdl.handle.net/21.11116/0000-0009-65FE-8 %R 10.1145/3437963.3441664 %D 2021 %B 14th International Conference on Web Search and Data Mining %Z date of event: 2021-03-08 - 2021-03-12 %C Virtual Event, Israel %B WSDM '21 %E Lewin-Eytan, Liane; Carmel, David; Yom-Tov, Elad; Agichtein, Eugene; Gabrilovich, Evgeniy %P 1143 - 1146 %I ACM %@ 978-1-4503-8297-7
[70]
S. Razniewski, A. Yates, N. Kassner, and G. Weikum, “Language Models As or For Knowledge Bases,” 2021. [Online]. Available: https://arxiv.org/abs/2110.04888. (arXiv: 2110.04888)
Abstract
Pre-trained language models (LMs) have recently gained attention for their potential as an alternative to (or proxy for) explicit knowledge bases (KBs). In this position paper, we examine this hypothesis, identify strengths and limitations of both LMs and KBs, and discuss the complementary nature of the two paradigms. In particular, we offer qualitative arguments that latent LMs are not suitable as a substitute for explicit KBs, but could play a major role for augmenting and curating KBs.
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@online{Razniewski_2110.04888, TITLE = {Language Models As or For Knowledge Bases}, AUTHOR = {Razniewski, Simon and Yates, Andrew and Kassner, Nora and Weikum, Gerhard}, LANGUAGE = {eng}, URL = {https://arxiv.org/abs/2110.04888}, EPRINT = {2110.04888}, EPRINTTYPE = {arXiv}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Pre-trained language models (LMs) have recently gained attention for their potential as an alternative to (or proxy for) explicit knowledge bases (KBs). In this position paper, we examine this hypothesis, identify strengths and limitations of both LMs and KBs, and discuss the complementary nature of the two paradigms. In particular, we offer qualitative arguments that latent LMs are not suitable as a substitute for explicit KBs, but could play a major role for augmenting and curating KBs.}, }
Endnote
%0 Report %A Razniewski, Simon %A Yates, Andrew %A Kassner, Nora %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 Language Models As or For Knowledge Bases : %G eng %U http://hdl.handle.net/21.11116/0000-0009-6510-3 %U https://arxiv.org/abs/2110.04888 %D 2021 %X Pre-trained language models (LMs) have recently gained attention for their potential as an alternative to (or proxy for) explicit knowledge bases (KBs). In this position paper, we examine this hypothesis, identify strengths and limitations of both LMs and KBs, and discuss the complementary nature of the two paradigms. In particular, we offer qualitative arguments that latent LMs are not suitable as a substitute for explicit KBs, but could play a major role for augmenting and curating KBs. %K Computer Science, Computation and Language, cs.CL,Computer Science, Artificial Intelligence, cs.AI,Computer Science, Databases, cs.DB
[71]
S. Razniewski, “Commonsense Knowledge Base Construction in the Age of Big Data,” 2021. [Online]. Available: https://arxiv.org/abs/2105.01925. (arXiv: 2105.01925)
Abstract
Compiling commonsense knowledge is traditionally an AI topic approached by manual labor. Recent advances in web data processing have enabled automated approaches. In this demonstration we will showcase three systems for automated commonsense knowledge base construction, highlighting each time one aspect of specific interest to the data management community. (i) We use Quasimodo to illustrate knowledge extraction systems engineering, (ii) Dice to illustrate the role that schema constraints play in cleaning fuzzy commonsense knowledge, and (iii) Ascent to illustrate the relevance of conceptual modelling. The demos are available online at https://quasimodo.r2.enst.fr, https://dice.mpi-inf.mpg.de and ascent.mpi-inf.mpg.de.
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@online{Razniewski_2105.01925, TITLE = {Commonsense Knowledge Base Construction in the Age of Big Data}, AUTHOR = {Razniewski, Simon}, LANGUAGE = {eng}, URL = {https://arxiv.org/abs/2105.01925}, EPRINT = {2105.01925}, EPRINTTYPE = {arXiv}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Compiling commonsense knowledge is traditionally an AI topic approached by manual labor. Recent advances in web data processing have enabled automated approaches. In this demonstration we will showcase three systems for automated commonsense knowledge base construction, highlighting each time one aspect of specific interest to the data management community. (i) We use Quasimodo to illustrate knowledge extraction systems engineering, (ii) Dice to illustrate the role that schema constraints play in cleaning fuzzy commonsense knowledge, and (iii) Ascent to illustrate the relevance of conceptual modelling. The demos are available online at https://quasimodo.r2.enst.fr, https://dice.mpi-inf.mpg.de and ascent.mpi-inf.mpg.de.}, }
Endnote
%0 Report %A Razniewski, Simon %+ Databases and Information Systems, MPI for Informatics, Max Planck Society %T Commonsense Knowledge Base Construction in the Age of Big Data : %G eng %U http://hdl.handle.net/21.11116/0000-0009-6604-0 %U https://arxiv.org/abs/2105.01925 %D 2021 %X Compiling commonsense knowledge is traditionally an AI topic approached by manual labor. Recent advances in web data processing have enabled automated approaches. In this demonstration we will showcase three systems for automated commonsense knowledge base construction, highlighting each time one aspect of specific interest to the data management community. (i) We use Quasimodo to illustrate knowledge extraction systems engineering, (ii) Dice to illustrate the role that schema constraints play in cleaning fuzzy commonsense knowledge, and (iii) Ascent to illustrate the relevance of conceptual modelling. The demos are available online at https://quasimodo.r2.enst.fr, https://dice.mpi-inf.mpg.de and ascent.mpi-inf.mpg.de. %K Computer Science, Artificial Intelligence, cs.AI,Computer Science, Computation and Language, cs.CL,Computer Science, Databases, cs.DB
[72]
J. Romero, “Pyformlang: An Educational Library for Formal Language Manipulation,” in SIGCSE ’21, The 52nd ACM Technical Symposium on Computer Science Education, Virtual Event, USA. (Accepted/in press)
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@inproceedings{Romero_SIGCSE21, TITLE = {Pyformlang: {An} Educational Library for Formal Language Manipulation}, AUTHOR = {Romero, Julien}, LANGUAGE = {eng}, YEAR = {2021}, PUBLREMARK = {Accepted}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {SIGCSE '21, The 52nd ACM Technical Symposium on Computer Science Education}, ADDRESS = {Virtual Event, USA}, }
Endnote
%0 Conference Proceedings %A Romero, Julien %+ Databases and Information Systems, MPI for Informatics, Max Planck Society %T Pyformlang: An Educational Library for Formal Language Manipulation : %G eng %U http://hdl.handle.net/21.11116/0000-0007-F836-5 %D 2021 %B The 52nd ACM Technical Symposium on Computer Science Education %Z date of event: 2021-03-13 - 2021-03-20 %C Virtual Event, USA %B SIGCSE '21
[73]
R. Saha Roy and A. Anand, Question Answering for the Curated Web: Tasks and Methods in QA over Knowledge Bases and Text Collections. San Rafael, CA: Morgan & Claypool, 2021.
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@book{SahaRoy2021, TITLE = {{Question Answering for the Curated Web: Tasks and Methods in QA over Knowledge Bases and Text Collections}}, AUTHOR = {Saha Roy, Rishiraj and Anand, Avishek}, LANGUAGE = {eng}, ISBN = {978-1636392387}, DOI = {10.2200/S0113ED1V01Y202109ICR076}, PUBLISHER = {Morgan \& Claypool}, ADDRESS = {San Rafael, CA}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, DATE = {2021}, PAGES = {194 p.}, SERIES = {Synthesis Lectures on Information Concepts, Retrieval, and Services}, }
Endnote
%0 Book %A Saha Roy, Rishiraj %A Anand, Avishek %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Question Answering for the Curated Web: Tasks and Methods in QA over Knowledge Bases and Text Collections : %G eng %U http://hdl.handle.net/21.11116/0000-0009-B116-6 %R 10.2200/S0113ED1V01Y202109ICR076 %@ 978-1636392387 %I Morgan & Claypool %C San Rafael, CA %D 2021 %P 194 p. %B Synthesis Lectures on Information Concepts, Retrieval, and Services
[74]
F. Schmidt, A. Marx, N. Baumgarten, M. Hebel, M. Wegner, M. Kaulich, M. S. Leisegang, R. P. Brandes, J. Göke, J. Vreeken, and M. H. Schulz, “Integrative Analysis of Epigenetics Data Identifies Gene-specific Regulatory Elements,” Nucleic Acids Research (London), vol. 49, no. 18, 2021.
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@article{Schmidt_NAR21, TITLE = {Integrative Analysis of Epigenetics Data Identifies Gene-specific Regulatory Elements}, AUTHOR = {Schmidt, Florian and Marx, Alexander and Baumgarten, Nina and Hebel, Marie and Wegner, Martin and Kaulich, Manuel and Leisegang, Matthias S. and Brandes, Ralf P and G{\"o}ke, Jonathan and Vreeken, Jilles and Schulz, Marcel Holger}, LANGUAGE = {eng}, ISSN = {0305-1048}, DOI = {10.1093/nar/gkab798}, PUBLISHER = {Oxford University Press}, ADDRESS = {Oxford}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, DATE = {2021}, JOURNAL = {Nucleic Acids Research (London)}, VOLUME = {49}, NUMBER = {18}, PAGES = {10397--10418}, }
Endnote
%0 Journal Article %A Schmidt, Florian %A Marx, Alexander %A Baumgarten, Nina %A Hebel, Marie %A Wegner, Martin %A Kaulich, Manuel %A Leisegang, Matthias S. %A Brandes, Ralf P %A G&#246;ke, Jonathan %A Vreeken, Jilles %A Schulz, Marcel Holger %+ Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society %T Integrative Analysis of Epigenetics Data Identifies Gene-specific Regulatory Elements : %G eng %U http://hdl.handle.net/21.11116/0000-0009-6D54-F %R 10.1093/nar/gkab798 %2 PMC8501997 %7 2021 %D 2021 %J Nucleic Acids Research (London) %O Nucleic Acids Res %V 49 %N 18 %& 10397 %P 10397 - 10418 %I Oxford University Press %C Oxford %@ false
[75]
X. Shen, “Deep Latent-Variable Models for Neural Text Generation,” Universität des Saarlandes, Saarbrücken, 2021.
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@phdthesis{Shenphd2021, TITLE = {Deep Latent-Variable Models for Neural Text Generation}, AUTHOR = {Shen, Xiaoyu}, LANGUAGE = {eng}, URL = {nbn:de:bsz:291--ds-350558}, DOI = {10.22028/D291-35055}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, DATE = {2021}, }
Endnote
%0 Thesis %A Shen, Xiaoyu %Y Klakow, Dietrich %A referee: Weikum, Gerhard %A referee: Sch&#252;tze, Hinrich %+ Databases and Information Systems, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Deep Latent-Variable Models for Neural Text Generation : %G eng %U http://hdl.handle.net/21.11116/0000-0009-B25D-6 %R 10.22028/D291-35055 %U nbn:de:bsz:291--ds-350558 %I Universit&#228;t des Saarlandes %C Saarbr&#252;cken %D 2021 %P 201 p. %V phd %9 phd %U https://publikationen.sulb.uni-saarland.de/handle/20.500.11880/32106
[76]
S. Singhania, S. Razniewski, and G. Weikum, “Predicting Document Coverage for Relation Extraction,” 2021. [Online]. Available: https://arxiv.org/abs/2111.13611. (arXiv: 2111.13611)
Abstract
This paper presents a new task of predicting the coverage of a text document for relation extraction (RE): does the document contain many relational tuples for a given entity? Coverage predictions are useful in selecting the best documents for knowledge base construction with large input corpora. To study this problem, we present a dataset of 31,366 diverse documents for 520 entities. We analyze the correlation of document coverage with features like length, entity mention frequency, Alexa rank, language complexity and information retrieval scores. Each of these features has only moderate predictive power. We employ methods combining features with statistical models like TF-IDF and language models like BERT. The model combining features and BERT, HERB, achieves an F1 score of up to 46%. We demonstrate the utility of coverage predictions on two use cases: KB construction and claim refutation.
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@online{Singhania2021, TITLE = {Predicting Document Coverage for Relation Extraction}, AUTHOR = {Singhania, Sneha and Razniewski, Simon and Weikum, Gerhard}, LANGUAGE = {eng}, URL = {https://arxiv.org/abs/2111.13611}, EPRINT = {2111.13611}, EPRINTTYPE = {arXiv}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, ABSTRACT = {This paper presents a new task of predicting the coverage of a text document for relation extraction (RE): does the document contain many relational tuples for a given entity? Coverage predictions are useful in selecting the best documents for knowledge base construction with large input corpora. To study this problem, we present a dataset of 31,366 diverse documents for 520 entities. We analyze the correlation of document coverage with features like length, entity mention frequency, Alexa rank, language complexity and information retrieval scores. Each of these features has only moderate predictive power. We employ methods combining features with statistical models like TF-IDF and language models like BERT. The model combining features and BERT, HERB, achieves an F1 score of up to 46%. We demonstrate the utility of coverage predictions on two use cases: KB construction and claim refutation.}, }
Endnote
%0 Report %A Singhania, Sneha %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 Predicting Document Coverage for Relation Extraction : %G eng %U http://hdl.handle.net/21.11116/0000-000A-237F-1 %U https://arxiv.org/abs/2111.13611 %D 2021 %X This paper presents a new task of predicting the coverage of a text document for relation extraction (RE): does the document contain many relational tuples for a given entity? Coverage predictions are useful in selecting the best documents for knowledge base construction with large input corpora. To study this problem, we present a dataset of 31,366 diverse documents for 520 entities. We analyze the correlation of document coverage with features like length, entity mention frequency, Alexa rank, language complexity and information retrieval scores. Each of these features has only moderate predictive power. We employ methods combining features with statistical models like TF-IDF and language models like BERT. The model combining features and BERT, HERB, achieves an F1 score of up to 46%. We demonstrate the utility of coverage predictions on two use cases: KB construction and claim refutation. %K Computer Science, Computation and Language, cs.CL,Computer Science, Artificial Intelligence, cs.AI
[77]
A. Tigunova, P. Mirza, A. Yates, and G. Weikum, “Exploring Personal Knowledge Extraction from Conversations with CHARM,” in WSDM ’21, 14th International Conference on Web Search and Data Mining, Virtual Event, Israel, 2021.
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@inproceedings{Tigunova_WSDM21, TITLE = {Exploring Personal Knowledge Extraction from Conversations with {CHARM}}, AUTHOR = {Tigunova, Anna and Mirza, Paramita and Yates, Andrew and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-8297-7}, DOI = {10.1145/3437963.3441699}, PUBLISHER = {ACM}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {WSDM '21, 14th International Conference on Web Search and Data Mining}, EDITOR = {Lewin-Eytan, Liane and Carmel, David and Yom-Tov, Elad and Agichtein, Eugene and Gabrilovich, Evgeniy}, PAGES = {1077--1080}, ADDRESS = {Virtual Event, Israel}, }
Endnote
%0 Conference Proceedings %A Tigunova, Anna %A Mirza, Paramita %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 Exploring Personal Knowledge Extraction from Conversations with CHARM : %G eng %U http://hdl.handle.net/21.11116/0000-0007-F850-7 %R 10.1145/3437963.3441699 %D 2021 %B 14th International Conference on Web Search and Data Mining %Z date of event: 2021-03-08 - 2021-03-12 %C Virtual Event, Israel %B WSDM '21 %E Lewin-Eytan, Liane; Carmel, David; Yom-Tov, Elad; Agichtein, Eugene; Gabrilovich, Evgeniy %P 1077 - 1080 %I ACM %@ 978-1-4503-8297-7
[78]
G. H. Torbati, A. Yates, and G. Weikum, “You Get What You Chat: Using Conversations to Personalize Search-based Recommendations,” in Advances in Information Retrieval (ECIR 2021), Lucca, Italy (Online Event), 2021.
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@inproceedings{Torbati_ECIR2021, TITLE = {You Get What You Chat: {U}sing Conversations to Personalize Search-based Recommendations}, AUTHOR = {Torbati, Ghazaleh Haratinezhad and Yates, Andrew and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-3-030-72112-1}, DOI = {10.1007/978-3-030-72113-8_14}, PUBLISHER = {Springer}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, DATE = {2021}, BOOKTITLE = {Advances in Information Retrieval (ECIR 2021)}, EDITOR = {Hiemstra, Djoerd and Moens, Marie-Francine and Mothe, Josiane and Perego, Raffaele and Potthast, Martin and Sebastiani, Fabrizio}, PAGES = {207--223}, SERIES = {Lecture Notes in Computer Science}, VOLUME = {12656}, ADDRESS = {Lucca, Italy (Online Event)}, }
Endnote
%0 Conference Proceedings %A Torbati, Ghazaleh Haratinezhad %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 %T You Get What You Chat: Using Conversations to Personalize Search-based Recommendations : %G eng %U http://hdl.handle.net/21.11116/0000-0007-ECA2-8 %R 10.1007/978-3-030-72113-8_14 %D 2021 %B 43rd European Conference on IR Research %Z date of event: 2021-03-28 - 2021-04-01 %C Lucca, Italy (Online Event) %B Advances in Information Retrieval %E Hiemstra, Djoerd; Moens, Marie-Francine; Mothe, Josiane; Perego, Raffaele; Potthast, Martin; Sebastiani, Fabrizio %P 207 - 223 %I Springer %@ 978-3-030-72112-1 %B Lecture Notes in Computer Science %N 12656
[79]
K. H. Tran, A. Ghazimatin, and R. Saha Roy, “Counterfactual Explanations for Neural Recommenders,” 2021. [Online]. Available: https://arxiv.org/abs/2105.05008. (arXiv: 2105.05008)
Abstract
Understanding why specific items are recommended to users can significantly increase their trust and satisfaction in the system. While neural recommenders have become the state-of-the-art in recent years, the complexity of deep models still makes the generation of tangible explanations for end users a challenging problem. Existing methods are usually based on attention distributions over a variety of features, which are still questionable regarding their suitability as explanations, and rather unwieldy to grasp for an end user. Counterfactual explanations based on a small set of the user's own actions have been shown to be an acceptable solution to the tangibility problem. However, current work on such counterfactuals cannot be readily applied to neural models. In this work, we propose ACCENT, the first general framework for finding counterfactual explanations for neural recommenders. It extends recently-proposed influence functions for identifying training points most relevant to a recommendation, from a single to a pair of items, while deducing a counterfactual set in an iterative process. We use ACCENT to generate counterfactual explanations for two popular neural models, Neural Collaborative Filtering (NCF) and Relational Collaborative Filtering (RCF), and demonstrate its feasibility on a sample of the popular MovieLens 100K dataset.
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@online{Tran_2105.05008, TITLE = {Counterfactual Explanations for Neural Recommenders}, AUTHOR = {Tran, Khanh Hiep and Ghazimatin, Azin and Saha Roy, Rishiraj}, LANGUAGE = {eng}, URL = {https://arxiv.org/abs/2105.05008}, EPRINT = {2105.05008}, EPRINTTYPE = {arXiv}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Understanding why specific items are recommended to users can significantly increase their trust and satisfaction in the system. While neural recommenders have become the state-of-the-art in recent years, the complexity of deep models still makes the generation of tangible explanations for end users a challenging problem. Existing methods are usually based on attention distributions over a variety of features, which are still questionable regarding their suitability as explanations, and rather unwieldy to grasp for an end user. Counterfactual explanations based on a small set of the user's own actions have been shown to be an acceptable solution to the tangibility problem. However, current work on such counterfactuals cannot be readily applied to neural models. In this work, we propose ACCENT, the first general framework for finding counterfactual explanations for neural recommenders. It extends recently-proposed influence functions for identifying training points most relevant to a recommendation, from a single to a pair of items, while deducing a counterfactual set in an iterative process. We use ACCENT to generate counterfactual explanations for two popular neural models, Neural Collaborative Filtering (NCF) and Relational Collaborative Filtering (RCF), and demonstrate its feasibility on a sample of the popular MovieLens 100K dataset.}, }
Endnote
%0 Report %A Tran, Khanh Hiep %A Ghazimatin, Azin %A 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 Counterfactual Explanations for Neural Recommenders : %G eng %U http://hdl.handle.net/21.11116/0000-0009-67C3-7 %U https://arxiv.org/abs/2105.05008 %D 2021 %X Understanding why specific items are recommended to users can significantly increase their trust and satisfaction in the system. While neural recommenders have become the state-of-the-art in recent years, the complexity of deep models still makes the generation of tangible explanations for end users a challenging problem. Existing methods are usually based on attention distributions over a variety of features, which are still questionable regarding their suitability as explanations, and rather unwieldy to grasp for an end user. Counterfactual explanations based on a small set of the user's own actions have been shown to be an acceptable solution to the tangibility problem. However, current work on such counterfactuals cannot be readily applied to neural models. In this work, we propose ACCENT, the first general framework for finding counterfactual explanations for neural recommenders. It extends recently-proposed influence functions for identifying training points most relevant to a recommendation, from a single to a pair of items, while deducing a counterfactual set in an iterative process. We use ACCENT to generate counterfactual explanations for two popular neural models, Neural Collaborative Filtering (NCF) and Relational Collaborative Filtering (RCF), and demonstrate its feasibility on a sample of the popular MovieLens 100K dataset. %K Computer Science, Information Retrieval, cs.IR,Computer Science, Learning, cs.LG
[80]
K. H. Tran, A. Ghazimatin, and R. Saha Roy, “Counterfactual Explanations for Neural Recommenders,” in SIGIR ’21, 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, Canada, 2021.
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@inproceedings{tran2021counterfactual, TITLE = {Counterfactual Explanations for Neural Recommenders}, AUTHOR = {Tran, Khanh Hiep and Ghazimatin, Azin and Saha Roy, Rishiraj}, LANGUAGE = {eng}, DOI = {10.1145/3404835.3463005}, PUBLISHER = {ACM}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {SIGIR '21, 44th International ACM SIGIR Conference on Research and Development in Information Retrieval}, EDITOR = {Diaz, Fernando and Shah, Chirag and Suel, Torsten and Castells, Pablo and Jones, Rosie and Sakai, Tetsuya and Bellogin, Alejandro and Yushioka, Massaharu}, PAGES = {1627--1631}, ADDRESS = {Virtual Event, Canada}, }
Endnote
%0 Conference Proceedings %A Tran, Khanh Hiep %A Ghazimatin, Azin %A 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 Counterfactual Explanations for Neural Recommenders : %G eng %U http://hdl.handle.net/21.11116/0000-0008-5140-4 %R 10.1145/3404835.3463005 %D 2021 %B 44th International ACM SIGIR Conference on Research and Development in Information Retrieval %Z date of event: 2021-07-11 - 2021-07-15 %C Virtual Event, Canada %B SIGIR '21 %E Diaz, Fernando; Shah, Chirag; Suel, Torsten; Castells, Pablo; Jones, Rosie; Sakai, Tetsuya; Bellogin, Alejandro; Yushioka, Massaharu %P 1627 - 1631 %I ACM
[81]
G. Weikum, “Knowledge Graphs 2021: A Data Odyssey,” Proceedings of the VLDB Endowment (Proc. VLDB 2021), vol. 14, no. 12, 2021.
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@article{Weikum2021_PVLDB, TITLE = {Knowledge Graphs 2021: {A} Data Odyssey}, AUTHOR = {Weikum, Gerhard}, LANGUAGE = {eng}, PUBLISHER = {VLDB Endowment Inc.}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, JOURNAL = {Proceedings of the VLDB Endowment (Proc. VLDB)}, VOLUME = {14}, NUMBER = {12}, PAGES = {3233--3238}, BOOKTITLE = {Proceedings of the 47th International Conference on Very Large Data Bases (VLDB 2021)}, EDITOR = {Dong, Xin Luna and Naumann, Felix}, }
Endnote
%0 Journal Article %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society %T Knowledge Graphs 2021: A Data Odyssey : %G eng %U http://hdl.handle.net/21.11116/0000-0009-631F-6 %7 2021 %D 2021 %J Proceedings of the VLDB Endowment %O PVLDB %V 14 %N 12 %& 3233 %P 3233 - 3238 %I VLDB Endowment Inc. %B Proceedings of the 47th International Conference on Very Large Data Bases %O VLDB 2021 Copenhagen, Denmark, 16-20 August 2021
[82]
G. Weikum, L. Dong, S. Razniewski, and F. Suchanek, “Machine Knowledge: Creation and Curation of Comprehensive Knowledge Bases,” Foundations and Trends in Databases, vol. 10, no. 2–4, 2021.
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@article{Weikum10.1561/1900000064, TITLE = {Machine Knowledge: {C}reation and Curation of Comprehensive Knowledge Bases}, AUTHOR = {Weikum, Gerhard and Dong, Luna and Razniewski, Simon and Suchanek, Fabian}, LANGUAGE = {eng}, ISSN = {1931-7883}, ISBN = {978-1-68083-836-7}, DOI = {10.1561/1900000064}, PUBLISHER = {Now Publishers}, ADDRESS = {Boston}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, DATE = {2021}, JOURNAL = {Foundations and Trends in Databases}, VOLUME = {10}, NUMBER = {2-4}, PAGES = {108--490}, }
Endnote
%0 Journal Article %A Weikum, Gerhard %A Dong, Luna %A Razniewski, Simon %A Suchanek, Fabian %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Machine Knowledge: Creation and Curation of Comprehensive Knowledge Bases : %G eng %U http://hdl.handle.net/21.11116/0000-0009-6317-E %R 10.1561/1900000064 %@ 978-1-68083-836-7 %7 2021 %D 2021 %J Foundations and Trends in Databases %V 10 %N 2-4 %& 108 %P 108 - 490 %I Now Publishers %C Boston %@ false
[83]
A. Yates, R. Nogueira, and J. Lin, “Pretrained Transformers for Text Ranking: BERT and Beyond,” in SIGIR ’21, 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, Canada, 2021.
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@inproceedings{Yates_SIGIR21, TITLE = {Pretrained Transformers for Text Ranking: {BERT} and Beyond}, AUTHOR = {Yates, Andrew and Nogueira, Rodrigo and Lin, Jimmy}, LANGUAGE = {eng}, ISBN = {978-1-4503-8037-9}, DOI = {10.1145/3404835.3462812}, PUBLISHER = {ACM}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {SIGIR '21, 44th International ACM SIGIR Conference on Research and Development in Information Retrieval}, EDITOR = {Diaz, Fernando and Shah, Chirag and Suel, Torsten and Castells, Pablo and Jones, Rosie and Sakai, Tetsuya and Bellog{\'i}n, Alejandro and Yushioka, Massaharu}, PAGES = {2666--2668}, ADDRESS = {Virtual Event, Canada}, }
Endnote
%0 Conference Proceedings %A Yates, Andrew %A Nogueira, Rodrigo %A Lin, Jimmy %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T Pretrained Transformers for Text Ranking: BERT and Beyond : %G eng %U http://hdl.handle.net/21.11116/0000-0009-6674-2 %R 10.1145/3404835.3462812 %D 2021 %B 44th International ACM SIGIR Conference on Research and Development in Information Retrieval %Z date of event: 2021-07-11 - 2021-07-15 %C Virtual Event, Canada %B SIGIR '21 %E Diaz, Fernando; Shah, Chirag; Suel, Torsten; Castells, Pablo; Jones, Rosie; Sakai, Tetsuya; Bellog&#237;n, Alejandro; Yushioka, Massaharu %P 2666 - 2668 %I ACM %@ 978-1-4503-8037-9
[84]
X. Zhang, A. Yates, and J. Lin, “Comparing Score Aggregation Approaches for Document Retrieval with Pretrained Transformers,” in Advances in Information Retrieval (ECIR 2021), Lucca, Italy (Online Event), 2021.
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@inproceedings{Torbati_ECIR2021, TITLE = {Comparing Score Aggregation Approaches for Document Retrieval with Pretrained Transformers}, AUTHOR = {Zhang, Xinyu and Yates, Andrew and Lin, Jimmy}, LANGUAGE = {eng}, ISBN = {978-3-030-72239-5}, DOI = {10.1007/978-3-030-72113-8_14}, PUBLISHER = {Springer}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, DATE = {2021}, BOOKTITLE = {Advances in Information Retrieval (ECIR 2021)}, EDITOR = {Hiemstra, Djoerd and Moens, Marie-Francine and Mothe, Josiane and Perego, Raffaele and Potthast, Martin and Sebastiani, Fabrizio}, PAGES = {150--163}, SERIES = {Lecture Notes in Computer Science}, VOLUME = {12657}, ADDRESS = {Lucca, Italy (Online Event)}, }
Endnote
%0 Conference Proceedings %A Zhang, Xinyu %A Yates, Andrew %A Lin, Jimmy %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Comparing Score Aggregation Approaches for Document Retrieval with Pretrained Transformers : %G eng %U http://hdl.handle.net/21.11116/0000-0009-6614-E %R 10.1007/978-3-030-72113-8_14 %D 2021 %B 43rd European Conference on IR Research %Z date of event: 2021-03-28 - 2021-04-01 %C Lucca, Italy (Online Event) %B Advances in Information Retrieval %E Hiemstra, Djoerd; Moens, Marie-Francine; Mothe, Josiane; Perego, Raffaele; Potthast, Martin; Sebastiani, Fabrizio %P 150 - 163 %I Springer %@ 978-3-030-72239-5 %B Lecture Notes in Computer Science %N 12657
[85]
X. Zhang, J. Xin, A. Yates, and J. Lin, “Bag-of-Words Baselines for Semantic Code Search,” in The 1st Workshop on Natural Language Processing for Programming (NLP4Prog 2021), Bangkog, Thailand (Online), 2021.
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@inproceedings{Zhang_NLP4Prog2021, TITLE = {Bag-of-Words Baselines for Semantic Code Search}, AUTHOR = {Zhang, Xinyu and Xin, Ji and Yates, Andrew and Lin, Jimmy}, LANGUAGE = {eng}, ISBN = {978-1-954085-64-0}, URL = {https://aclanthology.org/2021.nlp4prog-1.0}, PUBLISHER = {ACL}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {The 1st Workshop on Natural Language Processing for Programming (NLP4Prog 2021)}, EDITOR = {Lachmy, Royi and Yao, Ziyu and Durrett, Greg and Gligoric, Milos and Li, Junyi Jessy and Mooney, Ray and Neubig, Graham and Su, Yu and Sun, Huan and Tsarfaty, Reut}, PAGES = {88--94}, ADDRESS = {Bangkog, Thailand (Online)}, }
Endnote
%0 Conference Proceedings %A Zhang, Xinyu %A Xin, Ji %A Yates, Andrew %A Lin, Jimmy %+ External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Bag-of-Words Baselines for Semantic Code Search : %G eng %U http://hdl.handle.net/21.11116/0000-0009-619E-8 %U https://aclanthology.org/2021.nlp4prog-1.0 %D 2021 %B 1st Workshop on Natural Language Processing for Programming %Z date of event: 2021-08-06 - 2021-08-06 %C Bangkog, Thailand (Online) %B The 1st Workshop on Natural Language Processing for Programming %E Lachmy, Royi; Yao, Ziyu; Durrett, Greg; Gligoric, Milos; Li, Junyi Jessy; Mooney, Ray; Neubig, Graham; Su, Yu; Sun, Huan; Tsarfaty, Reut %P 88 - 94 %I ACL %@ 978-1-954085-64-0
[86]
Z. Zheng, K. Hui, B. He, X. Han, L. Sun, and A. Yates, “Contextualized Query Expansion via Unsupervised Chunk Selection for Text Retrieval,” Information Processing & Management, vol. 58, no. 5, 2021.
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@article{Zheng2021, TITLE = {Contextualized Query Expansion via Unsupervised Chunk Selection for Text Retrieval}, AUTHOR = {Zheng, Zhi and Hui, Kai and He, Ben and Han, Xianpei and Sun, Le and Yates, Andrew}, LANGUAGE = {eng}, ISSN = {0306-4573}, DOI = {10.1016/j.ipm.2021.102672}, PUBLISHER = {Elsevier}, ADDRESS = {Amsterdam}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, DATE = {2021}, JOURNAL = {Information Processing \& Management}, VOLUME = {58}, NUMBER = {5}, EID = {102672}, }
Endnote
%0 Journal Article %A Zheng, Zhi %A Hui, Kai %A He, Ben %A Han, Xianpei %A Sun, Le %A Yates, Andrew %+ External Organizations External Organizations External Organizations External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Contextualized Query Expansion via Unsupervised Chunk Selection for Text Retrieval : %G eng %U http://hdl.handle.net/21.11116/0000-0009-4747-8 %R 10.1016/j.ipm.2021.102672 %7 2021 %D 2021 %J Information Processing & Management %V 58 %N 5 %Z sequence number: 102672 %I Elsevier %C Amsterdam %@ false
2020
[87]
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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@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}, MARGINALMARK = {$\bullet$}, 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
[88]
H. Arnaout, S. Razniewski, and G. Weikum, “Enriching Knowledge Bases with Interesting Negative Statements,” in Automated Knowledge Base Construction (AKBC 2020), Virtual Conference, 2020.
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@inproceedings{Arnaout_AKBC2020, TITLE = {Enriching Knowledge Bases with Interesting Negative Statements}, AUTHOR = {Arnaout, Hiba and Razniewski, Simon and Weikum, Gerhard}, LANGUAGE = {eng}, DOI = {10.24432/C5101K}, PUBLISHER = {OpenReview}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Automated Knowledge Base Construction (AKBC 2020)}, ADDRESS = {Virtual Conference}, }
Endnote
%0 Conference Proceedings %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 Enriching Knowledge Bases with Interesting Negative Statements : %G eng %U http://hdl.handle.net/21.11116/0000-0007-EBC9-E %R 10.24432/C5101K %D 2020 %B 2nd Conference on Automated Knowledge Base Construction %Z date of event: 2020-06-22 - 2020-06-24 %C Virtual Conference %B Automated Knowledge Base Construction %I OpenReview %U https://openreview.net/forum?id=pSLmyZKaS
[89]
K. Balog, V. Setty, C. Lioma, Y. Liu, M. Zhang, and K. Berberich, Eds., ICTIR ’20. ACM, 2020.
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@proceedings{Balog_ICTIR20, TITLE = {ICTIR '20, ACM SIGIR International Conference on Theory of Information Retrieval}, EDITOR = {Balog, Krisztian and Setty, Vinay and Lioma, Christina and Liu, Yiqun and Zhang, Min and Berberich, Klaus}, LANGUAGE = {eng}, ISBN = {978-1-4503-8067-6}, DOI = {10.1145/3409256}, PUBLISHER = {ACM}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, ADDRESS = {Virtual Event, Norway}, }
Endnote
%0 Conference Proceedings %E Balog, Krisztian %E Setty, Vinay %E Lioma, Christina %E Liu, Yiqun %E Zhang, Min %E Berberich, Klaus %+ External Organizations External Organizations External Organizations External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T ICTIR '20 : Proceedings of the 2020 ACM SIGIR International Conference on Theory of Information Retrieval %G eng %U http://hdl.handle.net/21.11116/0000-0008-041D-4 %R 10.1145/3409256 %@ 978-1-4503-8067-6 %I ACM %D 2020 %B ACM SIGIR International Conference on Theory of Information Retrieval %Z date of event: 2020-09-14 - 2020-09-17 %D 2020 %C Virtual Event, Norway
[90]
C. Belth, X. Zheng, J. Vreeken, and D. Koutra, “What is Normal, What is Strange, and What is Missing in a Knowledge Graph: Unified Characterization via Inductive Summarization,” in Proceedings of The World Wide Web Conference (WWW 2020), Taipei, Taiwan, 2020.
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@inproceedings{belth:20:kgist, TITLE = {What is Normal, What is Strange, and What is Missing in a Knowledge Graph: {U}nified Characterization via Inductive Summarization}, AUTHOR = {Belth, Caleb and Zheng, Xinyi and Vreeken, Jilles and Koutra, Danai}, LANGUAGE = {eng}, ISBN = {978-1-4503-7023-3}, DOI = {10.1145/3366423.3380189}, PUBLISHER = {ACM}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of The World Wide Web Conference (WWW 2020)}, EDITOR = {Huang, Yennun and King, Irwin and Liu, Tie-Yan and van Steen, Maarten}, PAGES = {1115--1126}, ADDRESS = {Taipei, Taiwan}, }
Endnote
%0 Conference Proceedings %A Belth, Caleb %A Zheng, Xinyi %A Vreeken, Jilles %A Koutra, Danai %+ External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T What is Normal, What is Strange, and What is Missing in a Knowledge Graph: Unified Characterization via Inductive Summarization : %G eng %U http://hdl.handle.net/21.11116/0000-0008-253F-9 %R 10.1145/3366423.3380189 %D 2020 %B The World Wide Web Conference %Z date of event: 2020-04-20 - 2020-04-24 %C Taipei, Taiwan %B Proceedings of The World Wide Web Conference %E Huang, Yennun; King, Irwin; Liu, Tie-Yan; van Steen, Maarten %P 1115 - 1126 %I ACM %@ 978-1-4503-7023-3
[91]
J. J. Benjamin, C. Müller-Birn, and S. Razniewski, “Examining the Impact of Algorithm Awareness on Wikidata’s Recommender System Recoin,” 2020. [Online]. Available: https://arxiv.org/abs/2009.09049. (arXiv: 2009.09049)
Abstract
The global infrastructure of the Web, designed as an open and transparent system, has a significant impact on our society. However, algorithmic systems of corporate entities that neglect those principles increasingly populated the Web. Typical representatives of these algorithmic systems are recommender systems that influence our society both on a scale of global politics and during mundane shopping decisions. Recently, such recommender systems have come under critique for how they may strengthen existing or even generate new kinds of biases. To this end, designers and engineers are increasingly urged to make the functioning and purpose of recommender systems more transparent. Our research relates to the discourse of algorithm awareness, that reconsiders the role of algorithm visibility in interface design. We conducted online experiments with 105 participants using MTurk for the recommender system Recoin, a gadget for Wikidata. In these experiments, we presented users with one of a set of three different designs of Recoin's user interface, each of them exhibiting a varying degree of explainability and interactivity. Our findings include a positive correlation between comprehension of and trust in an algorithmic system in our interactive redesign. However, our results are not conclusive yet, and suggest that the measures of comprehension, fairness, accuracy and trust are not yet exhaustive for the empirical study of algorithm awareness. Our qualitative insights provide a first indication for further measures. Our study participants, for example, were less concerned with the details of understanding an algorithmic calculation than with who or what is judging the result of the algorithm.
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@online{Benjamin2009.09049, TITLE = {Examining the Impact of Algorithm Awareness on {W}ikidata's Recommender System Recoin}, AUTHOR = {Benjamin, Jesse Josua and M{\"u}ller-Birn, Claudia and Razniewski, Simon}, LANGUAGE = {eng}, URL = {https://arxiv.org/abs/2009.09049}, EPRINT = {2009.09049}, EPRINTTYPE = {arXiv}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, ABSTRACT = {The global infrastructure of the Web, designed as an open and transparent system, has a significant impact on our society. However, algorithmic systems of corporate entities that neglect those principles increasingly populated the Web. Typical representatives of these algorithmic systems are recommender systems that influence our society both on a scale of global politics and during mundane shopping decisions. Recently, such recommender systems have come under critique for how they may strengthen existing or even generate new kinds of biases. To this end, designers and engineers are increasingly urged to make the functioning and purpose of recommender systems more transparent. Our research relates to the discourse of algorithm awareness, that reconsiders the role of algorithm visibility in interface design. We conducted online experiments with 105 participants using MTurk for the recommender system Recoin, a gadget for Wikidata. In these experiments, we presented users with one of a set of three different designs of Recoin's user interface, each of them exhibiting a varying degree of explainability and interactivity. Our findings include a positive correlation between comprehension of and trust in an algorithmic system in our interactive redesign. However, our results are not conclusive yet, and suggest that the measures of comprehension, fairness, accuracy and trust are not yet exhaustive for the empirical study of algorithm awareness. Our qualitative insights provide a first indication for further measures. Our study participants, for example, were less concerned with the details of understanding an algorithmic calculation than with who or what is judging the result of the algorithm.}, }
Endnote
%0 Report %A Benjamin, Jesse Josua %A M&#252;ller-Birn, Claudia %A Razniewski, Simon %+ External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Examining the Impact of Algorithm Awareness on Wikidata's Recommender System Recoin : %G eng %U http://hdl.handle.net/21.11116/0000-0008-0661-4 %U https://arxiv.org/abs/2009.09049 %D 2020 %X The global infrastructure of the Web, designed as an open and transparent system, has a significant impact on our society. However, algorithmic systems of corporate entities that neglect those principles increasingly populated the Web. Typical representatives of these algorithmic systems are recommender systems that influence our society both on a scale of global politics and during mundane shopping decisions. Recently, such recommender systems have come under critique for how they may strengthen existing or even generate new kinds of biases. To this end, designers and engineers are increasingly urged to make the functioning and purpose of recommender systems more transparent. Our research relates to the discourse of algorithm awareness, that reconsiders the role of algorithm visibility in interface design. We conducted online experiments with 105 participants using MTurk for the recommender system Recoin, a gadget for Wikidata. In these experiments, we presented users with one of a set of three different designs of Recoin's user interface, each of them exhibiting a varying degree of explainability and interactivity. Our findings include a positive correlation between comprehension of and trust in an algorithmic system in our interactive redesign. However, our results are not conclusive yet, and suggest that the measures of comprehension, fairness, accuracy and trust are not yet exhaustive for the empirical study of algorithm awareness. Our qualitative insights provide a first indication for further measures. Our study participants, for example, were less concerned with the details of understanding an algorithmic calculation than with who or what is judging the result of the algorithm. %K Computer Science, Human-Computer Interaction, cs.HC,Computer Science, Computers and Society, cs.CY,Computer Science, Digital Libraries, cs.DL
[92]
A. Bhattacharya, S. Natarajan, and R. Saha Roy, Eds., Proceedings of the 7th ACM IKDD CoDS and 25th COMAD. ACM, 2020.
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@proceedings{SahaRoy_CoDSCOMAD20, TITLE = {Proceedings of the 7th ACM IKDD CoDS and 25th COMAD (CoDS-COMAD 2020)}, EDITOR = {Bhattacharya, Arnab and Natarajan, Sriaam and Saha Roy, Rishiraj}, LANGUAGE = {eng}, ISBN = {978-1-4503-7738-6}, DOI = {10.1145/3371158}, PUBLISHER = {ACM}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, ADDRESS = {Hyderabad, India}, }
Endnote
%0 Conference Proceedings %E Bhattacharya, Arnab %E Natarajan, Sriaam %E Saha Roy, Rishiraj %+ External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Proceedings of the 7th ACM IKDD CoDS and 25th COMAD : %G eng %U http://hdl.handle.net/21.11116/0000-0008-09CF-6 %R 10.1145/3371158 %@ 978-1-4503-7738-6 %I ACM %D 2020 %B ACM India Joint International Conferenceon Data Science and Management of Data %Z date of event: 2020-01-05 - 2020-01-07 %D 2020 %C Hyderabad, India
[93]
A. J. Biega, J. Schmidt, and R. Saha Roy, “Towards Query Logs for Privacy Studies: On Deriving Search Queries from Questions,” in Advances in Information Retrieval (ECIR 2020), Lisbon, Portugal, 2020.
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@inproceedings{Biega_ECIR2020, TITLE = {Towards Query Logs for Privacy Studies: {O}n Deriving Search Queries from Questions}, AUTHOR = {Biega, Asia J. and Schmidt, Jana and Saha Roy, Rishiraj}, LANGUAGE = {eng}, ISBN = {978-3-030-45441-8}, DOI = {10.1007/978-3-030-45442-5_14}, PUBLISHER = {Springer}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, DATE = {2020}, BOOKTITLE = {Advances in Information Retrieval (ECIR 2020)}, EDITOR = {Jose, Joemon M. and Yilmaz, Emine and Magalh{\~a}es, Jo{\~a}o and Castells, Pablo and Ferro, Nicola and Silva, M{\'a}rio J. and Martins, Fl{\'a}vio}, PAGES = {110--117}, SERIES = {Lecture Notes in Computer Science}, VOLUME = {12036}, ADDRESS = {Lisbon, Portugal}, }
Endnote
%0 Conference Proceedings %A Biega, Asia J. %A Schmidt, Jana %A Saha Roy, Rishiraj %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Towards Query Logs for Privacy Studies: On Deriving Search Queries from Questions : %G eng %U http://hdl.handle.net/21.11116/0000-0008-02FD-9 %R 10.1007/978-3-030-45442-5_14 %D 2020 %B 42nd European Conference on IR Research %Z date of event: 2020-04-14 - 2020-04-17 %C Lisbon, Portugal %B Advances in Information Retrieval %E Jose, Joemon M.; Yilmaz, Emine; Magalh&#227;es, Jo&#227;o; Castells, Pablo; Ferro, Nicola; Silva, M&#225;rio J.; Martins, Fl&#225;vio %P 110 - 117 %I Springer %@ 978-3-030-45441-8 %B Lecture Notes in Computer Science %N 12036
[94]
A. J. Biega, J. Schmidt, and R. Saha Roy, “Towards Query Logs for Privacy Studies: On Deriving Search Queries from Questions,” 2020. [Online]. Available: https://arxiv.org/abs/2004.02023. (arXiv: 2004.02023)
Abstract
Translating verbose information needs into crisp search queries is a phenomenon that is ubiquitous but hardly understood. Insights into this process could be valuable in several applications, including synthesizing large privacy-friendly query logs from public Web sources which are readily available to the academic research community. In this work, we take a step towards understanding query formulation by tapping into the rich potential of community question answering (CQA) forums. Specifically, we sample natural language (NL) questions spanning diverse themes from the Stack Exchange platform, and conduct a large-scale conversion experiment where crowdworkers submit search queries they would use when looking for equivalent information. We provide a careful analysis of this data, accounting for possible sources of bias during conversion, along with insights into user-specific linguistic patterns and search behaviors. We release a dataset of 7,000 question-query pairs from this study to facilitate further research on query understanding.
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@online{Biega2004.02023, TITLE = {Towards Query Logs for Privacy Studies: On Deriving Search Queries from Questions}, AUTHOR = {Biega, Asia J. and Schmidt, Jana and Saha Roy, Rishiraj}, LANGUAGE = {eng}, URL = {https://arxiv.org/abs/2004.02023}, EPRINT = {2004.02023}, EPRINTTYPE = {arXiv}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Translating verbose information needs into crisp search queries is a phenomenon that is ubiquitous but hardly understood. Insights into this process could be valuable in several applications, including synthesizing large privacy-friendly query logs from public Web sources which are readily available to the academic research community. In this work, we take a step towards understanding query formulation by tapping into the rich potential of community question answering (CQA) forums. Specifically, we sample natural language (NL) questions spanning diverse themes from the Stack Exchange platform, and conduct a large-scale conversion experiment where crowdworkers submit search queries they would use when looking for equivalent information. We provide a careful analysis of this data, accounting for possible sources of bias during conversion, along with insights into user-specific linguistic patterns and search behaviors. We release a dataset of 7,000 question-query pairs from this study to facilitate further research on query understanding.}, }
Endnote
%0 Report %A Biega, Asia J. %A Schmidt, Jana %A Saha Roy, Rishiraj %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Towards Query Logs for Privacy Studies: On Deriving Search Queries from Questions : %G eng %U http://hdl.handle.net/21.11116/0000-0008-09C7-E %U https://arxiv.org/abs/2004.02023 %D 2020 %X Translating verbose information needs into crisp search queries is a phenomenon that is ubiquitous but hardly understood. Insights into this process could be valuable in several applications, including synthesizing large privacy-friendly query logs from public Web sources which are readily available to the academic research community. In this work, we take a step towards understanding query formulation by tapping into the rich potential of community question answering (CQA) forums. Specifically, we sample natural language (NL) questions spanning diverse themes from the Stack Exchange platform, and conduct a large-scale conversion experiment where crowdworkers submit search queries they would use when looking for equivalent information. We provide a careful analysis of this data, accounting for possible sources of bias during conversion, along with insights into user-specific linguistic patterns and search behaviors. We release a dataset of 7,000 question-query pairs from this study to facilitate further research on query understanding. %K Computer Science, Information Retrieval, cs.IR
[95]
K. Budhathoki, “Causal Inference on Discrete Data,” Universität des Saarlandes, Saarbrücken, 2020.
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@phdthesis{BudDiss_2020, TITLE = {Causal Inference on Discrete Data}, AUTHOR = {Budhathoki, Kailash}, LANGUAGE = {eng}, URL = {urn:nbn:de:bsz:291--ds-329528}, DOI = {10.22028/D291-32952}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, DATE = {2020}, }
Endnote
%0 Thesis %A Budhathoki, Kailash %Y Vreeken, Jilles %A referee: Weikum, Gerhard %A referee: Heskes, Tom %+ 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 Causal Inference on Discrete Data : %G eng %U http://hdl.handle.net/21.11116/0000-0007-FE73-A %R 10.22028/D291-32952 %U urn:nbn:de:bsz:291--ds-329528 %I Universit&#228;t des Saarlandes %C Saarbr&#252;cken %D 2020 %P 171 p. %V phd %9 phd %U https://publikationen.sulb.uni-saarland.de/handle/20.500.11880/30501
[96]
D. Calvanes, J. Corman, D. Lanti, and S. Razniewski, “Counting Query Answers over a DL-Lite Knowledge Base,” in Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI 2020), Yokohama, Japan (Virtual), 2020.
Abstract
Counting answers to a query is an operation supported by virtually all database management systems. In this paper we focus on counting answers over a Knowledge Base (KB), which may be viewed as a database enriched with background knowledge about the domain under consideration. In particular, we place our work in the context of Ontology-Mediated Query Answering/Ontology-based Data Access (OMQA/OBDA), where the language used for the ontology is a member of the DL-Lite family and the data is a (usually virtual) set of assertions. We study the data complexity of query answering, for different members of the DL-Lite family that include number restrictions, and for variants of conjunctive queries with counting that differ with respect to their shape (connected, branching, rooted). We improve upon existing results by providing a PTIME and coNP lower bounds, and upper bounds in PTIME and LOGSPACE. For the latter case, we define a novel query rewriting technique into first-order logic with counting.
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@inproceedings{RazniewskiIJCAI2020, TITLE = {Counting Query Answers over a {$DL-Lite$} Knowledge Base}, AUTHOR = {Calvanes, Diego and Corman, Julien and Lanti, Davide and Razniewski, Simon}, LANGUAGE = {eng}, ISBN = {978-0-9992411-6-5}, DOI = {10.24963/ijcai.2020/230}, PUBLISHER = {IJCAI}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Counting answers to a query is an operation supported by virtually all database management systems. In this paper we focus on counting answers over a Knowledge Base (KB), which may be viewed as a database enriched with background knowledge about the domain under consideration. In particular, we place our work in the context of Ontology-Mediated Query Answering/Ontology-based Data Access (OMQA/OBDA), where the language used for the ontology is a member of the DL-Lite family and the data is a (usually virtual) set of assertions. We study the data complexity of query answering, for different members of the DL-Lite family that include number restrictions, and for variants of conjunctive queries with counting that differ with respect to their shape (connected, branching, rooted). We improve upon existing results by providing a PTIME and coNP lower bounds, and upper bounds in PTIME and LOGSPACE. For the latter case, we define a novel query rewriting technique into first-order logic with counting.}, BOOKTITLE = {Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI 2020)}, EDITOR = {Bessiere, Christian}, PAGES = {1658--1666}, ADDRESS = {Yokohama, Japan (Virtual)}, }
Endnote
%0 Conference Proceedings %A Calvanes, Diego %A Corman, Julien %A Lanti, Davide %A Razniewski, Simon %+ External Organizations External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Counting Query Answers over a DL-Lite Knowledge Base : %G eng %U http://hdl.handle.net/21.11116/0000-0008-009E-6 %R 10.24963/ijcai.2020/230 %D 2020 %B Twenty-Ninth International Joint Conference on Artificial Intelligence %Z date of event: 2021-01-07 - 2021-01-15 %C Yokohama, Japan (Virtual) %X Counting answers to a query is an operation supported by virtually all database management systems. In this paper we focus on counting answers over a Knowledge Base (KB), which may be viewed as a database enriched with background knowledge about the domain under consideration. In particular, we place our work in the context of Ontology-Mediated Query Answering/Ontology-based Data Access (OMQA/OBDA), where the language used for the ontology is a member of the DL-Lite family and the data is a (usually virtual) set of assertions. We study the data complexity of query answering, for different members of the DL-Lite family that include number restrictions, and for variants of conjunctive queries with counting that differ with respect to their shape (connected, branching, rooted). We improve upon existing results by providing a PTIME and coNP lower bounds, and upper bounds in PTIME and LOGSPACE. For the latter case, we define a novel query rewriting technique into first-order logic with counting. %K Computer Science, Databases, cs.DB,Computer Science, Artificial Intelligence, cs.AI %B Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence %E Bessiere, Christian %P 1658 - 1666 %I IJCAI %@ 978-0-9992411-6-5
[97]
D. Calvanes, J. Corman, D. Lanti, and S. Razniewski, “Counting Query Answers over a DL-Lite Knowledge Base (extended version),” 2020. [Online]. Available: https://arxiv.org/abs/2005.05886. (arXiv: 2005.05886)
Abstract
Counting answers to a query is an operation supported by virtually all database management systems. In this paper we focus on counting answers over a Knowledge Base (KB), which may be viewed as a database enriched with background knowledge about the domain under consideration. In particular, we place our work in the context of Ontology-Mediated Query Answering/Ontology-based Data Access (OMQA/OBDA), where the language used for the ontology is a member of the DL-Lite family and the data is a (usually virtual) set of assertions. We study the data complexity of query answering, for different members of the DL-Lite family that include number restrictions, and for variants of conjunctive queries with counting that differ with respect to their shape (connected, branching, rooted). We improve upon existing results by providing a PTIME and coNP lower bounds, and upper bounds in PTIME and LOGSPACE. For the latter case, we define a novel query rewriting technique into first-order logic with counting.
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@online{Razniewskiarxiv2020, TITLE = {Counting Query Answers over a {DL}-Lite Knowledge Base (extended version)}, AUTHOR = {Calvanes, Diego and Corman, Julien and Lanti, Davide and Razniewski, Simon}, LANGUAGE = {eng}, URL = {https://arxiv.org/abs/2005.05886}, EPRINT = {2005.05886}, EPRINTTYPE = {arXiv}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Counting answers to a query is an operation supported by virtually all database management systems. In this paper we focus on counting answers over a Knowledge Base (KB), which may be viewed as a database enriched with background knowledge about the domain under consideration. In particular, we place our work in the context of Ontology-Mediated Query Answering/Ontology-based Data Access (OMQA/OBDA), where the language used for the ontology is a member of the DL-Lite family and the data is a (usually virtual) set of assertions. We study the data complexity of query answering, for different members of the DL-Lite family that include number restrictions, and for variants of conjunctive queries with counting that differ with respect to their shape (connected, branching, rooted). We improve upon existing results by providing a PTIME and coNP lower bounds, and upper bounds in PTIME and LOGSPACE. For the latter case, we define a novel query rewriting technique into first-order logic with counting.}, }
Endnote
%0 Report %A Calvanes, Diego %A Corman, Julien %A Lanti, Davide %A Razniewski, Simon %+ External Organizations External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Counting Query Answers over a DL-Lite Knowledge Base (extended version) : %G eng %U http://hdl.handle.net/21.11116/0000-0007-FF5A-6 %U https://arxiv.org/abs/2005.05886 %D 2020 %X Counting answers to a query is an operation supported by virtually all database management systems. In this paper we focus on counting answers over a Knowledge Base (KB), which may be viewed as a database enriched with background knowledge about the domain under consideration. In particular, we place our work in the context of Ontology-Mediated Query Answering/Ontology-based Data Access (OMQA/OBDA), where the language used for the ontology is a member of the DL-Lite family and the data is a (usually virtual) set of assertions. We study the data complexity of query answering, for different members of the DL-Lite family that include number restrictions, and for variants of conjunctive queries with counting that differ with respect to their shape (connected, branching, rooted). We improve upon existing results by providing a PTIME and coNP lower bounds, and upper bounds in PTIME and LOGSPACE. For the latter case, we define a novel query rewriting technique into first-order logic with counting. %K Computer Science, Databases, cs.DB,Computer Science, Artificial Intelligence, cs.AI
[98]
D. Calvanese, J. Corman, D. Lanti, and S. Razniewski, “Rewriting Count Queries over DL-Lite TBoxes with Number Restrictions,” in Proceedings of the 33rd International Workshop on Description Logics (DL 2020), Rhodes, Greece (Virtual Event), 2020.
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@inproceedings{Calvanese_DL2020, TITLE = {Rewriting Count Queries over {DL}-Lite {TBoxes} with Number Restrictions}, AUTHOR = {Calvanese, Diego and Corman, Julien and Lanti, Davide and Razniewski, Simon}, LANGUAGE = {eng}, ISSN = {1613-0073}, URL = {http://ceur-ws.org/Vol-2663/paper-7.pdf; urn:nbn:de:0074-2663-4}, PUBLISHER = {ceur-ws.org}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of the 33rd International Workshop on Description Logics (DL 2020)}, EDITOR = {Borgwardt, Stefan and Meyer, Thomas}, EID = {7}, SERIES = {CEUR Workshop Proceedings}, VOLUME = {2663}, ADDRESS = {Rhodes, Greece (Virtual Event)}, }
Endnote
%0 Conference Proceedings %A Calvanese, Diego %A Corman, Julien %A Lanti, Davide %A Razniewski, Simon %+ External Organizations External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Rewriting Count Queries over DL-Lite TBoxes with Number Restrictions : %G eng %U http://hdl.handle.net/21.11116/0000-0008-0606-B %U http://ceur-ws.org/Vol-2663/paper-7.pdf %D 2020 %B 33rd International Workshop on Description Logics %Z date of event: 2020-09-12 - 2020-09-14 %C Rhodes, Greece (Virtual Event) %B Proceedings of the 33rd International Workshop on Description Logics %E Borgwardt , Stefan; Meyer, Thomas %Z sequence number: 7 %I ceur-ws.org %B CEUR Workshop Proceedings %N 2663 %@ false
[99]
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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@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}, MARGINALMARK = {$\bullet$}, 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
[100]
Y. Chalier, S. Razniewski, and G. Weikum, “Joint Reasoning for Multi-Faceted Commonsense Knowledge,” in Automated Knowledge Base Construction (AKBC 2020), Virtual Conference, 2020.
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@inproceedings{Chalier_AKBC2020, TITLE = {Joint Reasoning for Multi-Faceted Commonsense Knowledge}, AUTHOR = {Chalier, Yohan and Razniewski, Simon and Weikum, Gerhard}, LANGUAGE = {eng}, DOI = {10.24432/C58G6G}, PUBLISHER = {OpenReview}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Automated Knowledge Base Construction (AKBC 2020)}, ADDRESS = {Virtual Conference}, }
Endnote
%0 Conference Proceedings %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/21.11116/0000-0007-EBCF-8 %R 10.24432/C58G6G %D 2020 %B 2nd Conference on Automated Knowledge Base Construction %Z date of event: 2020-06-22 - 2020-06-24 %C Virtual Conference %B Automated Knowledge Base Construction %I OpenReview %U https://openreview.net/forum?id=QnPV72SZVt
[101]
Y. Chalier, S. Razniewski, and G. Weikum, “Dice: A Joint Reasoning Framework for Multi-Faceted Commonsense Knowledge,” in ISWC 2020 Posters, Demos, and Industry Tracks, Globally Online, 2020.
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@inproceedings{Chalier_ISCW20, TITLE = {Dice: {A} Joint Reasoning Framework for Multi-Faceted Commonsense Knowledge}, AUTHOR = {Chalier, Yohan and Razniewski, Simon and Weikum, Gerhard}, LANGUAGE = {eng}, ISSN = {1613-0073}, URL = {http://ceur-ws.org/Vol-2721/paper482.pdf; urn:nbn:de:0074-2721-6}, PUBLISHER = {ceur-ws.org}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {ISWC 2020 Posters, Demos, and Industry Tracks}, EDITOR = {Taylor, Kerry and Goncalves, Rafael and Lecue, Freddy and Yan, Jun}, PAGES = {16--20}, EID = {482}, SERIES = {CEUR Workshop Proceedings}, VOLUME = {2721}, ADDRESS = {Globally Online}, }
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%0 Conference Proceedings %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 Dice: A Joint Reasoning Framework for Multi-Faceted Commonsense Knowledge : %G eng %U http://hdl.handle.net/21.11116/0000-0007-F132-0 %U http://ceur-ws.org/Vol-2721/paper482.pdf %D 2020 %B 19th Internatinal Semantic Web Conference %Z date of event: 2020-11-01 - 2020-11-06 %C Globally Online %B ISWC 2020 Posters, Demos, and Industry Tracks %E Taylor, Kerry; Goncalves, Rafael; Lecue, Freddy; Yan, Jun %P 16 - 20 %Z sequence number: 482 %I ceur-ws.org %B CEUR Workshop Proceedings %N 2721 %@ false %U http://ceur-ws.org/Vol-2721/paper482.pdf
[102]
E. Chang, J. Caplinger, A. Marin, X. Shen, and V. Demberg, “DART: A Lightweight Quality-Suggestive Data-to-Text Annotation Tool,” in The 28th International Conference on Computational Linguistics (COLING 2020), Barcelona, Spain (Online), 2020.
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@inproceedings{chang2020dart, TITLE = {{DART}: {A} Lightweight Quality-Suggestive Data-to-Text Annotation Tool}, AUTHOR = {Chang, Ernie and Caplinger, Jeriah and Marin, Alex and Shen, Xiaoyu and Demberg, Vera}, LANGUAGE = {eng}, ISBN = {978-1-952148-28-6}, URL = {https://www.aclweb.org/anthology/2020.coling-demos.3}, DOI = {10.18653/v1/2020.coling-demos.3}, PUBLISHER = {ACL}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {The 28th International Conference on Computational Linguistics (COLING 2020)}, EDITOR = {Ptaszynski, Michal and Ziolko, Bartosz}, PAGES = {12--17}, ADDRESS = {Barcelona, Spain (Online)}, }
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%0 Conference Proceedings %A Chang, Ernie %A Caplinger, Jeriah %A Marin, Alex %A Shen, Xiaoyu %A Demberg, Vera %+ External Organizations External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T DART: A Lightweight Quality-Suggestive Data-to-Text Annotation Tool : %G eng %U http://hdl.handle.net/21.11116/0000-0008-149C-2 %U https://www.aclweb.org/anthology/2020.coling-demos.3 %R 10.18653/v1/2020.coling-demos.3 %D 2020 %B The 28th International Conferenceon Computational Linguistics %Z date of event: 2020-12-08 - 2020-12-13 %C Barcelona, Spain (Online) %B The 28th International Conference on Computational Linguistics %E Ptaszynski, Michal; Ziolko, Bartosz %P 12 - 17 %I ACL %@ 978-1-952148-28-6
[103]
C. X. Chu, S. Razniewski, and G. Weikum, “ENTYFI: A System for Fine-grained Entity Typing in Fictional Texts,” in The 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020), Online, 2020.
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@inproceedings{Chu_EMNLP20, TITLE = {{ENTYFI}: {A} System for Fine-grained Entity Typing in Fictional Texts}, AUTHOR = {Chu, Cuong Xuan and Razniewski, Simon and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-952148-62-0}, URL = {https://www.aclweb.org/anthology/2020.emnlp-demos.14/}, DOI = {10.18653/v1/2020.emnlp-demos.14}, PUBLISHER = {ACM}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {The 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020)}, EDITOR = {Liu, Qun and Schlangen, David}, PAGES = {100--106}, ADDRESS = {Online}, }
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: A System for Fine-grained Entity Typing in Fictional Texts : %G eng %U http://hdl.handle.net/21.11116/0000-0007-EED5-D %U https://www.aclweb.org/anthology/2020.emnlp-demos.14/ %R 10.18653/v1/2020.emnlp-demos.14 %D 2020 %B Conference on Empirical Methods in Natural Language Processing %Z date of event: 2020-11-16 - 2020-11-20 %C Online %B The 2020 Conference on Empirical Methods in Natural Language Processing %E Liu, Qun; Schlangen, David %P 100 - 106 %I ACM %@ 978-1-952148-62-0 %U https://www.aclweb.org/anthology/2020.emnlp-demos.14.pdf
[104]
C. X. Chu, S. Razniewski, and G. Weikum, “ENTYFI: Entity Typing in Fictional Texts,” in WSDM ’20, 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}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {WSDM '20, 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 '20 %E Caverlee, James; Hu, Xia Ben %P 124 - 132 %I ACM %@ 9781450368223
[105]
S. Dalleiger and J. Vreeken, “Explainable Data Decompositions,” in AAAI Technical Track: Machine Learning, New York, NY, USA, 2020.
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@inproceedings{dalleiger:20:disc, TITLE = {Explainable Data Decompositions}, AUTHOR = {Dalleiger, Sebastian and Vreeken, Jilles}, LANGUAGE = {eng}, ISBN = {978-1-57735-835-0}, DOI = {10.1609/aaai.v34i04.5780}, PUBLISHER = {AAAI}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, DATE = {2020}, BOOKTITLE = {AAAI Technical Track: Machine Learning}, PAGES = {3709--3716}, ADDRESS = {New York, NY, USA}, }
Endnote
%0 Conference Proceedings %A Dalleiger, Sebastian %A Vreeken, Jilles %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Explainable Data Decompositions : %G eng %U http://hdl.handle.net/21.11116/0000-0008-2559-B %R 10.1609/aaai.v34i04.5780 %D 2020 %B Thirty-Fourth AAAI Conference on Artificial Intelligence %Z date of event: 2020-02-07 - 2020-02-12 %C New York, NY, USA %B AAAI Technical Track: Machine Learning %P 3709 - 3716 %I AAAI %@ 978-1-57735-835-0
[106]
S. Dalleiger and J. Vreeken, “The Relaxed Maximum Entropy Distribution and its Application to Pattern Discovery,” in 20th IEEE International Conference on Data Mining (ICDM 2020), Virtual Conference, 2020.
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@inproceedings{dalleiger:20:reaper, TITLE = {The Relaxed Maximum Entropy Distribution and its Application to Pattern Discovery}, AUTHOR = {Dalleiger, Sebastian and Vreeken, Jilles}, LANGUAGE = {eng}, ISBN = {978-1-7281-8316-9}, DOI = {10.1109/ICDM50108.2020.00112}, PUBLISHER = {IEEE}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {20th IEEE International Conference on Data Mining (ICDM 2020)}, EDITOR = {Plant, Claudia and Wang, Haixun and Cuzzocrea, Alfredo and Zaniolo, Carlo and Wu, Xidong}, PAGES = {978--983}, ADDRESS = {Virtual Conference}, }
Endnote
%0 Conference Proceedings %A Dalleiger, Sebastian %A Vreeken, Jilles %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T The Relaxed Maximum Entropy Distribution and its Application to Pattern Discovery : %G eng %U http://hdl.handle.net/21.11116/0000-0008-254E-8 %R 10.1109/ICDM50108.2020.00112 %D 2020 %B 20th IEEE International Conference on Data Mining %Z date of event: 2020-11-17 - 2020-11-20 %C Virtual Conference %B 20th IEEE International Conference on Data Mining %E Plant, Claudia; Wang, Haixun; Cuzzocrea, Alfredo; Zaniolo, Carlo; Wu, Xidong %P 978 - 983 %I IEEE %@ 978-1-7281-8316-9
[107]
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}, MARGINALMARK = {$\bullet$}, DATE = {2020}, JOURNAL = {Semantic Web}, VOLUME = {11}, NUMBER = {1}, PAGES = {441--482}, }
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%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
[108]
J. Fischer and J. Vreeken, “Sets of Robust Rules, and How to Find Them,” in Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2019), Würzburg, Germany, 2020.
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@inproceedings{fischer:19:grab, TITLE = {Sets of Robust Rules, and How to Find Them}, AUTHOR = {Fischer, Jonas and Vreeken, Jilles}, LANGUAGE = {eng}, ISBN = {978-3-030-46150-8}, DOI = {10.1007/978-3-030-46150-8_3}, PUBLISHER = {Springer}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2020}, BOOKTITLE = {Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2019)}, PAGES = {38--54}, SERIES = {Lecture Notes in Artificial Intelligence}, VOLUME = {11906}, ADDRESS = {W{\"u}rzburg, Germany}, }
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%0 Conference Proceedings %A Fischer, Jonas %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Sets of Robust Rules, and How to Find Them : %G eng %U http://hdl.handle.net/21.11116/0000-0007-FEAE-8 %R 10.1007/978-3-030-46150-8_3 %D 2020 %B European Conference on Machine Learning and Knowledge Discovery in Databases %Z date of event: 2019-09-19 - 2019-09-20 %C W&#252;rzburg, Germany %B Machine Learning and Knowledge Discovery in Databases %P 38 - 54 %I Springer %@ 978-3-030-46150-8 %B Lecture Notes in Artificial Intelligence %N 11906
[109]
J. Fischer and J. Vreeken, “Discovering Succinct Pattern Sets Expressing Co-Occurrence and Mutual Exclusivity,” in KDD ’20, 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Virtual Event, USA, 2020.
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@inproceedings{fischer:20:mexican, TITLE = {Discovering Succinct Pattern Sets Expressing Co-Occurrence and Mutual Exclusivity}, AUTHOR = {Fischer, Jonas and Vreeken, Jilles}, LANGUAGE = {eng}, ISBN = {978-1-4503-7998-4}, DOI = {10.1145/3394486.3403124}, PUBLISHER = {ACM}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, DATE = {2020}, BOOKTITLE = {KDD '20, 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining}, EDITOR = {Gupta, Rajesh and Liu, Yan and Tang, Jilaiang and Prakash, B. Aditya}, PAGES = {813--823}, ADDRESS = {Virtual Event, USA}, }
Endnote
%0 Conference Proceedings %A Fischer, Jonas %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Discovering Succinct Pattern Sets Expressing Co-Occurrence and Mutual Exclusivity : %G eng %U http://hdl.handle.net/21.11116/0000-0007-FEA5-1 %R 10.1145/3394486.3403124 %D 2020 %B 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining %Z date of event: 2020-08-23 - 2020-08-27 %C Virtual Event, USA %B KDD '20 %E Gupta, Rajesh; Liu, Yan; Tang, Jilaiang; Prakash, B. Aditya %P 813 - 823 %I ACM %@ 978-1-4503-7998-4
[110]
M. H. Gad-Elrab, D. Stepanova, T.-K. Tran, H. Adel, and G. Weikum, “ExCut: Explainable Embedding-Based Clustering over Knowledge Graphs,” in The Semantic Web -- ISWC 2020, Athens, Greece (Virtual Conference), 2020.
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@inproceedings{Gad_Elrab_ISWC2020, TITLE = {{ExCut}: {E}xplainable Embedding-Based Clustering over Knowledge Graphs}, AUTHOR = {Gad-Elrab, Mohamed Hassan and Stepanova, Daria and Tran, Trung-Kien and Adel, Heike and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-3-030-62418-7}, DOI = {10.1007/978-3-030-62419-4_13}, PUBLISHER = {Springer}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, DATE = {2020}, BOOKTITLE = {The Semantic Web -- ISWC 2020}, EDITOR = {Pan, Jeff Z. and Tamma, Valentina and D'Amato, Claudia and Janowicz, Krzysztof and Fu, Bo and Polleres, Axel and Seneviratne, Oshani and Kagal, Lalana}, PAGES = {218--237}, SERIES = {Lecture Notes in Computer Science}, VOLUME = {12506}, ADDRESS = {Athens, Greece (Virtual Conference)}, }
Endnote
%0 Conference Proceedings %A Gad-Elrab, Mohamed Hassan %A Stepanova, Daria %A Tran, Trung-Kien %A Adel, Heike %A Weikum, Gerhard %+ 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 ExCut: Explainable Embedding-Based Clustering over Knowledge Graphs : %G eng %U http://hdl.handle.net/21.11116/0000-0007-830F-5 %R 10.1007/978-3-030-62419-4_13 %D 2020 %B 19th International Semantic Web Conference %Z date of event: 2020-11-02 - 2020-11-06 %C Athens, Greece (Virtual Conference) %B The Semantic Web -- ISWC 2020 %E Pan, Jeff Z.; Tamma, Valentina; D'Amato, Claudia; Janowicz, Krzysztof; Fu, Bo; Polleres, Axel; Seneviratne, Oshani; Kagal, Lalana %P 218 - 237 %I Springer %@ 978-3-030-62418-7 %B Lecture Notes in Computer Science %N 12506
[111]
M. H. Gad-Elrab, V. T. Ho, E. Levinkov, T.-K. Tran, and D. Stepanova, “Towards Utilizing Knowledge Graph Embedding Models for Conceptual Clustering,” in ISWC 2020 Posters, Demos, and Industry Tracks, Globally Online, 2020.
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@inproceedings{Gad-Elrab_ISCW20, TITLE = {Towards Utilizing Knowledge Graph Embedding Models for Conceptual Clustering}, AUTHOR = {Gad-Elrab, Mohamed Hassan and Ho, Vinh Thinh and Levinkov, Evgeny and Tran, Trung-Kien and Stepanova, Daria}, LANGUAGE = {eng}, ISSN = {1613-0073}, URL = {http://ceur-ws.org/Vol-2721/paper572.pdf; urn:nbn:de:0074-2721-6}, PUBLISHER = {ceur-ws.org}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {ISWC 2020 Posters, Demos, and Industry Tracks}, EDITOR = {Taylor, Kerry and Goncalves, Rafael and Lecue, Freddy and Yan, Jun}, PAGES = {281--286}, EID = {572}, SERIES = {CEUR Workshop Proceedings}, VOLUME = {2721}, ADDRESS = {Globally Online}, }
Endnote
%0 Conference Proceedings %A Gad-Elrab, Mohamed Hassan %A Ho, Vinh Thinh %A Levinkov, Evgeny %A Tran, Trung-Kien %A Stepanova, Daria %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations %T Towards Utilizing Knowledge Graph Embedding Models for Conceptual Clustering : %G eng %U http://hdl.handle.net/21.11116/0000-0007-F86B-A %U http://ceur-ws.org/Vol-2721/paper572.pdf %D 2020 %B 19th Internatinal Semantic Web Conference %Z date of event: 2020-11-01 - 2020-11-06 %C Globally Online %B ISWC 2020 Posters, Demos, and Industry Tracks %E Taylor, Kerry; Goncalves, Rafael; Lecue, Freddy; Yan, Jun %P 281 - 286 %Z sequence number: 572 %I ceur-ws.org %B CEUR Workshop Proceedings %N 2721 %@ false %U http://ceur-ws.org/Vol-2721/paper572.pdf
[112]
A. Ghazimatin, O. Balalau, R. Saha Roy, and G. Weikum, “PRINCE: Provider-side Interpretability with Counterfactual Explanations in Recommender Systems,” in WSDM ’20, 13th International Conference on Web Search and Data Mining, Houston, TX, USA, 2020.
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@inproceedings{GhazimatinWSDM2020, TITLE = {{PRINCE}: {P}rovider-side Interpretability with Counterfactual Explanations in Recommender Systemsxts}, AUTHOR = {Ghazimatin, Azin and Balalau, Oana and Saha Roy, Rishiraj and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-6822-3}, DOI = {10.1145/3336191.3371824}, PUBLISHER = {ACM}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {WSDM '20, 13th International Conference on Web Search and Data Mining}, EDITOR = {Caverlee, James and Hu, Xia Ben}, PAGES = {196--204}, ADDRESS = {Houston, TX, USA}, }
Endnote
%0 Conference Proceedings %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/21.11116/0000-0007-F173-7 %R 10.1145/3336191.3371824 %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 '20 %E Caverlee, James; Hu, Xia Ben %P 196 - 204 %I ACM %@ 978-1-4503-6822-3
[113]
S. Ghosh, S. Razniewski, and G. Weikum, “Uncovering Hidden Semantics of Set Information in Knowledge Bases,” Journal of Web Semantics, vol. 64, 2020.
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@article{Ghosh_2020, TITLE = {Uncovering Hidden Semantics of Set Information in Knowledge Bases}, AUTHOR = {Ghosh, Shrestha and Razniewski, Simon and Weikum, Gerhard}, LANGUAGE = {eng}, ISSN = {1570-8268}, DOI = {10.1016/j.websem.2020.100588}, PUBLISHER = {Elsevier}, ADDRESS = {Amsterdam}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, DATE = {2020}, JOURNAL = {Journal of Web Semantics}, VOLUME = {64}, EID = {100588}, }
Endnote
%0 Journal Article %A Ghosh, Shrestha %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 Uncovering Hidden Semantics of Set Information in Knowledge Bases : %G eng %U http://hdl.handle.net/21.11116/0000-0007-066D-9 %R 10.1016/j.websem.2020.100588 %7 2020 %D 2020 %J Journal of Web Semantics %V 64 %Z sequence number: 100588 %I Elsevier %C Amsterdam %@ false
[114]
S. Ghosh, S. Razniewski, and G. Weikum, “CounQER: A System for Discovering and Linking Count Information in Knowledge Bases,” in The Semantic Web: ESWC 2020 Satellite Events, Heraklion, Greece, 2020.
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@inproceedings{Ghosh_ESWC20, TITLE = {{CounQER}: {A} System for Discovering and Linking Count Information in Knowledge Bases}, AUTHOR = {Ghosh, Shrestha and Razniewski, Simon and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-3-030-62326-5}, DOI = {10.1007/978-3-030-62327-2_15}, PUBLISHER = {Springer}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, DATE = {2020}, BOOKTITLE = {The Semantic Web: ESWC 2020 Satellite Events}, EDITOR = {Harth, Andreas and Presutti, Valentina and Troncy, Rapha{\"e}l and Acosta, Maribel and Polleres, Axel and Fern{\'a}ndez, Javier D. and Xavier Parreira, Josiane and Hartig, Olaf and Hose, Katja and Cochez, Michael}, PAGES = {84--90}, SERIES = {Lecture Notes in Computer Science}, VOLUME = {12124}, ADDRESS = {Heraklion, Greece}, }
Endnote
%0 Conference Proceedings %A Ghosh, Shrestha %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 CounQER: A System for Discovering and Linking Count Information in Knowledge Bases : %G eng %U http://hdl.handle.net/21.11116/0000-0007-EFB9-C %R 10.1007/978-3-030-62327-2_15 %D 2020 %B 17th Extended Semantic Web Conference %Z date of event: 2020-05-31 - 2020-06-04 %C Heraklion, Greece %B The Semantic Web: ESWC 2020 Satellite Events %E Harth, Andreas; Presutti, Valentina; Troncy, Rapha&#235;l; Acosta, Maribel; Polleres, Axel; Fern&#225;ndez, Javier D.; Xavier Parreira, Josiane; Hartig, Olaf; Hose, Katja; Cochez, Michael %P 84 - 90 %I Springer %@ 978-3-030-62326-5 %B Lecture Notes in Computer Science %N 12124
[115]
S. Ghosh, S. Razniewski, and G. Weikum, “CounQER: A System for Discovering and Linking Count Information in Knowledge Bases,” 2020. [Online]. Available: https://arxiv.org/abs/2005.03529. (arXiv: 2005.03529)
Abstract
Predicate constraints of general-purpose knowledge bases (KBs) like Wikidata, DBpedia and Freebase are often limited to subproperty, domain and range constraints. In this demo we showcase CounQER, a system that illustrates the alignment of counting predicates, like staffSize, and enumerating predicates, like workInstitution^{-1} . In the demonstration session, attendees can inspect these alignments, and will learn about the importance of these alignments for KB question answering and curation. CounQER is available at https://counqer.mpi-inf.mpg.de/spo.
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@online{Ghosh_2005.03529, TITLE = {{CounQER}: {A} System for Discovering and Linking Count Information in Knowledge Bases}, AUTHOR = {Ghosh, Shrestha and Razniewski, Simon and Weikum, Gerhard}, LANGUAGE = {eng}, URL = {https://arxiv.org/abs/2005.03529}, EPRINT = {2005.03529}, EPRINTTYPE = {arXiv}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Predicate constraints of general-purpose knowledge bases (KBs) like Wikidata, DBpedia and Freebase are often limited to subproperty, domain and range constraints. In this demo we showcase CounQER, a system that illustrates the alignment of counting predicates, like staffSize, and enumerating predicates, like workInstitution^{-1} . In the demonstration session, attendees can inspect these alignments, and will learn about the importance of these alignments for KB question answering and curation. CounQER is available at https://counqer.mpi-inf.mpg.de/spo.}, }
Endnote
%0 Report %A Ghosh, Shrestha %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 CounQER: A System for Discovering and Linking Count Information in Knowledge Bases : %G eng %U http://hdl.handle.net/21.11116/0000-0007-F187-0 %U https://arxiv.org/abs/2005.03529 %D 2020 %X Predicate constraints of general-purpose knowledge bases (KBs) like Wikidata, DBpedia and Freebase are often limited to subproperty, domain and range constraints. In this demo we showcase CounQER, a system that illustrates the alignment of counting predicates, like staffSize, and enumerating predicates, like workInstitution^{-1} . In the demonstration session, attendees can inspect these alignments, and will learn about the importance of these alignments for KB question answering and curation. CounQER is available at https://counqer.mpi-inf.mpg.de/spo. %K Computer Science, Information Retrieval, cs.IR,Computer Science, Artificial Intelligence, cs.AI,Computer Science, Databases, cs.DB
[116]
S. Ghosh, S. Razniewski, and G. Weikum, “Uncovering Hidden Semantics of Set Information in Knowledge Bases,” 2020. [Online]. Available: http://arxiv.org/abs/2003.03155. (arXiv: 2003.03155)
Abstract
Knowledge Bases (KBs) contain a wealth of structured information about entities and predicates. This paper focuses on set-valued predicates, i.e., the relationship between an entity and a set of entities. In KBs, this information is often represented in two formats: (i) via counting predicates such as numberOfChildren and staffSize, that store aggregated integers, and (ii) via enumerating predicates such as parentOf and worksFor, that store individual set memberships. Both formats are typically complementary: unlike enumerating predicates, counting predicates do not give away individuals, but are more likely informative towards the true set size, thus this coexistence could enable interesting applications in question answering and KB curation. In this paper we aim at uncovering this hidden knowledge. We proceed in two steps. (i) We identify set-valued predicates from a given KB predicates via statistical and embedding-based features. (ii) We link counting predicates and enumerating predicates by a combination of co-occurrence, correlation and textual relatedness metrics. We analyze the prevalence of count information in four prominent knowledge bases, and show that our linking method achieves up to 0.55 F1 score in set predicate identification versus 0.40 F1 score of a random selection, and normalized discounted gains of up to 0.84 at position 1 and 0.75 at position 3 in relevant predicate alignments. Our predicate alignments are showcased in a demonstration system available at https://counqer.mpi-inf.mpg.de/spo.
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@online{Ghosh_arXiv2003.03155, TITLE = {Uncovering Hidden Semantics of Set Information in Knowledge Bases}, AUTHOR = {Ghosh, Shrestha and Razniewski, Simon and Weikum, Gerhard}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/2003.03155}, EPRINT = {2003.03155}, EPRINTTYPE = {arXiv}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Knowledge Bases (KBs) contain a wealth of structured information about entities and predicates. This paper focuses on set-valued predicates, i.e., the relationship between an entity and a set of entities. In KBs, this information is often represented in two formats: (i) via counting predicates such as numberOfChildren and staffSize, that store aggregated integers, and (ii) via enumerating predicates such as parentOf and worksFor, that store individual set memberships. Both formats are typically complementary: unlike enumerating predicates, counting predicates do not give away individuals, but are more likely informative towards the true set size, thus this coexistence could enable interesting applications in question answering and KB curation. In this paper we aim at uncovering this hidden knowledge. We proceed in two steps. (i) We identify set-valued predicates from a given KB predicates via statistical and embedding-based features. (ii) We link counting predicates and enumerating predicates by a combination of co-occurrence, correlation and textual relatedness metrics. We analyze the prevalence of count information in four prominent knowledge bases, and show that our linking method achieves up to 0.55 F1 score in set predicate identification versus 0.40 F1 score of a random selection, and normalized discounted gains of up to 0.84 at position 1 and 0.75 at position 3 in relevant predicate alignments. Our predicate alignments are showcased in a demonstration system available at https://counqer.mpi-inf.mpg.de/spo.}, }
Endnote
%0 Report %A Ghosh, Shrestha %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 Uncovering Hidden Semantics of Set Information in Knowledge Bases : %G eng %U http://hdl.handle.net/21.11116/0000-0007-0662-4 %U http://arxiv.org/abs/2003.03155 %D 2020 %X Knowledge Bases (KBs) contain a wealth of structured information about entities and predicates. This paper focuses on set-valued predicates, i.e., the relationship between an entity and a set of entities. In KBs, this information is often represented in two formats: (i) via counting predicates such as numberOfChildren and staffSize, that store aggregated integers, and (ii) via enumerating predicates such as parentOf and worksFor, that store individual set memberships. Both formats are typically complementary: unlike enumerating predicates, counting predicates do not give away individuals, but are more likely informative towards the true set size, thus this coexistence could enable interesting applications in question answering and KB curation. In this paper we aim at uncovering this hidden knowledge. We proceed in two steps. (i) We identify set-valued predicates from a given KB predicates via statistical and embedding-based features. (ii) We link counting predicates and enumerating predicates by a combination of co-occurrence, correlation and textual relatedness metrics. We analyze the prevalence of count information in four prominent knowledge bases, and show that our linking method achieves up to 0.55 F1 score in set predicate identification versus 0.40 F1 score of a random selection, and normalized discounted gains of up to 0.84 at position 1 and 0.75 at position 3 in relevant predicate alignments. Our predicate alignments are showcased in a demonstration system available at https://counqer.mpi-inf.mpg.de/spo. %K Computer Science, Databases, cs.DB,Computer Science, Information Retrieval, cs.IR
[117]
D. Gupta and K. Berberich, “Weaving Text into Tables,” in CIKM ’20, 29th ACM International Conference on Information & Knowledge Management, Virtual Event, Ireland, 2020.
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@inproceedings{DBLP:conf/cikm/0001B20, TITLE = {Weaving Text into Tables}, AUTHOR = {Gupta, Dhruv and Berberich, Klaus}, LANGUAGE = {eng}, ISBN = {978-1-4503-6859-9}, DOI = {10.1145/3340531.3417442}, PUBLISHER = {ACM}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, DATE = {2020}, BOOKTITLE = {CIKM '20, 29th ACM International Conference on Information \& Knowledge Management}, EDITOR = {d{\textquoteright}Aquin, Mathieu and Dietze, Stefan}, PAGES = {3401--34049}, ADDRESS = {Virtual Event, Ireland}, }
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 Weaving Text into Tables : %G eng %U http://hdl.handle.net/21.11116/0000-0008-0313-F %R 10.1145/3340531.3417442 %D 2020 %B 29th ACM International Conference on Information & Knowledge Management %Z date of event: 2020-10-19 - 2020-10-23 %C Virtual Event, Ireland %B CIKM '20 %E d&#8217;Aquin, Mathieu; Dietze, Stefan %P 3401 - 34049 %I ACM %@ 978-1-4503-6859-9
[118]
D. Gupta and K. Berberich, “Optimizing Hyper-Phrase Queries,” in ICTIR ’20, ACM SIGIR International Conference on Theory of Information Retrieval, Virtual Event, Norway, 2020.
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@inproceedings{DBLP:conf/ictir/0002B20, TITLE = {Optimizing Hyper-Phrase Queries}, AUTHOR = {Gupta, Dhruv and Berberich, Klaus}, LANGUAGE = {eng}, ISBN = {978-1-4503-8067-6}, DOI = {10.1145/3409256.3409827}, PUBLISHER = {ACM}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {ICTIR '20, ACM SIGIR International Conference on Theory of Information Retrieval}, EDITOR = {Balog, Krisztian and Setty, Vinay and Lioma, Christina and Liu, Yiqun and Zhang, Min and Berberich, Klaus}, PAGES = {41--48}, ADDRESS = {Virtual Event, Norway}, }
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 Optimizing Hyper-Phrase Queries : %G eng %U http://hdl.handle.net/21.11116/0000-0008-0335-9 %R 10.1145/3409256.3409827 %D 2020 %B ACM SIGIR International Conference on Theory of Information Retrieval %Z date of event: 2020-09-14 - 2020-09-17 %C Virtual Event, Norway %B ICTIR '20 %E Balog, Krisztian; Setty, Vinay; Lioma, Christina; Liu, Yiqun; Zhang, Min; Berberich, Klaus %P 41 - 48 %I ACM %@ 978-1-4503-8067-6
[119]
E. Heiter, “Factoring Out Prior Knowledge from Low-dimensional Embeddings,” Universität des Saarlandes, Saarbrücken, 2020.
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@mastersthesis{heiter:20:confetti, TITLE = {Factoring Out Prior Knowledge from Low-dimensional Embeddings}, AUTHOR = {Heiter, Edith}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, DATE = {2020}, }
Endnote
%0 Thesis %A Heiter, Edith %Y Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Factoring Out Prior Knowledge from Low-dimensional Embeddings : %G eng %U http://hdl.handle.net/21.11116/0000-0007-FEF8-4 %I Universit&#228;t des Saarlandes %C Saarbr&#252;cken %D 2020 %V master %9 master
[120]
V. T. Ho, K. Pal, N. Kleer, K. Berberich, and G. Weikum, “Entities with Quantities: Extraction, Search, and Ranking,” in WSDM ’20, 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}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {WSDM '20, 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 '20 %E Caverlee, James; Hu, Xia Ben %P 833 - 836 %I ACM %@ 9781450368223
[121]
M. Jain, P. Mirza, and R. Mutharaju, “Cardinality Extraction from Text for Ontology Learning,” in Proceedings of the 7th ACM IKDD CoDS and 25th COMAD (CoDS-COMAD 2020), Hyderabad, India, 2020.
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@inproceedings{Jain_CoDS2020, TITLE = {Cardinality Extraction from Text for Ontology Learning}, AUTHOR = {Jain, Monika and Mirza, Paramita and Mutharaju, Raghava}, LANGUAGE = {eng}, ISBN = {9781450377386}, DOI = {10.1145/3371158.3371223}, PUBLISHER = {ACM}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of the 7th ACM IKDD CoDS and 25th COMAD (CoDS-COMAD 2020)}, EDITOR = {Bhattacharya, Arnab and Natarajan, Sriraam and Saha Roy, Rishiraj}, PAGES = {354--354}, ADDRESS = {Hyderabad, India}, }
Endnote
%0 Conference Proceedings %A Jain, Monika %A Mirza, Paramita %A Mutharaju, Raghava %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Cardinality Extraction from Text for Ontology Learning : %G eng %U http://hdl.handle.net/21.11116/0000-0008-AB73-6 %R 10.1145/3371158.3371223 %D 2020 %B ACM India Joint International Conferenceon Data Science and Management of Data %Z date of event: 2020-01-05 - 2020-01-07 %C Hyderabad, India %B Proceedings of the 7th ACM IKDD CoDS and 25th COMAD %E Bhattacharya, Arnab; Natarajan, Sriraam; Saha Roy, Rishiraj %P 354 - 354 %I ACM %@ 9781450377386
[122]
M. Kaiser, “Incorporating User Feedback in Conversational Question Answering over Heterogeneous Web Sources,” in SIGIR ’20, 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, China, 2020.
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@inproceedings{Kaiser_SIGIR20b, TITLE = {Incorporating User Feedback in Conversational Question Answering over Heterogeneous {Web} Sources}, AUTHOR = {Kaiser, Magdalena}, LANGUAGE = {eng}, ISBN = {9781450380164}, DOI = {10.1145/3397271.3401454}, PUBLISHER = {ACM}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {SIGIR '20, 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval}, PAGES = {2482--2482}, ADDRESS = {Virtual Event, China}, }
Endnote
%0 Conference Proceedings %A Kaiser, Magdalena %+ Databases and Information Systems, MPI for Informatics, Max Planck Society %T Incorporating User Feedback in Conversational Question Answering over Heterogeneous Web Sources : %G eng %U http://hdl.handle.net/21.11116/0000-0007-FCDA-8 %R 10.1145/3397271.3401454 %D 2020 %B 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval %Z date of event: 2020-07-25 - 2020-07-30 %C Virtual Event, China %B SIGIR '20 %P 2482 - 2482 %I ACM %@ 9781450380164
[123]
M. Kaiser, R. Saha Roy, and G. Weikum, “Conversational Question Answering over Passages by Leveraging Word Proximity Networks,” 2020. [Online]. Available: https://arxiv.org/abs/2004.13117. (arXiv: 2004.13117)
Abstract
Question answering (QA) over text passages is a problem of long-standing interest in information retrieval. Recently, the conversational setting has attracted attention, where a user asks a sequence of questions to satisfy her information needs around a topic. While this setup is a natural one and similar to humans conversing with each other, it introduces two key research challenges: understanding the context left implicit by the user in follow-up questions, and dealing with ad hoc question formulations. In this work, we demonstrate CROWN (Conversational passage ranking by Reasoning Over Word Networks): an unsupervised yet effective system for conversational QA with passage responses, that supports several modes of context propagation over multiple turns. To this end, CROWN first builds a word proximity network (WPN) from large corpora to store statistically significant term co-occurrences. At answering time, passages are ranked by a combination of their similarity to the question, and coherence of query terms within: these factors are measured by reading off node and edge weights from the WPN. CROWN provides an interface that is both intuitive for end-users, and insightful for experts for reconfiguration to individual setups. CROWN was evaluated on TREC CAsT data, where it achieved above-median performance in a pool of neural methods.
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@online{Kaiser_2004.13117, TITLE = {Conversational Question Answering over Passages by Leveraging Word Proximity Networks}, AUTHOR = {Kaiser, Magdalena and Saha Roy, Rishiraj and Weikum, Gerhard}, LANGUAGE = {eng}, URL = {https://arxiv.org/abs/2004.13117}, EPRINT = {2004.13117}, EPRINTTYPE = {arXiv}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Question answering (QA) over text passages is a problem of long-standing interest in information retrieval. Recently, the conversational setting has attracted attention, where a user asks a sequence of questions to satisfy her information needs around a topic. While this setup is a natural one and similar to humans conversing with each other, it introduces two key research challenges: understanding the context left implicit by the user in follow-up questions, and dealing with ad hoc question formulations. In this work, we demonstrate CROWN (Conversational passage ranking by Reasoning Over Word Networks): an unsupervised yet effective system for conversational QA with passage responses, that supports several modes of context propagation over multiple turns. To this end, CROWN first builds a word proximity network (WPN) from large corpora to store statistically significant term co-occurrences. At answering time, passages are ranked by a combination of their similarity to the question, and coherence of query terms within: these factors are measured by reading off node and edge weights from the WPN. CROWN provides an interface that is both intuitive for end-users, and insightful for experts for reconfiguration to individual setups. CROWN was evaluated on TREC CAsT data, where it achieved above-median performance in a pool of neural methods.}, }
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 Conversational Question Answering over Passages by Leveraging Word Proximity Networks : %G eng %U http://hdl.handle.net/21.11116/0000-0007-F17D-D %U https://arxiv.org/abs/2004.13117 %D 2020 %X Question answering (QA) over text passages is a problem of long-standing interest in information retrieval. Recently, the conversational setting has attracted attention, where a user asks a sequence of questions to satisfy her information needs around a topic. While this setup is a natural one and similar to humans conversing with each other, it introduces two key research challenges: understanding the context left implicit by the user in follow-up questions, and dealing with ad hoc question formulations. In this work, we demonstrate CROWN (Conversational passage ranking by Reasoning Over Word Networks): an unsupervised yet effective system for conversational QA with passage responses, that supports several modes of context propagation over multiple turns. To this end, CROWN first builds a word proximity network (WPN) from large corpora to store statistically significant term co-occurrences. At answering time, passages are ranked by a combination of their similarity to the question, and coherence of query terms within: these factors are measured by reading off node and edge weights from the WPN. CROWN provides an interface that is both intuitive for end-users, and insightful for experts for reconfiguration to individual setups. CROWN was evaluated on TREC CAsT data, where it achieved above-median performance in a pool of neural methods. %K Computer Science, Information Retrieval, cs.IR,Computer Science, Computation and Language, cs.CL
[124]
M. Kaiser, R. Saha Roy, and G. Weikum, “Conversational Question Answering over Passages by Leveraging Word Proximity Networks,” in SIGIR ’20, 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, China, 2020.
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@inproceedings{Kaiser_SIGIR20, TITLE = {Conversational Question Answering over Passages by Leveraging Word Proximity Networks}, AUTHOR = {Kaiser, Magdalena and Saha Roy, Rishiraj and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {9781450380164}, DOI = {10.1145/3397271.3401399}, PUBLISHER = {ACM}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {SIGIR '20, 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval}, PAGES = {2129--2132}, ADDRESS = {Virtual Event, China}, }
Endnote
%0 Conference Proceedings %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 Conversational Question Answering over Passages by Leveraging Word Proximity Networks : %G eng %U http://hdl.handle.net/21.11116/0000-0007-F152-C %R 10.1145/3397271.3401399 %D 2020 %B 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval %Z date of event: 2020-07-25 - 2020-07-30 %C Virtual Event, China %B SIGIR '20 %P 2129 - 2132 %I ACM %@ 9781450380164
[125]
P. Lahoti, A. Beutel, J. Chen, K. Lee, F. Prost, N. Thain, X. Wang, and E. Chi, “Fairness without Demographics through Adversarially Reweighted Learning,” in Advances in Neural Information Processing Systems 33 (NeurIPS 2020), Virtual Event, 2020.
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@inproceedings{DBLP:conf/nips/LahotiBCLPT0C20, TITLE = {Fairness without Demographics through Adversarially Reweighted Learning}, AUTHOR = {Lahoti, Preethi and Beutel, Alex and Chen, Jilin and Lee, Kang and Prost, Flavien and Thain, Nithum and Wang, Xuezhi and Chi, Ed}, LANGUAGE = {eng}, PUBLISHER = {Curran Associates, Inc.}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Advances in Neural Information Processing Systems 33 (NeurIPS 2020)}, EDITOR = {Larochelle, Hugo and Ranzato, Marc Aurelio and Hadsell, Raia and Balcan, Maria-Florina and Lin, Hsuan-Tien}, ADDRESS = {Virtual Event}, }
Endnote
%0 Conference Proceedings %A Lahoti, Preethi %A Beutel, Alex %A Chen, Jilin %A Lee, Kang %A Prost, Flavien %A Thain, Nithum %A Wang, Xuezhi %A Chi, Ed %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations %T Fairness without Demographics through Adversarially Reweighted Learning : %G eng %U http://hdl.handle.net/21.11116/0000-0007-FCC2-2 %D 2020 %B 34th Conference on Neural Information Processing Systems %Z date of event: 2020-12-06 - 2020-12-12 %C Virtual Event %B Advances in Neural Information Processing Systems 33 %E Larochelle, Hugo; Ranzato, Marc Aurelio; Hadsell, Raia; Balcan, Maria-Florina; Lin, Hsuan-Tien %I Curran Associates, Inc. %U https://proceedings.neurips.cc/paper/2020/hash/07fc15c9d169ee48573edd749d25945d-Abstract.html
[126]
C. Li, A. Yates, S. MacAvaney, B. He, and Y. Sun, “PARADE: Passage Representation Aggregation for Document Reranking,” 2020. [Online]. Available: https://arxiv.org/abs/2008.09093. (arXiv: 2008.09093)
Abstract
We present PARADE, an end-to-end Transformer-based model that considers document-level context for document reranking. PARADE leverages passage-level relevance representations to predict a document relevance score, overcoming the limitations of previous approaches that perform inference on passages independently. Experiments on two ad-hoc retrieval benchmarks demonstrate PARADE's effectiveness over such methods. We conduct extensive analyses on PARADE's efficiency, highlighting several strategies for improving it. When combined with knowledge distillation, a PARADE model with 72\% fewer parameters achieves effectiveness competitive with previous approaches using BERT-Base. Our code is available at \url{https://github.com/canjiali/PARADE}.
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@online{Li2008.09093, TITLE = {{PARADE}: Passage Representation Aggregation for Document Reranking}, AUTHOR = {Li, Canjia and Yates, Andrew and MacAvaney, Sean and He, Ben and Sun, Yingfei}, LANGUAGE = {eng}, URL = {https://arxiv.org/abs/2008.09093}, EPRINT = {2008.09093}, EPRINTTYPE = {arXiv}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, ABSTRACT = {We present PARADE, an end-to-end Transformer-based model that considers document-level context for document reranking. PARADE leverages passage-level relevance representations to predict a document relevance score, overcoming the limitations of previous approaches that perform inference on passages independently. Experiments on two ad-hoc retrieval benchmarks demonstrate PARADE's effectiveness over such methods. We conduct extensive analyses on PARADE's efficiency, highlighting several strategies for improving it. When combined with knowledge distillation, a PARADE model with 72\% fewer parameters achieves effectiveness competitive with previous approaches using BERT-Base. Our code is available at \url{https://github.com/canjiali/PARADE}.}, }
Endnote
%0 Report %A Li, Canjia %A Yates, Andrew %A MacAvaney, Sean %A He, Ben %A Sun, Yingfei %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations %T PARADE: Passage Representation Aggregation for Document Reranking : %G eng %U http://hdl.handle.net/21.11116/0000-0008-06CF-9 %U https://arxiv.org/abs/2008.09093 %D 2020 %X We present PARADE, an end-to-end Transformer-based model that considers document-level context for document reranking. PARADE leverages passage-level relevance representations to predict a document relevance score, overcoming the limitations of previous approaches that perform inference on passages independently. Experiments on two ad-hoc retrieval benchmarks demonstrate PARADE's effectiveness over such methods. We conduct extensive analyses on PARADE's efficiency, highlighting several strategies for improving it. When combined with knowledge distillation, a PARADE model with 72\% fewer parameters achieves effectiveness competitive with previous approaches using BERT-Base. Our code is available at \url{https://github.com/canjiali/PARADE}. %K Computer Science, Information Retrieval, cs.IR
[127]
J. Lin, R. Nogueira, and A. Yates, “Pretrained Transformers for Text Ranking: BERT and Beyond,” 2020. [Online]. Available: https://arxiv.org/abs/2010.06467. (arXiv: 2010.06467)
Abstract
The goal of text ranking is to generate an ordered list of texts retrieved from a corpus in response to a query. Although the most common formulation of text ranking is search, instances of the task can also be found in many natural language processing applications. This survey provides an overview of text ranking with neural network architectures known as transformers, of which BERT is the best-known example. The combination of transformers and self-supervised pretraining has, without exaggeration, revolutionized the fields of natural language processing (NLP), information retrieval (IR), and beyond. In this survey, we provide a synthesis of existing work as a single point of entry for practitioners who wish to gain a better understanding of how to apply transformers to text ranking problems and researchers who wish to pursue work in this area. We cover a wide range of modern techniques, grouped into two high-level categories: transformer models that perform reranking in multi-stage ranking architectures and learned dense representations that attempt to perform ranking directly. There are two themes that pervade our survey: techniques for handling long documents, beyond the typical sentence-by-sentence processing approaches used in NLP, and techniques for addressing the tradeoff between effectiveness (result quality) and efficiency (query latency). Although transformer architectures and pretraining techniques are recent innovations, many aspects of how they are applied to text ranking are relatively well understood and represent mature techniques. However, there remain many open research questions, and thus in addition to laying out the foundations of pretrained transformers for text ranking, this survey also attempts to prognosticate where the field is heading.
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@online{Lin2010.06467, TITLE = {Pretrained Transformers for Text Ranking: {BERT} and Beyond}, AUTHOR = {Lin, Jimmy and Nogueira, Rodrigo and Yates, Andrew}, LANGUAGE = {eng}, URL = {https://arxiv.org/abs/2010.06467}, EPRINT = {2010.06467}, EPRINTTYPE = {arXiv}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, ABSTRACT = {The goal of text ranking is to generate an ordered list of texts retrieved from a corpus in response to a query. Although the most common formulation of text ranking is search, instances of the task can also be found in many natural language processing applications. This survey provides an overview of text ranking with neural network architectures known as transformers, of which BERT is the best-known example. The combination of transformers and self-supervised pretraining has, without exaggeration, revolutionized the fields of natural language processing (NLP), information retrieval (IR), and beyond. In this survey, we provide a synthesis of existing work as a single point of entry for practitioners who wish to gain a better understanding of how to apply transformers to text ranking problems and researchers who wish to pursue work in this area. We cover a wide range of modern techniques, grouped into two high-level categories: transformer models that perform reranking in multi-stage ranking architectures and learned dense representations that attempt to perform ranking directly. There are two themes that pervade our survey: techniques for handling long documents, beyond the typical sentence-by-sentence processing approaches used in NLP, and techniques for addressing the tradeoff between effectiveness (result quality) and efficiency (query latency). Although transformer architectures and pretraining techniques are recent innovations, many aspects of how they are applied to text ranking are relatively well understood and represent mature techniques. However, there remain many open research questions, and thus in addition to laying out the foundations of pretrained transformers for text ranking, this survey also attempts to prognosticate where the field is heading.}, }
Endnote
%0 Report %A Lin, Jimmy %A Nogueira, Rodrigo %A Yates, Andrew %+ External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Pretrained Transformers for Text Ranking: BERT and Beyond : %G eng %U http://hdl.handle.net/21.11116/0000-0008-06DA-C %U https://arxiv.org/abs/2010.06467 %D 2020 %X The goal of text ranking is to generate an ordered list of texts retrieved from a corpus in response to a query. Although the most common formulation of text ranking is search, instances of the task can also be found in many natural language processing applications. This survey provides an overview of text ranking with neural network architectures known as transformers, of which BERT is the best-known example. The combination of transformers and self-supervised pretraining has, without exaggeration, revolutionized the fields of natural language processing (NLP), information retrieval (IR), and beyond. In this survey, we provide a synthesis of existing work as a single point of entry for practitioners who wish to gain a better understanding of how to apply transformers to text ranking problems and researchers who wish to pursue work in this area. We cover a wide range of modern techniques, grouped into two high-level categories: transformer models that perform reranking in multi-stage ranking architectures and learned dense representations that attempt to perform ranking directly. There are two themes that pervade our survey: techniques for handling long documents, beyond the typical sentence-by-sentence processing approaches used in NLP, and techniques for addressing the tradeoff between effectiveness (result quality) and efficiency (query latency). Although transformer architectures and pretraining techniques are recent innovations, many aspects of how they are applied to text ranking are relatively well understood and represent mature techniques. However, there remain many open research questions, and thus in addition to laying out the foundations of pretrained transformers for text ranking, this survey also attempts to prognosticate where the field is heading. %K Computer Science, Information Retrieval, cs.IR,Computer Science, Computation and Language, cs.CL
[128]
P. Mandros, M. Boley, and J. Vreeken, “Discovering Dependencies with Reliable Mutual Information,” Knowledge and Information Systems, vol. 62, 2020.
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@article{Mandros2020, TITLE = {Discovering Dependencies with Reliable Mutual Information}, AUTHOR = {Mandros, Panagiotis and Boley, Mario and Vreeken, Jilles}, LANGUAGE = {eng}, ISSN = {0219-3116}, DOI = {10.1007/s10115-020-01494-9}, PUBLISHER = {Springer}, ADDRESS = {New York, NY}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, JOURNAL = {Knowledge and Information Systems}, VOLUME = {62}, PAGES = {4223--4253}, }
Endnote
%0 Journal Article %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 Dependencies with Reliable Mutual Information : %G eng %U http://hdl.handle.net/21.11116/0000-0006-DC90-F %R 10.1007/s10115-020-01494-9 %7 2020 %D 2020 %J Knowledge and Information Systems %V 62 %& 4223 %P 4223 - 4253 %I Springer %C New York, NY %@ false
[129]
S. Nag Chowdhury, W. Cheng, G. de Melo, S. Razniewski, and G. Weikum, “Illustrate Your Story: Enriching Text with Images,” in WSDM ’20, 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}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {WSDM '20, 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 '20 %E Caverlee, James; Hu, Xia Ben %P 849 - 852 %I ACM %@ 9781450368223
[130]
T.-P. Nguyen, “Advanced Semantics for Commonsense Knowledge Extraction,” Universität des Saarlandes, Saarbrücken, 2020.
Abstract
Commonsense knowledge (CSK) about concepts and their properties is useful for AI applications such as robust chatbots. Prior works like ConceptNet, TupleKB and others compiled large CSK collections, but are restricted in their expressiveness to subject-predicate-object (SPO) triples with simple concepts for S and monolithic strings for P and O. Also, these projects have either prioritized precision or recall, but hardly reconcile these complementary goals. This thesis presents a methodology, called Ascent, to automatically build a large-scale knowledge base (KB) of CSK assertions, with advanced expressiveness and both better precision and recall than prior works. Ascent goes beyond triples by capturing composite concepts with subgroups and aspects, and by refining assertions with semantic facets. The latter are important to express temporal and spatial validity of assertions and further qualifiers. Ascent combines open information extraction with judicious cleaning using language models. Intrinsic evaluation shows the superior size and quality of the Ascent KB, and an extrinsic evaluation for QA-support tasks underlines the benefits of Ascent.
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@mastersthesis{NguyenMSc2020, TITLE = {Advanced Semantics for Commonsense Knowledge Extraction}, AUTHOR = {Nguyen, Tuan-Phong}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, DATE = {2020}, ABSTRACT = {Commonsense knowledge (CSK) about concepts and their properties is useful for AI applications such as robust chatbots. Prior works like ConceptNet, TupleKB and others compiled large CSK collections, but are restricted in their expressiveness to subject-predicate-object (SPO) triples with simple concepts for S and monolithic strings for P and O. Also, these projects have either prioritized precision or recall, but hardly reconcile these complementary goals. This thesis presents a methodology, called Ascent, to automatically build a large-scale knowledge base (KB) of CSK assertions, with advanced expressiveness and both better precision and recall than prior works. Ascent goes beyond triples by capturing composite concepts with subgroups and aspects, and by refining assertions with semantic facets. The latter are important to express temporal and spatial validity of assertions and further qualifiers. Ascent combines open information extraction with judicious cleaning using language models. Intrinsic evaluation shows the superior size and quality of the Ascent KB, and an extrinsic evaluation for QA-support tasks underlines the benefits of Ascent.}, }
Endnote
%0 Thesis %A Nguyen, Tuan-Phong %Y Razniewski, Simon %+ 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 Advanced Semantics for Commonsense Knowledge Extraction : %G eng %U http://hdl.handle.net/21.11116/0000-0007-FED0-0 %I Universit&#228;t des Saarlandes %C Saarbr&#252;cken %D 2020 %P 67 p. %V master %9 master %X Commonsense knowledge (CSK) about concepts and their properties is useful for AI applications such as robust chatbots. Prior works like ConceptNet, TupleKB and others compiled large CSK collections, but are restricted in their expressiveness to subject-predicate-object (SPO) triples with simple concepts for S and monolithic strings for P and O. Also, these projects have either prioritized precision or recall, but hardly reconcile these complementary goals. This thesis presents a methodology, called Ascent, to automatically build a large-scale knowledge base (KB) of CSK assertions, with advanced expressiveness and both better precision and recall than prior works. Ascent goes beyond triples by capturing composite concepts with subgroups and aspects, and by refining assertions with semantic facets. The latter are important to express temporal and spatial validity of assertions and further qualifiers. Ascent combines open information extraction with judicious cleaning using language models. Intrinsic evaluation shows the superior size and quality of the Ascent KB, and an extrinsic evaluation for QA-support tasks underlines the benefits of Ascent.
[131]
T.-P. Nguyen, S. Razniewski, and G. Weikum, “Advanced Semantics for Commonsense Knowledge Extraction,” WWW 2021, 2020. [Online]. Available: https://arxiv.org/abs/2011.00905. (arXiv: 2011.00905)
Abstract
Commonsense knowledge (CSK) about concepts and their properties is useful for AI applications such as robust chatbots. Prior works like ConceptNet, TupleKB and others compiled large CSK collections, but are restricted in their expressiveness to subject-predicate-object (SPO) triples with simple concepts for S and monolithic strings for P and O. Also, these projects have either prioritized precision or recall, but hardly reconcile these complementary goals. This paper presents a methodology, called Ascent, to automatically build a large-scale knowledge base (KB) of CSK assertions, with advanced expressiveness and both better precision and recall than prior works. Ascent goes beyond triples by capturing composite concepts with subgroups and aspects, and by refining assertions with semantic facets. The latter are important to express temporal and spatial validity of assertions and further qualifiers. Ascent combines open information extraction with judicious cleaning using language models. Intrinsic evaluation shows the superior size and quality of the Ascent KB, and an extrinsic evaluation for QA-support tasks underlines the benefits of Ascent.
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@online{Nguyen_2011.00905, TITLE = {Advanced Semantics for Commonsense Knowledge Extraction}, AUTHOR = {Nguyen, Tuan-Phong and Razniewski, Simon and Weikum, Gerhard}, LANGUAGE = {eng}, URL = {https://arxiv.org/abs/2011.00905}, EPRINT = {2011.00905}, EPRINTTYPE = {arXiv}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Commonsense knowledge (CSK) about concepts and their properties is useful for AI applications such as robust chatbots. Prior works like ConceptNet, TupleKB and others compiled large CSK collections, but are restricted in their expressiveness to subject-predicate-object (SPO) triples with simple concepts for S and monolithic strings for P and O. Also, these projects have either prioritized precision or recall, but hardly reconcile these complementary goals. This paper presents a methodology, called Ascent, to automatically build a large-scale knowledge base (KB) of CSK assertions, with advanced expressiveness and both better precision and recall than prior works. Ascent goes beyond triples by capturing composite concepts with subgroups and aspects, and by refining assertions with semantic facets. The latter are important to express temporal and spatial validity of assertions and further qualifiers. Ascent combines open information extraction with judicious cleaning using language models. Intrinsic evaluation shows the superior size and quality of the Ascent KB, and an extrinsic evaluation for QA-support tasks underlines the benefits of Ascent.}, JOURNAL = {WWW 2021}, }
Endnote
%0 Report %A Nguyen, Tuan-Phong %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 Advanced Semantics for Commonsense Knowledge Extraction : %G eng %U http://hdl.handle.net/21.11116/0000-0007-FEDA-6 %U https://arxiv.org/abs/2011.00905 %D 2020 %X Commonsense knowledge (CSK) about concepts and their properties is useful for AI applications such as robust chatbots. Prior works like ConceptNet, TupleKB and others compiled large CSK collections, but are restricted in their expressiveness to subject-predicate-object (SPO) triples with simple concepts for S and monolithic strings for P and O. Also, these projects have either prioritized precision or recall, but hardly reconcile these complementary goals. This paper presents a methodology, called Ascent, to automatically build a large-scale knowledge base (KB) of CSK assertions, with advanced expressiveness and both better precision and recall than prior works. Ascent goes beyond triples by capturing composite concepts with subgroups and aspects, and by refining assertions with semantic facets. The latter are important to express temporal and spatial validity of assertions and further qualifiers. Ascent combines open information extraction with judicious cleaning using language models. Intrinsic evaluation shows the superior size and quality of the Ascent KB, and an extrinsic evaluation for QA-support tasks underlines the benefits of Ascent. %K Computer Science, Artificial Intelligence, cs.AI,Computer Science, Computation and Language, cs.CL %J WWW 2021
[132]
A. Oláh, “What’s in the Box? Explaining Neural Networks with Robust Rules,” Universität des Saarlandes, Saarbrücken, 2020.
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@mastersthesis{olah:20:explainn, TITLE = {What's in the Box? Explaining Neural Networks with Robust Rules}, AUTHOR = {Ol{\'a}h, Anna}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, DATE = {2020}, }
Endnote
%0 Thesis %A Ol&#225;h, Anna %Y Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T What's in the Box? Explaining Neural Networks with Robust Rules : %G eng %U http://hdl.handle.net/21.11116/0000-0007-FEFA-2 %I Universit&#228;t des Saarlandes %C Saarbr&#252;cken %D 2020 %V master %9 master
[133]
K. Pal, V. T. Ho, and G. Weikum, “Co-Clustering Triples from Open Information Extraction,” in Proceedings of the 7th ACM IKDD CoDS and 25th COMAD (CoDS-COMAD 2020), Hyderabad, India, 2020.
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@inproceedings{Pal_CoDS2020, TITLE = {Co-Clustering Triples from Open Information Extraction}, AUTHOR = {Pal, Koninika and Ho, Vinh Thinh and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {9781450377386}, DOI = {10.1145/3371158.3371183}, PUBLISHER = {ACM}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of the 7th ACM IKDD CoDS and 25th COMAD (CoDS-COMAD 2020)}, EDITOR = {Bhattacharya, Arnab and Natarajan, Sriraam and Saha Roy, Rishiraj}, PAGES = {190--194}, ADDRESS = {Hyderabad, India}, }
Endnote
%0 Conference Proceedings %A Pal, Koninika %A Ho, Vinh Thinh %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 Co-Clustering Triples from Open Information Extraction : %G eng %U http://hdl.handle.net/21.11116/0000-0007-EBFC-5 %R 10.1145/3371158.3371183 %D 2020 %B ACM India Joint International Conferenceon Data Science and Management of Data %Z date of event: 2020-01-05 - 2020-01-07 %C Hyderabad, India %B Proceedings of the 7th ACM IKDD CoDS and 25th COMAD %E Bhattacharya, Arnab; Natarajan, Sriraam; Saha Roy, Rishiraj %P 190 - 194 %I ACM %@ 9781450377386
[134]
T. Pellissier Tanon, G. Weikum, and F. Suchanek, “YAGO 4: A Reason-able Knowledge Base,” in The Semantic Web (ESWC 2020), Heraklion, Greece, 2020.
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@inproceedings{Pellissier_ESCW2020, TITLE = {{YAGO 4}: {A} Reason-able Knowledge Base}, AUTHOR = {Pellissier Tanon, Thomas and Weikum, Gerhard and Suchanek, Fabian}, LANGUAGE = {eng}, ISBN = {978-3-030-49460-5}, DOI = {10.1007/978-3-030-49461-2_34}, PUBLISHER = {Springer}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, DATE = {2020}, BOOKTITLE = {The Semantic Web (ESWC 2020)}, EDITOR = {Harth, Andreas and Kirrane, Sabrina and Ngonga Ngomo, Axel-Cyrille and Paulheim, Heiko and Rula, Anisa and Gentile, Anna Lisa and Haase, Peter and Cochez, Michael}, PAGES = {583 {\textbar}--596}, SERIES = {Lecture Notes in Computer Science}, VOLUME = {12123}, ADDRESS = {Heraklion, Greece}, }
Endnote
%0 Conference Proceedings %A Pellissier Tanon, Thomas %A Weikum, Gerhard %A Suchanek, Fabian %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T YAGO 4: A Reason-able Knowledge Base : %G eng %U http://hdl.handle.net/21.11116/0000-0007-EFC8-B %R 10.1007/978-3-030-49461-2_34 %D 2020 %B 17th Extended Semantic Web Conference %Z date of event: 2020-05-31 - 2020-06-04 %C Heraklion, Greece %B The Semantic Web %E Harth, Andreas; Kirrane, Sabrina; Ngonga Ngomo, Axel-Cyrille; Paulheim, Heiko; Rula, Anisa; Gentile, Anna Lisa; Haase, Peter; Cochez, Michael %P 583 | - 596 %I Springer %@ 978-3-030-49460-5 %B Lecture Notes in Computer Science %N 12123
[135]
F. Pennerath, P. Mandros, and J. Vreeken, “Discovering Approximate Functional Dependencies using Smoothed Mutual Information,” in KDD ’20, 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Virtual Event, USA, 2020.
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@inproceedings{penerath:20:smooth, TITLE = {Discovering Approximate Functional Dependencies using Smoothed Mutual Information}, AUTHOR = {Pennerath, Fr{\'e}d{\'e}ric and Mandros, Panagiotis and Vreeken, Jilles}, LANGUAGE = {eng}, ISBN = {978-1-4503-7998-4}, DOI = {10.1145/3394486.3403178}, PUBLISHER = {ACM}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, DATE = {2020}, BOOKTITLE = {KDD '20, 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining}, EDITOR = {Gupta, Rajesh and Liu, Yan and Tang, Jilaiang and Prakash, B. Aditya}, PAGES = {1254--1264}, ADDRESS = {Virtual Event, USA}, }
Endnote
%0 Conference Proceedings %A Pennerath, Fr&#233;d&#233;ric %A Mandros, Panagiotis %A Vreeken, Jilles %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Discovering Approximate Functional Dependencies using Smoothed Mutual Information : %G eng %U http://hdl.handle.net/21.11116/0000-0008-2560-2 %R 10.1145/3394486.3403178 %D 2020 %B 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining %Z date of event: 2020-08-23 - 2020-08-27 %C Virtual Event, USA %B KDD '20 %E Gupta, Rajesh; Liu, Yan; Tang, Jilaiang; Prakash, B. Aditya %P 1254 - 1264 %I ACM %@ 978-1-4503-7998-4
[136]
S. Qiu, B. Xu, J. Zhang, Y. Wang, X. Shen, G. de Melo, C. Long, and X. Li, “EasyAug: An Automatic Textual Data Augmentation Platform for Classification Tasks,” in Companion of The World Wide Web Conference (WWW 2020), Taipei, Taiwan, 2020.
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@inproceedings{qiu2020easyaug, TITLE = {{EasyAug}: {An} Automatic Textual Data Augmentation Platform for Classification Tasks}, AUTHOR = {Qiu, Siyuan and Xu, Binxia and Zhang, Jie and Wang, Yafang and Shen, Xiaoyu and de Melo, Gerard and Long, Chong and Li, Xiaolong}, LANGUAGE = {eng}, ISBN = {978-1-4503-7024-0}, DOI = {10.1145/3366424.3383552}, PUBLISHER = {ACM}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Companion of The World Wide Web Conference (WWW 2020)}, EDITOR = {El Fallah, Amal and Sukthankar, Gita and Liu, Tie-Yan and van Steen, Maarten}, PAGES = {249--252}, ADDRESS = {Taipei, Taiwan}, }
Endnote
%0 Conference Proceedings %A Qiu, Siyuan %A Xu, Binxia %A Zhang, Jie %A Wang, Yafang %A Shen, Xiaoyu %A de Melo, Gerard %A Long, Chong %A Li, Xiaolong %+ External Organizations External Organizations External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations %T EasyAug: An Automatic Textual Data Augmentation Platform for Classification Tasks : %G eng %U http://hdl.handle.net/21.11116/0000-0008-143B-0 %R 10.1145/3366424.3383552 %D 2020 %B The World Wide Web Conference %Z date of event: 2020-04-20 - 2020-04-24 %C Taipei, Taiwan %B Companion of The World Wide Web Conference %E El Fallah, Amal; Sukthankar, Gita; Liu, Tie-Yan; van Steen, Maarten %P 249 - 252 %I ACM %@ 978-1-4503-7024-0
[137]
N. H. Ramadhana, F. Darari, P. O. H. Putra, W. Nutt, S. Razniewski, and R. I. Akbar, “User-Centered Design for Knowledge Imbalance Analysis: A Case Study of ProWD,” in VOILA!2020, Fifth International Workshop on Visualization and Interaction for Ontologies and Linked Data, Virtual Conference, 2020.
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@inproceedings{Ramadhana_VOILA2020, TITLE = {User-Centered Design for Knowledge Imbalance Analysis: {A} Case Study of {ProWD}}, AUTHOR = {Ramadhana, Nadyah Hani and Darari, Fariz and Putra, Panca O. Hadi and Nutt, Werner and Razniewski, Simon and Akbar, Refo Ilmiya}, LANGUAGE = {eng}, ISSN = {1613-0073}, URL = {http://ceur-ws.org/Vol-2778/paper2.pdf; urn:nbn:de:0074-2778-8}, PUBLISHER = {ceur-ws.org}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {VOILA!2020, Fifth International Workshop on Visualization and Interaction for Ontologies and Linked Data}, EDITOR = {Ivanova, Valentina and Lambrix, Patrick and Pesquita, Catia and Wiens, Vitalis}, PAGES = {14--27}, EID = {2}, SERIES = {CEUR Workshop Proceedings}, VOLUME = {2778}, ADDRESS = {Virtual Conference}, }
Endnote
%0 Conference Proceedings %A Ramadhana, Nadyah Hani %A Darari, Fariz %A Putra, Panca O. Hadi %A Nutt, Werner %A Razniewski, Simon %A Akbar, Refo Ilmiya %+ External Organizations External Organizations External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T User-Centered Design for Knowledge Imbalance Analysis: A Case Study of ProWD : %G eng %U http://hdl.handle.net/21.11116/0000-0008-063B-0 %U http://ceur-ws.org/Vol-2778/paper2.pdf %D 2020 %B Fifth International Workshop on Visualization and Interaction for Ontologies and Linked Data %Z date of event: 2020-11-02 - 2020-11-02 %C Virtual Conference %B VOILA!2020 %E Ivanova, Valentina; Lambrix, Patrick; Pesquita, Catia; Wiens, Vitalis %P 14 - 27 %Z sequence number: 2 %I ceur-ws.org %B CEUR Workshop Proceedings %N 2778 %@ false %U http://ceur-ws.org/Vol-2778/paper2.pdf
[138]
S. Razniewski and P. Das, “Structured Knowledge: Have We Made Progress? An Extrinsic Study of KB Coverage over 19 Years,” in CIKM ’20, 29th ACM International Conference on Information & Knowledge Management, Virtual Event, Ireland, 2020.
Abstract
Structured world knowledge is at the foundation of knowledge-centric AI applications. Despite considerable research on knowledge base construction, beyond mere statement counts, little is known about the progress of KBs, in particular concerning their coverage, and one may wonder whether there is constant progress, or diminishing returns. In this paper we employ question answering and entity summarization as extrinsic use cases for a longitudinal study of the progress of KB coverage. Our analysis shows a near-continuous improvement of two popular KBs, DBpedia and Wikidata, over the last 19 years, with little signs of flattening out or leveling off.
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@inproceedings{razniewski2020structured, TITLE = {Structured Knowledge: {H}ave We Made Progress? {A}n Extrinsic Study of {KB} Coverage over 19 Years}, AUTHOR = {Razniewski, Simon and Das, Priyanka}, LANGUAGE = {eng}, ISBN = {978-1-4503-6859-9}, DOI = {10.1145/3340531.3417447}, PUBLISHER = {ACM}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, DATE = {2020}, ABSTRACT = {Structured world knowledge is at the foundation of knowledge-centric AI applications. Despite considerable research on knowledge base construction, beyond mere statement counts, little is known about the progress of KBs, in particular concerning their coverage, and one may wonder whether there is constant progress, or diminishing returns. In this paper we employ question answering and entity summarization as extrinsic use cases for a longitudinal study of the progress of KB coverage. Our analysis shows a near-continuous improvement of two popular KBs, DBpedia and Wikidata, over the last 19 years, with little signs of flattening out or leveling off.}, BOOKTITLE = {CIKM '20, 29th ACM International Conference on Information \& Knowledge Management}, EDITOR = {d{\textquoteright}Aquin, Mathieu and Dietze, Stefan}, PAGES = {3317--3320}, ADDRESS = {Virtual Event, Ireland}, }
Endnote
%0 Conference Proceedings %A Razniewski, Simon %A Das, Priyanka %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Structured Knowledge: Have We Made Progress? An Extrinsic Study of KB Coverage over 19 Years : %G eng %U http://hdl.handle.net/21.11116/0000-0007-FF42-0 %R 10.1145/3340531.3417447 %D 2020 %B 29th ACM International Conference on Information & Knowledge Management %Z date of event: 2020-10-19 - 2020-10-23 %C Virtual Event, Ireland %X Structured world knowledge is at the foundation of knowledge-centric AI applications. Despite considerable research on knowledge base construction, beyond mere statement counts, little is known about the progress of KBs, in particular concerning their coverage, and one may wonder whether there is constant progress, or diminishing returns. In this paper we employ question answering and entity summarization as extrinsic use cases for a longitudinal study of the progress of KB coverage. Our analysis shows a near-continuous improvement of two popular KBs, DBpedia and Wikidata, over the last 19 years, with little signs of flattening out or leveling off. %B CIKM '20 %E d&#8217;Aquin, Mathieu; Dietze, Stefan %P 3317 - 3320 %I ACM %@ 978-1-4503-6859-9
[139]
J. Romero and S. Razniewski, “Inside Quasimodo: Exploring Construction and Usage of Commonsense Knowledge,” in CIKM ’20, 29th ACM International Conference on Information & Knowledge Management, Virtual Event, Ireland, 2020.
Abstract
Structured world knowledge is at the foundation of knowledge-centric AI applications. Despite considerable research on knowledge base construction, beyond mere statement counts, little is known about the progress of KBs, in particular concerning their coverage, and one may wonder whether there is constant progress, or diminishing returns. In this paper we employ question answering and entity summarization as extrinsic use cases for a longitudinal study of the progress of KB coverage. Our analysis shows a near-continuous improvement of two popular KBs, DBpedia and Wikidata, over the last 19 years, with little signs of flattening out or leveling off.
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@inproceedings{Romero_CIKM2020, TITLE = {Inside {Quasimodo}: {E}xploring Construction and Usage of Commonsense Knowledge}, AUTHOR = {Romero, Julien and Razniewski, Simon}, LANGUAGE = {eng}, ISBN = {978-1-4503-6859-9}, DOI = {10.1145/3340531.3417416}, PUBLISHER = {ACM}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, DATE = {2020}, ABSTRACT = {Structured world knowledge is at the foundation of knowledge-centric AI applications. Despite considerable research on knowledge base construction, beyond mere statement counts, little is known about the progress of KBs, in particular concerning their coverage, and one may wonder whether there is constant progress, or diminishing returns. In this paper we employ question answering and entity summarization as extrinsic use cases for a longitudinal study of the progress of KB coverage. Our analysis shows a near-continuous improvement of two popular KBs, DBpedia and Wikidata, over the last 19 years, with little signs of flattening out or leveling off.}, BOOKTITLE = {CIKM '20, 29th ACM International Conference on Information \& Knowledge Management}, EDITOR = {d{\textquoteright}Aquin, Mathieu and Dietze, Stefan}, PAGES = {3445--3448}, ADDRESS = {Virtual Event, Ireland}, }
Endnote
%0 Conference Proceedings %A Romero, Julien %A Razniewski, Simon %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Inside Quasimodo: Exploring Construction and Usage of Commonsense Knowledge : %G eng %U http://hdl.handle.net/21.11116/0000-0008-04C6-4 %R 10.1145/3340531.3417416 %D 2020 %B 29th ACM International Conference on Information & Knowledge Management %Z date of event: 2020-10-19 - 2020-10-23 %C Virtual Event, Ireland %X Structured world knowledge is at the foundation of knowledge-centric AI applications. Despite considerable research on knowledge base construction, beyond mere statement counts, little is known about the progress of KBs, in particular concerning their coverage, and one may wonder whether there is constant progress, or diminishing returns. In this paper we employ question answering and entity summarization as extrinsic use cases for a longitudinal study of the progress of KB coverage. Our analysis shows a near-continuous improvement of two popular KBs, DBpedia and Wikidata, over the last 19 years, with little signs of flattening out or leveling off. %B CIKM '20 %E d&#8217;Aquin, Mathieu; Dietze, Stefan %P 3445 - 3448 %I ACM %@ 978-1-4503-6859-9
[140]
R. Saha Roy and A. Anand, “Question Answering over Curated and Open Web Sources,” 2020. [Online]. Available: https://arxiv.org/abs/2004.11980. (arXiv: 2004.11980)
Abstract
The last few years have seen an explosion of research on the topic of automated question answering (QA), spanning the communities of information retrieval, natural language processing, and artificial intelligence. This tutorial would cover the highlights of this really active period of growth for QA to give the audience a grasp over the families of algorithms that are currently being used. We partition research contributions by the underlying source from where answers are retrieved: curated knowledge graphs, unstructured text, or hybrid corpora. We choose this dimension of partitioning as it is the most discriminative when it comes to algorithm design. Other key dimensions are covered within each sub-topic: like the complexity of questions addressed, and degrees of explainability and interactivity introduced in the systems. We would conclude the tutorial with the most promising emerging trends in the expanse of QA, that would help new entrants into this field make the best decisions to take the community forward. Much has changed in the community since the last tutorial on QA in SIGIR 2016, and we believe that this timely overview will indeed benefit a large number of conference participants.
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@online{SahaRoy2004.11980, TITLE = {Question Answering over Curated and Open Web Sources}, AUTHOR = {Saha Roy, Rishiraj and Anand, Avishek}, LANGUAGE = {eng}, URL = {https://arxiv.org/abs/2004.11980}, EPRINT = {2004.11980}, EPRINTTYPE = {arXiv}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, ABSTRACT = {The last few years have seen an explosion of research on the topic of automated question answering (QA), spanning the communities of information retrieval, natural language processing, and artificial intelligence. This tutorial would cover the highlights of this really active period of growth for QA to give the audience a grasp over the families of algorithms that are currently being used. We partition research contributions by the underlying source from where answers are retrieved: curated knowledge graphs, unstructured text, or hybrid corpora. We choose this dimension of partitioning as it is the most discriminative when it comes to algorithm design. Other key dimensions are covered within each sub-topic: like the complexity of questions addressed, and degrees of explainability and interactivity introduced in the systems. We would conclude the tutorial with the most promising emerging trends in the expanse of QA, that would help new entrants into this field make the best decisions to take the community forward. Much has changed in the community since the last tutorial on QA in SIGIR 2016, and we believe that this timely overview will indeed benefit a large number of conference participants.}, }
Endnote
%0 Report %A Saha Roy, Rishiraj %A Anand, Avishek %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Question Answering over Curated and Open Web Sources : %G eng %U http://hdl.handle.net/21.11116/0000-0008-09CA-B %U https://arxiv.org/abs/2004.11980 %D 2020 %X The last few years have seen an explosion of research on the topic of automated question answering (QA), spanning the communities of information retrieval, natural language processing, and artificial intelligence. This tutorial would cover the highlights of this really active period of growth for QA to give the audience a grasp over the families of algorithms that are currently being used. We partition research contributions by the underlying source from where answers are retrieved: curated knowledge graphs, unstructured text, or hybrid corpora. We choose this dimension of partitioning as it is the most discriminative when it comes to algorithm design. Other key dimensions are covered within each sub-topic: like the complexity of questions addressed, and degrees of explainability and interactivity introduced in the systems. We would conclude the tutorial with the most promising emerging trends in the expanse of QA, that would help new entrants into this field make the best decisions to take the community forward. Much has changed in the community since the last tutorial on QA in SIGIR 2016, and we believe that this timely overview will indeed benefit a large number of conference participants. %K Computer Science, Information Retrieval, cs.IR,Computer Science, Computation and Language, cs.CL
[141]
R. Saha Roy and A. Anand, “Question Answering over Curated and Open Web Sources,” in SIGIR ’20, 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, China, 2020.
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@inproceedings{SahaRoy_SIGIR20, TITLE = {Question Answering over Curated and Open Web Sources}, AUTHOR = {Saha Roy, Rishiraj and Anand, Avishek}, LANGUAGE = {eng}, ISBN = {9781450380164}, DOI = {10.1145/3397271.3401421}, PUBLISHER = {ACM}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {SIGIR '20, 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval}, PAGES = {2432--2435}, ADDRESS = {Virtual Event, China}, }
Endnote
%0 Conference Proceedings %A Saha Roy, Rishiraj %A Anand, Avishek %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Question Answering over Curated and Open Web Sources : %G eng %U http://hdl.handle.net/21.11116/0000-0008-02F6-0 %R 10.1145/3397271.3401421 %D 2020 %B 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval %Z date of event: 2020-07-25 - 2020-07-30 %C Virtual Event, China %B SIGIR '20 %P 2432 - 2435 %I ACM %@ 9781450380164
[142]
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, vol. 115, 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}, MARGINALMARK = {$\bullet$}, JOURNAL = {Wireless Personal Communications}, VOLUME = {115}, PAGES = {335--361}, }
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 %V 115 %& 335 %P 335 - 361 %I Springer %C New York, NY %@ false
[143]
X. Shen, E. Chang, H. Su, C. Niu, and D. Klakow, “Neural Data-to-Text Generation via Jointly Learning the Segmentation and Correspondence,” in The 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020), 2020.
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@inproceedings{shen2020neural, TITLE = {Neural Data-to-Text Generation via Jointly Learning the Segmentation and Correspondence}, AUTHOR = {Shen, Xiaoyu and Chang, Ernie and Su, Hui and Niu, Cheng and Klakow, Dietrich}, LANGUAGE = {eng}, ISBN = {978-1-952148-25-5}, URL = {https://www.aclweb.org/anthology/2020.acl-main.641}, DOI = {10.18653/v1/2020.acl-main.641}, PUBLISHER = {ACL}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {The 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020)}, EDITOR = {Jurafsky, Dan and Chai, Joyce and Schluter, Natalie and Tetreault, Joel}, PAGES = {7155--7165}, }
Endnote
%0 Conference Proceedings %A Shen, Xiaoyu %A Chang, Ernie %A Su, Hui %A Niu, Cheng %A Klakow, Dietrich %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations External Organizations %T Neural Data-to-Text Generation via Jointly Learning the Segmentation and Correspondence : %G eng %U http://hdl.handle.net/21.11116/0000-0008-141B-4 %U https://www.aclweb.org/anthology/2020.acl-main.641 %R 10.18653/v1/2020.acl-main.641 %D 2020 %B 58th Annual Meeting of the Association for Computational Linguistics %Z date of event: 2020-07-05 - 2020-07-10 %B The 58th Annual Meeting of the Association for Computational Linguistics %E Jurafsky, Dan; Chai, Joyce; Schluter, Natalie; Tetreault, Joel %P 7155 - 7165 %I ACL %@ 978-1-952148-25-5
[144]
H. Su, X. Shen, S. Zhao, Z. Xiao, P. Hu, C. Niu, and J. Zhou, “Diversifying Dialogue Generation with Non-Conversational Text,” in The 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020), 2020.
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@inproceedings{su2020diversifying, TITLE = {Diversifying Dialogue Generation with Non-Conversational Text}, AUTHOR = {Su, Hui and Shen, Xiaoyu and Zhao, Sanqiang and Xiao, Zhou and Hu, Pengwei and Niu, Cheng and Zhou, Jie}, LANGUAGE = {eng}, ISBN = {978-1-952148-25-5}, URL = {https://www.aclweb.org/anthology/2020.acl-main.634}, DOI = {10.18653/v1/2020.acl-main.634}, PUBLISHER = {ACL}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {The 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020)}, EDITOR = {Jurafsky, Dan and Chai, Joyce and Schluter, Natalie and Tetreault, Joel}, PAGES = {7087--7097}, }
Endnote
%0 Conference Proceedings %A Su, Hui %A Shen, Xiaoyu %A Zhao, Sanqiang %A Xiao, Zhou %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 Diversifying Dialogue Generation with Non-Conversational Text : %G eng %U http://hdl.handle.net/21.11116/0000-0008-14AF-D %U https://www.aclweb.org/anthology/2020.acl-main.634 %R 10.18653/v1/2020.acl-main.634 %D 2020 %B 58th Annual Meeting of the Association for Computational Linguistics %Z date of event: 2020-07-05 - 2020-07-10 %B The 58th Annual Meeting of the Association for Computational Linguistics %E Jurafsky, Dan; Chai, Joyce; Schluter, Natalie; Tetreault, Joel %P 7087 - 7097 %I ACL %@ 978-1-952148-25-5
[145]
S. Sukarieh, “SPRAP: Detecting Opinion Spam Campaigns in Online Rating Services,” Universität des Saarlandes, Saarbrücken, 2020.
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@mastersthesis{sukarieh:20:sprap, TITLE = {{SPRAP}: Detecting Opinion Spam Campaigns in Online Rating Services}, AUTHOR = {Sukarieh, Sandra}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, DATE = {2020}, }
Endnote
%0 Thesis %A Sukarieh, Sandra %Y Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T SPRAP: Detecting Opinion Spam Campaigns in Online Rating Services : %G eng %U http://hdl.handle.net/21.11116/0000-0007-FF00-A %I Universit&#228;t des Saarlandes %C Saarbr&#252;cken %D 2020 %V master %9 master
[146]
C. Sutton, M. Boley, L. Ghiringhelli, M. Rupp, J. Vreeken, and M. Scheffler,, “Identifying Domains of Applicability of Machine Learning Models for Materials Science,” Nature Communications, vol. 11, 2020.
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@article{sutton:20:natcomm, TITLE = {Identifying Domains of Applicability of Machine Learning Models for Materials Science}, AUTHOR = {Sutton, Chris and Boley, Mario and Ghiringhelli, Luca and Rupp, Matthias and Vreeken, Jilles and Scheffler,, Matthias}, LANGUAGE = {eng}, ISSN = {2041-1723}, DOI = {10.1038/s41467-020-17112-9}, PUBLISHER = {Nature Publishing Group}, ADDRESS = {London}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, JOURNAL = {Nature Communications}, VOLUME = {11}, EID = {4428}, }
Endnote
%0 Journal Article %A Sutton, Chris %A Boley, Mario %A Ghiringhelli, Luca %A Rupp, Matthias %A Vreeken, Jilles %A Scheffler,, Matthias %+ External Organizations External Organizations External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Identifying Domains of Applicability of Machine Learning Models for Materials Science : %G eng %U http://hdl.handle.net/21.11116/0000-0008-26CF-5 %R 10.1038/s41467-020-17112-9 %7 2020 %D 2020 %J Nature Communications %O Nat. Commun. %V 11 %Z sequence number: 4428 %I Nature Publishing Group %C London %@ false
[147]
E. Terolli, P. Ernst, and G. Weikum, “Focused Query Expansion with Entity Cores for Patient-Centric Health Search,” in The Semantic Web -- ISWC 2020, Athens, Greece (Virtual Conference), 2020.
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@inproceedings{Terolli_ISWC2020, TITLE = {Focused Query Expansion with Entity Cores for Patient-Centric Health Search}, AUTHOR = {Terolli, Erisa and Ernst, Patrick and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-3-030-62418-7}, DOI = {10.1007/978-3-030-62419-4_31}, PUBLISHER = {Springer}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, DATE = {2020}, BOOKTITLE = {The Semantic Web -- ISWC 2020}, EDITOR = {Pan, Jeff Z. and Tamma, Valentina and D'Amato, Claudia and Janowicz, Krzysztof and Fu, Bo and Polleres, Axel and Seneviratne, Oshani and Kagal, Lalana}, PAGES = {547--564}, SERIES = {Lecture Notes in Computer Science}, VOLUME = {12506}, ADDRESS = {Athens, Greece (Virtual Conference)}, }
Endnote
%0 Conference Proceedings %A Terolli, Erisa %A Ernst, Patrick %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 Focused Query Expansion with Entity Cores for Patient-Centric Health Search : %G eng %U http://hdl.handle.net/21.11116/0000-0007-78D7-0 %R 10.1007/978-3-030-62419-4_31 %D 2020 %B 19th International Semantic Web Conference %Z date of event: 2020-11-02 - 2020-11-06 %C Athens, Greece (Virtual Conference) %B The Semantic Web -- ISWC 2020 %E Pan, Jeff Z.; Tamma, Valentina; D'Amato, Claudia; Janowicz, Krzysztof; Fu, Bo; Polleres, Axel; Seneviratne, Oshani; Kagal, Lalana %P 547 - 564 %I Springer %@ 978-3-030-62418-7 %B Lecture Notes in Computer Science %N 12506
[148]
A. Tigunova, “Extracting Personal Information from Conversations,” in Companion of The World Wide Web Conference (WWW 2020), Taipei, Taiwan, 2020.
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@inproceedings{tigunova2020extracting, TITLE = {Extracting Personal Information from Conversations}, AUTHOR = {Tigunova, Anna}, LANGUAGE = {eng}, ISBN = {978-1-4503-7024-0}, DOI = {10.1145/3366424.3382089}, PUBLISHER = {ACM}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Companion of The World Wide Web Conference (WWW 2020)}, EDITOR = {El Fallah, Amal and Sukthankar, Gita and Liu, Tie-Yan and van Steen, Maarten}, PAGES = {284--288}, ADDRESS = {Taipei, Taiwan}, }
Endnote
%0 Conference Proceedings %A Tigunova, Anna %+ Databases and Information Systems, MPI for Informatics, Max Planck Society %T Extracting Personal Information from Conversations : %G eng %U http://hdl.handle.net/21.11116/0000-0007-F845-4 %R 10.1145/3366424.3382089 %D 2020 %B The World Wide Web Conference %Z date of event: 2020-04-20 - 2020-04-24 %C Taipei, Taiwan %B Companion of The World Wide Web Conference %E El Fallah, Amal; Sukthankar, Gita; Liu, Tie-Yan; van Steen, Maarten %P 284 - 288 %I ACM %@ 978-1-4503-7024-0
[149]
A. Tigunova, A. Yates, P. Mirza, and G. Weikum, “CHARM: Inferring Personal Attributes from Conversations,” in The 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020), Online, 2020.
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@inproceedings{Tigunova_EMNLP20, TITLE = {{CHARM}: {I}nferring Personal Attributes from Conversations}, AUTHOR = {Tigunova, Anna and Yates, Andrew and Mirza, Paramita and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-952148-60-6}, URL = {https://www.aclweb.org/anthology/2020.emnlp-main.434}, DOI = {10.18653/v1/2020.emnlp-main.434}, PUBLISHER = {ACM}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {The 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020)}, EDITOR = {Webber, Bonnie and Cohn, Trevor and He, Yulan and Liu, Yang}, PAGES = {5391--5404}, ADDRESS = {Online}, }
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 CHARM: Inferring Personal Attributes from Conversations : %G eng %U http://hdl.handle.net/21.11116/0000-0007-EEDB-7 %U https://www.aclweb.org/anthology/2020.emnlp-main.434 %R 10.18653/v1/2020.emnlp-main.434 %D 2020 %B Conference on Empirical Methods in Natural Language Processing %Z date of event: 2020-11-16 - 2020-11-20 %C Online %B The 2020 Conference on Empirical Methods in Natural Language Processing %E Webber, Bonnie; Cohn, Trevor; He, Yulan; Liu, Yang %P 5391 - 5404 %I ACM %@ 978-1-952148-60-6 %U https://www.aclweb.org/anthology/2020.emnlp-main.434.pdf
[150]
A. Tigunova, P. Mirza, A. Yates, and G. Weikum, “RedDust: a Large Reusable Dataset of Reddit User Traits,” in Twelfth Language Resources and Evaluation Conference (LREC 2020), Marseille, France, 2020.
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@inproceedings{Tigunova_ELREC20, TITLE = {{RedDust}: a Large Reusable Dataset of {Reddit} User Traits}, AUTHOR = {Tigunova, Anna and Mirza, Paramita and Yates, Andrew and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {979-10-95546-34-4}, URL = {https://www.aclweb.org/anthology/2020.lrec-1.751}, PUBLISHER = {ELRA}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Twelfth Language Resources and Evaluation Conference (LREC 2020)}, EDITOR = {Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Moreno, Asuncion and Odiik, Jan and Piperidis, Stelios}, PAGES = {6118--6126}, ADDRESS = {Marseille, France}, }
Endnote
%0 Conference Proceedings %A Tigunova, Anna %A Mirza, Paramita %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 RedDust: a Large Reusable Dataset of Reddit User Traits : %G eng %U http://hdl.handle.net/21.11116/0000-0007-F0A9-B %U https://www.aclweb.org/anthology/2020.lrec-1.751 %D 2020 %B 12th Language Resources and Evaluation Conference %Z date of event: 2020-05-11 - 2020-05-16 %C Marseille, France %B Twelfth Language Resources and Evaluation Conference %E Calzolari, Nicoletta; B&#233;chet, Fr&#233;d&#233;ric; Blache, Philippe; Choukri, Khalid; Cieri, Christopher; Declerck, Thierry; Goggi, Sara; Mariani, Joseph; Mazo, H&#233;l&#232;ne; Moreno, Asuncion; Odiik, Jan; Piperidis, Stelios %P 6118 - 6126 %I ELRA %@ 979-10-95546-34-4 %U https://www.aclweb.org/anthology/2020.lrec-1.751.pdf
[151]
G. H. Torbati, A. Yates, and G. Weikum, “Personalized Entity Search by Sparse and Scrutable User Profiles,” in CHIIR ’20, Fifth ACM SIGIR Conference on Human Information Interaction and Retrieval, Vancouver, BC, Canada, 2020.
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@inproceedings{CHIIR2020Torbati, TITLE = {Personalized Entity Search by Sparse and Scrutable User Profiles}, AUTHOR = {Torbati, Ghazaleh Haratinezhad and Yates, Andrew and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {9781450368926}, DOI = {10.1145/3343413.3378011}, PUBLISHER = {ACM}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {CHIIR '20, Fifth ACM SIGIR Conference on Human Information Interaction and Retrieval}, EDITOR = {O'Brain, Heather and Freund, Luanne}, PAGES = {427--431}, ADDRESS = {Vancouver, BC, Canada}, }
Endnote
%0 Conference Proceedings %A Torbati, Ghazaleh Haratinezhad %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 %T Personalized Entity Search by Sparse and Scrutable User Profiles : %G eng %U http://hdl.handle.net/21.11116/0000-0007-EAD7-F %R 10.1145/3343413.3378011 %D 2020 %B Fifth ACM SIGIR Conference on Human Information Interaction and Retrieval %Z date of event: 2020-03-14 - 2020-03-18 %C Vancouver, BC, Canada %B CHIIR '20 %E O'Brain, Heather; Freund, Luanne %P 427 - 431 %I ACM %@ 9781450368926
[152]
T.-K. Tran, M. H. Gad-Elrab, D. Stepanova, E. Kharlamov, and J. Strötgen, “Fast Computation of Explanations for Inconsistency in Large-Scale Knowledge Graphs,” in Companion of The World Wide Web Conference (WWW 2020), Taipei, Taiwan, 2020.
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@inproceedings{DBLP:conf/www/TranG0KS20, TITLE = {Fast Computation of Explanations for Inconsistency in Large-Scale Knowledge Graphs}, AUTHOR = {Tran, Trung-Kien and Gad-Elrab, Mohamed Hassan and Stepanova, Daria and Kharlamov, Evgeny and Str{\"o}tgen, Jannik}, LANGUAGE = {eng}, ISBN = {978-1-4503-7024-0}, DOI = {10.1145/3366423.3380014}, PUBLISHER = {ACM}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Companion of The World Wide Web Conference (WWW 2020)}, EDITOR = {El Fallah, Amal and Sukthankar, Gita and Liu, Tie-Yan and van Steen, Maarten}, PAGES = {2613--2619}, ADDRESS = {Taipei, Taiwan}, }
Endnote
%0 Conference Proceedings %A Tran, Trung-Kien %A Gad-Elrab, Mohamed Hassan %A Stepanova, Daria %A Kharlamov, Evgeny %A Str&#246;tgen, Jannik %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations %T Fast Computation of Explanations for Inconsistency in Large-Scale Knowledge Graphs : %G eng %U http://hdl.handle.net/21.11116/0000-0007-F861-4 %R 10.1145/3366423.3380014 %D 2020 %B The World Wide Web Conference %Z date of event: 2020-04-20 - 2020-04-24 %C Taipei, Taiwan %B Companion of The World Wide Web Conference %E El Fallah, Amal; Sukthankar, Gita; Liu, Tie-Yan; van Steen, Maarten %P 2613 - 2619 %I ACM %@ 978-1-4503-7024-0
[153]
L. Wang, X. Shen, G. de Melo, and G. Weikum, “Cross-Domain Learning for Classifying Propaganda in Online Contents,” in Proceedings of the 2020 Truth and Trust Online Conference (TTO 2020), Virtual, 2020.
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@inproceedings{Wang_TTO2020, TITLE = {Cross-Domain Learning for Classifying Propaganda in Online Contents}, AUTHOR = {Wang, Liqiang and Shen, Xiaoyu and de Melo, Gerard and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-7359904-0-8}, URL = {https://truthandtrustonline.com/wp-content/uploads/2020/10/TTO03.pdf}, PUBLISHER = {Hacks Hackers}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of the 2020 Truth and Trust Online Conference (TTO 2020)}, EDITOR = {De Cristofaro, Emiliano and Nakov, Preslav}, PAGES = {21--31}, ADDRESS = {Virtual}, }
Endnote
%0 Conference Proceedings %A Wang, Liqiang %A Shen, Xiaoyu %A de Melo, Gerard %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 Cross-Domain Learning for Classifying Propaganda in Online Contents : %G eng %U http://hdl.handle.net/21.11116/0000-0007-F169-3 %U https://truthandtrustonline.com/wp-content/uploads/2020/10/TTO03.pdf %D 2020 %B Truth and Trust Online Conference %Z date of event: 2020-10-16 - 2020-10-17 %C Virtual %B Proceedings of the 2020 Truth and Trust Online Conference %E De Cristofaro, Emiliano; Nakov, Preslav %P 21 - 31 %I Hacks Hackers %@ 978-1-7359904-0-8 %U https://truthandtrustonline.com/wp-content/uploads/2020/10/TTO03.pdf
[154]
L. Wang, X. Shen, G. de Melo, and G. Weikum, “Cross-Domain Learning for Classifying Propaganda in Online Contents,” 2020. [Online]. Available: https://arxiv.org/abs/2011.06844. (arXiv: 2011.06844)
Abstract
As news and social media exhibit an increasing amount of manipulative polarized content, detecting such propaganda has received attention as a new task for content analysis. Prior work has focused on supervised learning with training data from the same domain. However, as propaganda can be subtle and keeps evolving, manual identification and proper labeling are very demanding. As a consequence, training data is a major bottleneck. In this paper, we tackle this bottleneck and present an approach to leverage cross-domain learning, based on labeled documents and sentences from news and tweets, as well as political speeches with a clear difference in their degrees of being propagandistic. We devise informative features and build various classifiers for propaganda labeling, using cross-domain learning. Our experiments demonstrate the usefulness of this approach, and identify difficulties and limitations in various configurations of sources and targets for the transfer step. We further analyze the influence of various features, and characterize salient indicators of propaganda.
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@online{Wang_2011.06844, TITLE = {Cross-Domain Learning for Classifying Propaganda in Online Contents}, AUTHOR = {Wang, Liqiang and Shen, Xiaoyu and de Melo, Gerard and Weikum, Gerhard}, LANGUAGE = {eng}, URL = {https://arxiv.org/abs/2011.06844}, EPRINT = {2011.06844}, EPRINTTYPE = {arXiv}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, ABSTRACT = {As news and social media exhibit an increasing amount of manipulative polarized content, detecting such propaganda has received attention as a new task for content analysis. Prior work has focused on supervised learning with training data from the same domain. However, as propaganda can be subtle and keeps evolving, manual identification and proper labeling are very demanding. As a consequence, training data is a major bottleneck. In this paper, we tackle this bottleneck and present an approach to leverage cross-domain learning, based on labeled documents and sentences from news and tweets, as well as political speeches with a clear difference in their degrees of being propagandistic. We devise informative features and build various classifiers for propaganda labeling, using cross-domain learning. Our experiments demonstrate the usefulness of this approach, and identify difficulties and limitations in various configurations of sources and targets for the transfer step. We further analyze the influence of various features, and characterize salient indicators of propaganda.}, }
Endnote
%0 Report %A Wang, Liqiang %A Shen, Xiaoyu %A de Melo, Gerard %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 Cross-Domain Learning for Classifying Propaganda in Online Contents : %G eng %U http://hdl.handle.net/21.11116/0000-0007-FEBF-5 %U https://arxiv.org/abs/2011.06844 %D 2020 %X As news and social media exhibit an increasing amount of manipulative polarized content, detecting such propaganda has received attention as a new task for content analysis. Prior work has focused on supervised learning with training data from the same domain. However, as propaganda can be subtle and keeps evolving, manual identification and proper labeling are very demanding. As a consequence, training data is a major bottleneck. In this paper, we tackle this bottleneck and present an approach to leverage cross-domain learning, based on labeled documents and sentences from news and tweets, as well as political speeches with a clear difference in their degrees of being propagandistic. We devise informative features and build various classifiers for propaganda labeling, using cross-domain learning. Our experiments demonstrate the usefulness of this approach, and identify difficulties and limitations in various configurations of sources and targets for the transfer step. We further analyze the influence of various features, and characterize salient indicators of propaganda. %K Computer Science, Computation and Language, cs.CL
[155]
G. Weikum, L. Dong, S. Razniewski, and F. Suchanek, “Machine Knowledge: Creation and Curation of Comprehensive Knowledge Bases,” 2020. [Online]. Available: https://arxiv.org/abs/2009.11564. (arXiv: 2009.11564)
Abstract
Equipping machines with comprehensive knowledge of the world's entities and their relationships has been a long-standing goal of AI. Over the last decade, large-scale knowledge bases, also known as knowledge graphs, have been automatically constructed from web contents and text sources, and have become a key asset for search engines. This machine knowledge can be harnessed to semantically interpret textual phrases in news, social media and web tables, and contributes to question answering, natural language processing and data analytics. This article surveys fundamental concepts and practical methods for creating and curating large knowledge bases. It covers models and methods for discovering and canonicalizing entities and their semantic types and organizing them into clean taxonomies. On top of this, the article discusses the automatic extraction of entity-centric properties. To support the long-term life-cycle and the quality assurance of machine knowledge, the article presents methods for constructing open schemas and for knowledge curation. Case studies on academic projects and industrial knowledge graphs complement the survey of concepts and methods.
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@online{Weikum_2009.11564, TITLE = {Machine Knowledge: {C}reation and Curation of Comprehensive Knowledge Bases}, AUTHOR = {Weikum, Gerhard and Dong, Luna and Razniewski, Simon and Suchanek, Fabian}, LANGUAGE = {eng}, URL = {https://arxiv.org/abs/2009.11564}, EPRINT = {2009.11564}, EPRINTTYPE = {arXiv}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Equipping machines with comprehensive knowledge of the world's entities and their relationships has been a long-standing goal of AI. Over the last decade, large-scale knowledge bases, also known as knowledge graphs, have been automatically constructed from web contents and text sources, and have become a key asset for search engines. This machine knowledge can be harnessed to semantically interpret textual phrases in news, social media and web tables, and contributes to question answering, natural language processing and data analytics. This article surveys fundamental concepts and practical methods for creating and curating large knowledge bases. It covers models and methods for discovering and canonicalizing entities and their semantic types and organizing them into clean taxonomies. On top of this, the article discusses the automatic extraction of entity-centric properties. To support the long-term life-cycle and the quality assurance of machine knowledge, the article presents methods for constructing open schemas and for knowledge curation. Case studies on academic projects and industrial knowledge graphs complement the survey of concepts and methods.}, }
Endnote
%0 Report %A Weikum, Gerhard %A Dong, Luna %A Razniewski, Simon %A Suchanek, Fabian %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Machine Knowledge: Creation and Curation of Comprehensive Knowledge Bases : %G eng %U http://hdl.handle.net/21.11116/0000-0007-F1A6-D %U https://arxiv.org/abs/2009.11564 %D 2020 %X Equipping machines with comprehensive knowledge of the world's entities and their relationships has been a long-standing goal of AI. Over the last decade, large-scale knowledge bases, also known as knowledge graphs, have been automatically constructed from web contents and text sources, and have become a key asset for search engines. This machine knowledge can be harnessed to semantically interpret textual phrases in news, social media and web tables, and contributes to question answering, natural language processing and data analytics. This article surveys fundamental concepts and practical methods for creating and curating large knowledge bases. It covers models and methods for discovering and canonicalizing entities and their semantic types and organizing them into clean taxonomies. On top of this, the article discusses the automatic extraction of entity-centric properties. To support the long-term life-cycle and the quality assurance of machine knowledge, the article presents methods for constructing open schemas and for knowledge curation. Case studies on academic projects and industrial knowledge graphs complement the survey of concepts and methods. %K Computer Science, Artificial Intelligence, cs.AI,Computer Science, Databases, cs.DB,Computer Science, General Literature, cs.GL
[156]
G. Weikum, “Entities with Quantities,” Bulletin of the Technical Committee on Data Engineering, vol. 43, no. 1, 2020.
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@article{Weikum_Entities2020, TITLE = {Entities with Quantities}, AUTHOR = {Weikum, Gerhard}, LANGUAGE = {eng}, URL = {http://sites.computer.org/debull/A20mar/p4.pdf}, PUBLISHER = {IEEE Computer Society}, ADDRESS = {Los Alamitos, CA}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, JOURNAL = {Bulletin of the Technical Committee on Data Engineering}, VOLUME = {43}, NUMBER = {1}, PAGES = {4--8}, }
Endnote
%0 Journal Article %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society %T Entities with Quantities : %G eng %U http://hdl.handle.net/21.11116/0000-0007-EBBB-E %U http://sites.computer.org/debull/A20mar/p4.pdf %7 2020 %D 2020 %J Bulletin of the Technical Committee on Data Engineering %V 43 %N 1 %& 4 %P 4 - 8 %I IEEE Computer Society %C Los Alamitos, CA
[157]
B. Xu, S. Qiu, J. Zhang, Y. Wang, X. Shen, and G. de Melo, “Data Augmentation for Multiclass Utterance Classification - A Systematic Study,” in The 28th International Conference on Computational Linguistics (COLING 2020), Barcelona, Spain (Online), 2020.
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@inproceedings{xu2020data, TITLE = {Data Augmentation for Multiclass Utterance Classification -- A Systematic Study}, AUTHOR = {Xu, Binxia and Qiu, Siyuan and Zhang, Jie and Wang, Yafang and Shen, Xiaoyu and de Melo, Gerard}, LANGUAGE = {eng}, ISBN = {978-1-952148-27-9}, URL = {https://www.aclweb.org/anthology/2020.coling-main.479}, DOI = {10.18653/v1/2020.coling-main.479}, PUBLISHER = {ACL}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {The 28th International Conference on Computational Linguistics (COLING 2020)}, EDITOR = {Scott, Donia and Bel, Nuria and Zong, Chengqing}, PAGES = {5494--5506}, ADDRESS = {Barcelona, Spain (Online)}, }
Endnote
%0 Conference Proceedings %A Xu, Binxia %A Qiu, Siyuan %A Zhang, Jie %A Wang, Yafang %A Shen, Xiaoyu %A de Melo, Gerard %+ External Organizations External Organizations External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Data Augmentation for Multiclass Utterance Classification - A Systematic Study : %G eng %U http://hdl.handle.net/21.11116/0000-0008-1498-6 %U https://www.aclweb.org/anthology/2020.coling-main.479 %R 10.18653/v1/2020.coling-main.479 %D 2020 %B The 28th International Conferenceon Computational Linguistics %Z date of event: 2020-12-08 - 2020-12-13 %C Barcelona, Spain (Online) %B The 28th International Conference on Computational Linguistics %E Scott, Donia; Bel, Nuria; Zong, Chengqing %P 5494 - 5506 %I ACL %@ 978-1-952148-27-9
[158]
A. Yates, K. M. Jose, X. Zhang, and J. Lin, “Flexible IR Pipelines with Capreolus,” in CIKM ’20, 29th ACM International Conference on Information & Knowledge Management, Virtual Event, Ireland, 2020.
Abstract
Structured world knowledge is at the foundation of knowledge-centric AI applications. Despite considerable research on knowledge base construction, beyond mere statement counts, little is known about the progress of KBs, in particular concerning their coverage, and one may wonder whether there is constant progress, or diminishing returns. In this paper we employ question answering and entity summarization as extrinsic use cases for a longitudinal study of the progress of KB coverage. Our analysis shows a near-continuous improvement of two popular KBs, DBpedia and Wikidata, over the last 19 years, with little signs of flattening out or leveling off.
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@inproceedings{Yates_CIKM2020, TITLE = {Flexible {IR} Pipelines with {Capreolus}}, AUTHOR = {Yates, Andrew and Jose, Kevin Martin and Zhang, Xinyu and Lin, Jimmy}, LANGUAGE = {eng}, ISBN = {978-1-4503-6859-9}, DOI = {10.1145/3340531.3412780}, PUBLISHER = {ACM}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, DATE = {2020}, ABSTRACT = {Structured world knowledge is at the foundation of knowledge-centric AI applications. Despite considerable research on knowledge base construction, beyond mere statement counts, little is known about the progress of KBs, in particular concerning their coverage, and one may wonder whether there is constant progress, or diminishing returns. In this paper we employ question answering and entity summarization as extrinsic use cases for a longitudinal study of the progress of KB coverage. Our analysis shows a near-continuous improvement of two popular KBs, DBpedia and Wikidata, over the last 19 years, with little signs of flattening out or leveling off.}, BOOKTITLE = {CIKM '20, 29th ACM International Conference on Information \& Knowledge Management}, EDITOR = {d{\textquoteright}Aquin, Mathieu and Dietze, Stefan}, PAGES = {3181--3188}, ADDRESS = {Virtual Event, Ireland}, }
Endnote
%0 Conference Proceedings %A Yates, Andrew %A Jose, Kevin Martin %A Zhang, Xinyu %A Lin, Jimmy %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations %T Flexible IR Pipelines with Capreolus : %G eng %U http://hdl.handle.net/21.11116/0000-0008-066A-B %R 10.1145/3340531.3412780 %D 2020 %B 29th ACM International Conference on Information & Knowledge Management %Z date of event: 2020-10-19 - 2020-10-23 %C Virtual Event, Ireland %X Structured world knowledge is at the foundation of knowledge-centric AI applications. Despite considerable research on knowledge base construction, beyond mere statement counts, little is known about the progress of KBs, in particular concerning their coverage, and one may wonder whether there is constant progress, or diminishing returns. In this paper we employ question answering and entity summarization as extrinsic use cases for a longitudinal study of the progress of KB coverage. Our analysis shows a near-continuous improvement of two popular KBs, DBpedia and Wikidata, over the last 19 years, with little signs of flattening out or leveling off. %B CIKM '20 %E d&#8217;Aquin, Mathieu; Dietze, Stefan %P 3181 - 3188 %I ACM %@ 978-1-4503-6859-9
[159]
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 ’20, 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}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {WSDM '20, 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 '20 %E Caverlee, James; Hu, Xia Ben %P 861 - 864 %I ACM %@ 9781450368223
[160]
Z. Zheng, K. Hui, B. He, X. Han, L. Sun, and A. Yates, “BERT-QE: Contextualized Query Expansion for Document Re-ranking,” in Findings of the ACL: EMNLP 2020, Online, 2020.
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@inproceedings{Zheng_EMNLP20, TITLE = {{BERT-QE}: {C}ontextualized Query Expansion for Document Re-ranking}, AUTHOR = {Zheng, Zhi and Hui, Kai and He, Ben and Han, Xianpei and Sun, Le and Yates, Andrew}, LANGUAGE = {eng}, ISBN = {978-1-952148-90-3}, URL = {https://www.aclweb.org/anthology/2020.findings-emnlp.424}, DOI = {10.18653/v1/2020.findings-emnlp.424}, PUBLISHER = {ACM}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Findings of the ACL: EMNLP 2020}, EDITOR = {Cohn, Trevor and He, Yulan and Liu, Yang}, PAGES = {4718--4728}, SERIES = {Findings of the Association for Computational Linguistics}, VOLUME = {1}, ADDRESS = {Online}, }
Endnote
%0 Conference Proceedings %A Zheng, Zhi %A Hui, Kai %A He, Ben %A Han, Xianpei %A Sun, Le %A Yates, Andrew %+ External Organizations External Organizations External Organizations External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T BERT-QE: Contextualized Query Expansion for Document Re-ranking : %G eng %U http://hdl.handle.net/21.11116/0000-0008-0687-9 %U https://www.aclweb.org/anthology/2020.findings-emnlp.424 %R 10.18653/v1/2020.findings-emnlp.424 %D 2020 %B Conference on Empirical Methods in Natural Language Processing %Z date of event: 2020-11-16 - 2020-11-20 %C Online %B Findings of the ACL: EMNLP 2020 %E Cohn, Trevor; He, Yulan; Liu, Yang %P 4718 - 4728 %I ACM %@ 978-1-952148-90-3 %B Findings of the Association for Computational Linguistics %N 1 %U https://www.aclweb.org/anthology/2020.findings-emnlp.424.pdf
[161]
Z. Zheng, K. Hui, B. He, X. Han, L. Sun, and A. Yates, “BERT-QE: Contextualized Query Expansion for Document Re-ranking,” 2020. [Online]. Available: https://arxiv.org/abs/2009.07258. (arXiv: 2009.07258)
Abstract
Query expansion aims to mitigate the mismatch between the language used in a query and in a document. However, query expansion methods can suffer from introducing non-relevant information when expanding the query. To bridge this gap, inspired by recent advances in applying contextualized models like BERT to the document retrieval task, this paper proposes a novel query expansion model that leverages the strength of the BERT model to select relevant document chunks for expansion. In evaluation on the standard TREC Robust04 and GOV2 test collections, the proposed BERT-QE model significantly outperforms BERT-Large models.
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@online{Zheng2009.07258, TITLE = {{BERT}-{QE}: Contextualized Query Expansion for Document Re-ranking}, AUTHOR = {Zheng, Zhi and Hui, Kai and He, Ben and Han, Xianpei and Sun, Le and Yates, Andrew}, LANGUAGE = {eng}, URL = {https://arxiv.org/abs/2009.07258}, EPRINT = {2009.07258}, EPRINTTYPE = {arXiv}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Query expansion aims to mitigate the mismatch between the language used in a query and in a document. However, query expansion methods can suffer from introducing non-relevant information when expanding the query. To bridge this gap, inspired by recent advances in applying contextualized models like BERT to the document retrieval task, this paper proposes a novel query expansion model that leverages the strength of the BERT model to select relevant document chunks for expansion. In evaluation on the standard TREC Robust04 and GOV2 test collections, the proposed BERT-QE model significantly outperforms BERT-Large models.}, }
Endnote
%0 Report %A Zheng, Zhi %A Hui, Kai %A He, Ben %A Han, Xianpei %A Sun, Le %A Yates, Andrew %+ External Organizations External Organizations External Organizations External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T BERT-QE: Contextualized Query Expansion for Document Re-ranking : %G eng %U http://hdl.handle.net/21.11116/0000-0008-06D5-1 %U https://arxiv.org/abs/2009.07258 %D 2020 %X Query expansion aims to mitigate the mismatch between the language used in a query and in a document. However, query expansion methods can suffer from introducing non-relevant information when expanding the query. To bridge this gap, inspired by recent advances in applying contextualized models like BERT to the document retrieval task, this paper proposes a novel query expansion model that leverages the strength of the BERT model to select relevant document chunks for expansion. In evaluation on the standard TREC Robust04 and GOV2 test collections, the proposed BERT-QE model significantly outperforms BERT-Large models. %K Computer Science, Information Retrieval, cs.IR,Computer Science, Computation and Language, cs.CL
2019
[162]
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 Universit&#228;t des Saarlandes %C Saarbr&#252;cken %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.
[163]
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
[164]
A. Abujabal, “Question Answering over Knowledge Bases with Continuous Learning,” Universität des Saarlandes, Saarbrücken, 2019.
Abstract
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@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
[165]
M. Alikhani, S. Nag Chowdhury, G. de Melo, and M. Stone, “CITE: A Corpus Of Text-Image Discourse Relations,” 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{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 = {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 = {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 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 570 - 575 %I ACL %@ 978-1-950737-13-0 %U https://aclweb.org/anthology/papers/N/N19/N19-1056/
[166]
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
[167]
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 (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 (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 %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
[168]
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 Universit&#228;t des Saarlandes %C Saarbr&#252;cken %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
[169]
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_WWW2019b, 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)}, EDITOR = {McAuley, Julian}, 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 %E McAuley, Julian %P 2623 - 2629 %I ACM %@ 978-1-4503-6674-8
[170]
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
[171]
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
[172]
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
[173]
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
[174]
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
[175]
S. A. Cotop, “How to be Grim,” Universität des Saarlandes, Saarbrücken, 2019.
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@mastersthesis{cotop:19:grim, TITLE = {How to be Grim}, AUTHOR = {Cotop, Simina Ana}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, }
Endnote
%0 Thesis %A Cotop, Simina Ana %Y Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T How to be Grim : Explaining Data at Different Granularity Levels %G eng %U http://hdl.handle.net/21.11116/0000-0007-FF05-5 %I Universit&#228;t des Saarlandes %C Saarbr&#252;cken %D 2019 %V master %9 master
[176]
J. Cueppers, “How to Make Cake: Finding Causal Patterns for Marked Events in Sequences,” Universität des Saarlandes, Saarbrücken, 2019.
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@mastersthesis{cuepper:19:cake, TITLE = {How to Make Cake: Finding Causal Patterns for Marked Events in Sequences}, AUTHOR = {Cueppers, Joscha}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, }
Endnote
%0 Thesis %A Cueppers, Joscha %Y Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T How to Make Cake: Finding Causal Patterns for Marked Events in Sequences : %G eng %U http://hdl.handle.net/21.11116/0000-0007-FF09-1 %I Universit&#228;t des Saarlandes %C Saarbr&#252;cken %D 2019 %V master %9 master
[177]
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 Str&#246;tgen, 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
[178]
P. Ernst, E. Terolli, and G. Weikum, “LongLife: a Platform for Personalized Search for 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 Satellites), Auckland, New Zealand, 2019.
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@inproceedings{Ernst_ISWC2019, TITLE = {{LongLife}: a Platform for Personalized Search for 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 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 Search for 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 %E Su&#225;rez-Figueroa, Mari Carmen; Cheng, Gong; Gentile, Anna Lisa; Gu&#233;ret, Christophe; Keet, Maria; Bernstein, Abraham %P 237 - 240 %Z sequence number: 62 %I ceur-ws.org %B CEUR Workshop Proceedings %N 2456 %@ false
[179]
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
[180]
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&#160; : %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
[181]
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
[182]
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,
[183]
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
[184]
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 Guimar&#227;es, 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
[185]
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
[186]
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 Portoro&#382;, 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; Stadtm&#252;ller, Steffen; Hose, Katja; Verborgh, Ruben %P 90 - 94 %I Springer %@ 978-3-030-32326-4 %B Lecture Notes in Computer Science %N 11762
[187]
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 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 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 Portoro&#382;, Slovenia %B The Semantic Web %E Hitzler, Pascal; Fern&#225;ndez, 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
[188]
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
[189]
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
[190]
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 Universit&#228;t des Saarlandes %C Saarbr&#252;cken %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
[191]
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
[192]
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
[193]
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}, 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; Sv&#225;tek, Vojt&#277;ch; Cruz, Isabel; Hogan, Aidan; Song, Jie; Lefran&#231;ois, Maxime; Gandon, Fabien %P 237 - 257 %I Springer %@ 978-3-030-30792-9 %B Lecture Notes in Computer Science %N 11778 %@ false
[194]
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 Universit&#228;t des Saarlandes %C Saarbr&#252;cken %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
[195]
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
[196]
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
[197]
Y. Ismaeil, O. Balalau, and P. Mirza, “Discovering the Functions of Language in Online Forums,” in Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2020), Hong Kong, China, 2019.
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@inproceedings{ismaeil-etal-2019-discovering, TITLE = {Discovering the Functions of Language in Online Forums}, AUTHOR = {Ismaeil, Youmna and Balalau, Oana and Mirza, Paramita}, LANGUAGE = {eng}, ISBN = {978-1-950737-84-0}, DOI = {10.18653/v1/D19-5534}, PUBLISHER = {ACL}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2020)}, EDITOR = {Xu, Wei and Ritter, Alan and Baldwin, Tim and Rahimi, Afshin}, PAGES = {259--264}, ADDRESS = {Hong Kong, China}, }
Endnote
%0 Conference Proceedings %A Ismaeil, Youmna %A Balalau, Oana %A Mirza, Paramita %+ 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 the Functions of Language in Online Forums : %G eng %U http://hdl.handle.net/21.11116/0000-0008-0405-E %R 10.18653/v1/D19-5534 %F OTHER: D19-5534 %D 2019 %B 5th Workshop on Noisy User-generated Text %Z date of event: 2019-11-04 - 2019-11-04 %C Hong Kong, China %B Proceedings of the 5th Workshop on Noisy User-generated Text %E Xu, Wei; Ritter, Alan; Baldwin, Tim; Rahimi, Afshin %P 259 - 264 %I ACL %@ 978-1-950737-84-0 %U https://www.aclweb.org/anthology/D19-5534
[198]
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 Str&#246;tgen, 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
[199]
M. Kaiser, R. Saha Roy, and G. Weikum, “CROWN: Conversational Passage Ranking by Reasoning over Word Networks,” in Proceedings of the Twenty-Eighth Text REtrieval Conference (TREC 2019), Gaithersburg, MD, USA, 2019.
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@inproceedings{KaiserTrec19, TITLE = {{CROWN}: {C}onversational Passage Ranking by Reasoning over Word Networks}, AUTHOR = {Kaiser, Magdalena and Saha Roy, Rishiraj and Weikum, Gerhard}, LANGUAGE = {eng}, PUBLISHER = {NIST}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of the Twenty-Eighth Text REtrieval Conference (TREC 2019)}, EDITOR = {Voorhees, Ellen M. and Ellis, Angela}, SERIES = {NIST Special Publication}, VOLUME = {1250}, ADDRESS = {Gaithersburg, MD, USA}, }
Endnote
%0 Conference Proceedings %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/21.11116/0000-0008-03C3-8 %D 2019 %B Twenty-Eighth Text REtrieval Conference %Z date of event: 2019-11-13 - 2019-11-15 %C Gaithersburg, MD, USA %B Proceedings of the Twenty-Eighth Text REtrieval Conference %E Voorhees, Ellen M.; Ellis, Angela %I NIST %B NIST Special Publication %N 1250
[200]
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
[201]
J. Kalofolias, M. Boley, and J. Vreeken, “Discovering Robustly Connected Subgraphs with Simple Descriptions,” in 19th IEEE International Conference on Data Mining (ICDM 2019), Beijing, China, 2019.
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@inproceedings{kalofolias:19:rosi, TITLE = {Discovering Robustly Connected Subgraphs with Simple Descriptions}, AUTHOR = {Kalofolias, Janis and Boley, Mario and Vreeken, Jilles}, LANGUAGE = {eng}, ISBN = {978-1-7281-4604-1}, DOI = {10.1109/ICDM.2019.00139}, PUBLISHER = {IEEE}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, BOOKTITLE = {19th IEEE International Conference on Data Mining (ICDM 2019)}, PAGES = {1150--1155}, ADDRESS = {Beijing, China}, }
Endnote
%0 Conference Proceedings %A Kalofolias, Janis %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 Discovering Robustly Connected Subgraphs with Simple Descriptions : %G eng %U http://hdl.handle.net/21.11116/0000-0008-26D3-F %R 10.1109/ICDM.2019.00139 %D 2019 %B 19th IEEE International Conference on Data Mining %Z date of event: 2019-11-08 - 2019-11-11 %C Beijing, China %B 19th IEEE International Conference on Data Mining %P 1150 - 1155 %I IEEE %@ 978-1-7281-4604-1
[202]
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
[203]
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
[204]
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
[205]
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 Universit&#228;t des Saarlandes %C Saarbr&#252;cken %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 (&#8220;negative numbers&#8221;) 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 &#8211; 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
[206]
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
[207]
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
[208]
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
[209]
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
[210]
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, &#201;ric %P 105 - 114 %I ACM %@ 978-1-4503-6172-9
[211]
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,” 2019. [Online]. Available: http://arxiv.org/abs/1908.00469. (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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@online{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.}, }
Endnote
%0 Report %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 %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
[212]
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}, EDITOR = {Piwowarski, Benjamin and Chevalier, Max and Gaussier, {\'E}ric}, 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 %E Piwowarski, Benjamin; Chevalier, Max; Gaussier, &#201;ric %P 993 - 996 %I ACM %@ 9781450361729
[213]
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
[214]
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
[215]
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}, EDITOR = {Piwowarski, Benjamin and Chevalier, Max and Gaussier, {\'E}ric}, 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 %E Piwowarski, Benjamin; Chevalier, Max; Gaussier, &#201;ric %P 1101 - 1104 %I ACM %@ 9781450361729
[216]
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
[217]
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
[218]
P. Mandros, M. Boley, and J. Vreeken, “Discovering Reliable Correlations in Categorical Data,” in 19th IEEE International Conference on Data Mining (ICDM 2019), Beijing, China, 2019.
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@inproceedings{Mandros_ICDM2019, TITLE = {Discovering Reliable Correlations in Categorical Data}, AUTHOR = {Mandros, Panagiotis and Boley, Mario and Vreeken, Jilles}, LANGUAGE = {eng}, ISBN = {978-1-7281-4604-1}, DOI = {10.1109/ICDM.2019.00156}, PUBLISHER = {IEEE}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, BOOKTITLE = {19th IEEE International Conference on Data Mining (ICDM 2019)}, PAGES = {1252--1257}, ADDRESS = {Beijing, China}, }
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 Correlations in Categorical Data : %G eng %U http://hdl.handle.net/21.11116/0000-0006-F27B-F %R 10.1109/ICDM.2019.00156 %D 2019 %B 19th IEEE International Conference on Data Mining %Z date of event: 2019-11-08 - 2019-11-11 %C Beijing, China %B 19th IEEE International Conference on Data Mining %P 1252 - 1257 %I IEEE %@ 978-1-7281-4604-1
[219]
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
[220]
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
[221]
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
[222]
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
[223]
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
[224]
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
[225]
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, M