2020
[1]
H. Arnaout, S. Razniewski, and G. Weikum, “Negative Statements Considered Useful,” 2020. [Online]. Available: http://arxiv.org/abs/2001.04425. (arXiv: 2001.04425)
Abstract
Knowledge bases (KBs), pragmatic collections of knowledge about notable entities, are an important asset in applications such as search, question answering and dialogue. Rooted in a long tradition in knowledge representation, all popular KBs only store positive information, while they abstain from taking any stance towards statements not contained in them. In this paper, we make the case for explicitly stating interesting statements which are not true. Negative statements would be important to overcome current limitations of question answering, yet due to their potential abundance, any effort towards compiling them needs a tight coupling with ranking. We introduce two approaches towards compiling negative statements. (i) In peer-based statistical inferences, we compare entities with highly related entities in order to derive potential negative statements, which we then rank using supervised and unsupervised features. (ii) In query-log-based text extraction, we use a pattern-based approach for harvesting search engine query logs. Experimental results show that both approaches hold promising and complementary potential. Along with this paper, we publish the first datasets on interesting negative information, containing over 1.1M statements for 100K popular Wikidata entities.
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BibTeX
@online{Arnaout_arXiv2001.04425, TITLE = {Negative Statements Considered Useful}, AUTHOR = {Arnaout, Hiba and Razniewski, Simon and Weikum, Gerhard}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/2001.04425}, EPRINT = {2001.04425}, EPRINTTYPE = {arXiv}, YEAR = {2020}, ABSTRACT = {Knowledge bases (KBs), pragmatic collections of knowledge about notable entities, are an important asset in applications such as search, question answering and dialogue. Rooted in a long tradition in knowledge representation, all popular KBs only store positive information, while they abstain from taking any stance towards statements not contained in them. In this paper, we make the case for explicitly stating interesting statements which are not true. Negative statements would be important to overcome current limitations of question answering, yet due to their potential abundance, any effort towards compiling them needs a tight coupling with ranking. We introduce two approaches towards compiling negative statements. (i) In peer-based statistical inferences, we compare entities with highly related entities in order to derive potential negative statements, which we then rank using supervised and unsupervised features. (ii) In query-log-based text extraction, we use a pattern-based approach for harvesting search engine query logs. Experimental results show that both approaches hold promising and complementary potential. Along with this paper, we publish the first datasets on interesting negative information, containing over 1.1M statements for 100K popular Wikidata entities.}, }
Endnote
%0 Report %A Arnaout, Hiba %A Razniewski, Simon %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Negative Statements Considered Useful : %G eng %U http://hdl.handle.net/21.11116/0000-0005-821F-6 %U http://arxiv.org/abs/2001.04425 %D 2020 %X Knowledge bases (KBs), pragmatic collections of knowledge about notable entities, are an important asset in applications such as search, question answering and dialogue. Rooted in a long tradition in knowledge representation, all popular KBs only store positive information, while they abstain from taking any stance towards statements not contained in them. In this paper, we make the case for explicitly stating interesting statements which are not true. Negative statements would be important to overcome current limitations of question answering, yet due to their potential abundance, any effort towards compiling them needs a tight coupling with ranking. We introduce two approaches towards compiling negative statements. (i) In peer-based statistical inferences, we compare entities with highly related entities in order to derive potential negative statements, which we then rank using supervised and unsupervised features. (ii) In query-log-based text extraction, we use a pattern-based approach for harvesting search engine query logs. Experimental results show that both approaches hold promising and complementary potential. Along with this paper, we publish the first datasets on interesting negative information, containing over 1.1M statements for 100K popular Wikidata entities. %K Computer Science, Information Retrieval, cs.IR,Computer Science, Artificial Intelligence, cs.AI,Computer Science, Computation and Language, cs.CL,Computer Science, Databases, cs.DB
[2]
Y. Chalier, S. Razniewski, and G. Weikum, “Joint Reasoning for Multi-Faceted Commonsense Knowledge,” 2020. [Online]. Available: http://arxiv.org/abs/2001.04170. (arXiv: 2001.04170)
Abstract
Commonsense knowledge (CSK) supports a variety of AI applications, from visual understanding to chatbots. Prior works on acquiring CSK, such as ConceptNet, have compiled statements that associate concepts, like everyday objects or activities, with properties that hold for most or some instances of the concept. Each concept is treated in isolation from other concepts, and the only quantitative measure (or ranking) of properties is a confidence score that the statement is valid. This paper aims to overcome these limitations by introducing a multi-faceted model of CSK statements and methods for joint reasoning over sets of inter-related statements. Our model captures four different dimensions of CSK statements: plausibility, typicality, remarkability and salience, with scoring and ranking along each dimension. For example, hyenas drinking water is typical but not salient, whereas hyenas eating carcasses is salient. For reasoning and ranking, we develop a method with soft constraints, to couple the inference over concepts that are related in in a taxonomic hierarchy. The reasoning is cast into an integer linear programming (ILP), and we leverage the theory of reduction costs of a relaxed LP to compute informative rankings. This methodology is applied to several large CSK collections. Our evaluation shows that we can consolidate these inputs into much cleaner and more expressive knowledge. Results are available at https://dice.mpi-inf.mpg.de.
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BibTeX
@online{Chalier_arXiv2001.04170, TITLE = {Joint Reasoning for Multi-Faceted Commonsense Knowledge}, AUTHOR = {Chalier, Yohan and Razniewski, Simon and Weikum, Gerhard}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/2001.04170}, EPRINT = {2001.04170}, EPRINTTYPE = {arXiv}, YEAR = {2020}, ABSTRACT = {Commonsense knowledge (CSK) supports a variety of AI applications, from visual understanding to chatbots. Prior works on acquiring CSK, such as ConceptNet, have compiled statements that associate concepts, like everyday objects or activities, with properties that hold for most or some instances of the concept. Each concept is treated in isolation from other concepts, and the only quantitative measure (or ranking) of properties is a confidence score that the statement is valid. This paper aims to overcome these limitations by introducing a multi-faceted model of CSK statements and methods for joint reasoning over sets of inter-related statements. Our model captures four different dimensions of CSK statements: plausibility, typicality, remarkability and salience, with scoring and ranking along each dimension. For example, hyenas drinking water is typical but not salient, whereas hyenas eating carcasses is salient. For reasoning and ranking, we develop a method with soft constraints, to couple the inference over concepts that are related in in a taxonomic hierarchy. The reasoning is cast into an integer linear programming (ILP), and we leverage the theory of reduction costs of a relaxed LP to compute informative rankings. This methodology is applied to several large CSK collections. Our evaluation shows that we can consolidate these inputs into much cleaner and more expressive knowledge. Results are available at https://dice.mpi-inf.mpg.de.}, }
Endnote
%0 Report %A Chalier, Yohan %A Razniewski, Simon %A Weikum, Gerhard %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Joint Reasoning for Multi-Faceted Commonsense Knowledge : %G eng %U http://hdl.handle.net/21.11116/0000-0005-8226-D %U http://arxiv.org/abs/2001.04170 %D 2020 %X Commonsense knowledge (CSK) supports a variety of AI applications, from visual understanding to chatbots. Prior works on acquiring CSK, such as ConceptNet, have compiled statements that associate concepts, like everyday objects or activities, with properties that hold for most or some instances of the concept. Each concept is treated in isolation from other concepts, and the only quantitative measure (or ranking) of properties is a confidence score that the statement is valid. This paper aims to overcome these limitations by introducing a multi-faceted model of CSK statements and methods for joint reasoning over sets of inter-related statements. Our model captures four different dimensions of CSK statements: plausibility, typicality, remarkability and salience, with scoring and ranking along each dimension. For example, hyenas drinking water is typical but not salient, whereas hyenas eating carcasses is salient. For reasoning and ranking, we develop a method with soft constraints, to couple the inference over concepts that are related in in a taxonomic hierarchy. The reasoning is cast into an integer linear programming (ILP), and we leverage the theory of reduction costs of a relaxed LP to compute informative rankings. This methodology is applied to several large CSK collections. Our evaluation shows that we can consolidate these inputs into much cleaner and more expressive knowledge. Results are available at https://dice.mpi-inf.mpg.de. %K Computer Science, Computation and Language, cs.CL,Computer Science, Artificial Intelligence, cs.AI,Computer Science, Information Retrieval, cs.IR
[3]
C. X. Chu, S. Razniewski, and G. Weikum, “ENTYFI: Entity Typing in Fictional Texts,” in WSDM’19, 13th International Conference on Web Search and Data Mining, Houston, TX, USA, 2020.
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@inproceedings{ChuWSDM2020, TITLE = {{ENTYFI}: {E}ntity Typing in Fictional Texts}, AUTHOR = {Chu, Cuong Xuan and Razniewski, Simon and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {9781450368223}, DOI = {10.1145/3336191.3371808}, PUBLISHER = {ACM}, YEAR = {2020}, BOOKTITLE = {WSDM'19, 13th International Conference on Web Search and Data Mining}, EDITOR = {Caverlee, James and Hu, Xia Ben}, PAGES = {124--132}, ADDRESS = {Houston, TX, USA}, }
Endnote
%0 Conference Proceedings %A Chu, Cuong Xuan %A Razniewski, Simon %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T ENTYFI: Entity Typing in Fictional Texts : %G eng %U http://hdl.handle.net/21.11116/0000-0006-A27E-6 %R 10.1145/3336191.3371808 %D 2020 %B 13th International Conference on Web Search and Data Mining %Z date of event: 2020-02-03 - 2020-02-07 %C Houston, TX, USA %B WSDM'19 %E Caverlee, James; Hu, Xia Ben %P 124 - 132 %I ACM %@ 9781450368223
[4]
F. Darari, W. Nutt, S. Razniewski, and S. Rudolph, “Completeness and soundness guarantees for conjunctive SPARQL queries over RDF data sources with completeness statements,” Semantic Web, vol. 11, no. 1, 2020.
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@article{Darari2020, TITLE = {Completeness and soundness guarantees for conjunctive {SPARQL} queries over {RDF} data sources with completeness statements}, AUTHOR = {Darari, Fariza and Nutt, Werner and Razniewski, Simon and Rudolph, Sebastian}, LANGUAGE = {eng}, ISSN = {1570-0844}, DOI = {10.3233/SW-190344}, PUBLISHER = {IOS Press}, ADDRESS = {Amsterdam}, YEAR = {2020}, DATE = {2020}, JOURNAL = {Semantic Web}, VOLUME = {11}, NUMBER = {1}, PAGES = {441--482}, }
Endnote
%0 Journal Article %A Darari, Fariza %A Nutt, Werner %A Razniewski, Simon %A Rudolph, Sebastian %+ External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Completeness and soundness guarantees for conjunctive SPARQL queries over RDF data sources with completeness statements : %G eng %U http://hdl.handle.net/21.11116/0000-0006-9A06-6 %R 10.3233/SW-190344 %7 2020 %D 2020 %J Semantic Web %V 11 %N 1 %& 441 %P 441 - 482 %I IOS Press %C Amsterdam %@ false
[5]
V. T. Ho, K. Pal, N. Kleer, K. Berberich, and G. Weikum, “Entities with Quantities: Extraction, Search, and Ranking,” in WSDM’19, 13th International Conference on Web Search and Data Mining, Houston, TX, USA, 2020.
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@inproceedings{HoWSDM2020, TITLE = {Entities with Quantities: {E}xtraction, Search, and Ranking}, AUTHOR = {Ho, Vinh Thinh and Pal, Koninika and Kleer, Niko and Berberich, Klaus and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {9781450368223}, DOI = {10.1145/3336191.3371860}, PUBLISHER = {ACM}, YEAR = {2020}, BOOKTITLE = {WSDM'19, 13th International Conference on Web Search and Data Mining}, EDITOR = {Caverlee, James and Hu, Xia Ben}, PAGES = {833--836}, ADDRESS = {Houston, TX, USA}, }
Endnote
%0 Conference Proceedings %A Ho, Vinh Thinh %A Pal, Koninika %A Kleer, Niko %A Berberich, Klaus %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Entities with Quantities: Extraction, Search, and Ranking : %G eng %U http://hdl.handle.net/21.11116/0000-0006-A284-D %R 10.1145/3336191.3371860 %D 2020 %B 13th International Conference on Web Search and Data Mining %Z date of event: 2020-02-03 - 2020-02-07 %C Houston, TX, USA %B WSDM'19 %E Caverlee, James; Hu, Xia Ben %P 833 - 836 %I ACM %@ 9781450368223
[6]
S. Nag Chowdhury, W. Cheng, G. de Melo, S. Razniewski, and G. Weikum, “Illustrate Your Story: Enriching Text with Images,” in WSDM’19, 13th International Conference on Web Search and Data Mining, Houston, TX, USA, 2020.
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@inproceedings{NagWSDM2020, TITLE = {Illustrate Your Story: {Enriching} Text with Images}, AUTHOR = {Nag Chowdhury, Sreyasi and Cheng, William and de Melo, Gerard and Razniewski, Simon and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {9781450368223}, DOI = {10.1145/3336191.3371866}, PUBLISHER = {ACM}, YEAR = {2020}, BOOKTITLE = {WSDM'19, 13th International Conference on Web Search and Data Mining}, EDITOR = {Caverlee, James and Hu, Xia Ben}, PAGES = {849--852}, ADDRESS = {Houston, TX, USA}, }
Endnote
%0 Conference Proceedings %A Nag Chowdhury, Sreyasi %A Cheng, William %A de Melo, Gerard %A Razniewski, Simon %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Illustrate Your Story: Enriching Text with Images : %G eng %U http://hdl.handle.net/21.11116/0000-0006-A27C-8 %R 10.1145/3336191.3371866 %D 2020 %B 13th International Conference on Web Search and Data Mining %Z date of event: 2020-02-03 - 2020-02-07 %C Houston, TX, USA %B WSDM'19 %E Caverlee, James; Hu, Xia Ben %P 849 - 852 %I ACM %@ 9781450368223
[7]
A. Yates, S. Arora, X. Zhang, W. Yang, K. M. Jose, and J. Lin, “Capreolus: A Toolkit for End-to-End Neural Ad Hoc Retrieval,” in WSDM’19, 13th International Conference on Web Search and Data Mining, Houston, TX, USA, 2020.
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@inproceedings{YatesWSDM2020, TITLE = {Capreolus: {A} Toolkit for End-to-End Neural Ad Hoc Retrieval}, AUTHOR = {Yates, Andrew and Arora, Siddhant and Zhang, Xinyu and Yang, Wei and Jose, Kevin Martin and Lin, Jimmy}, LANGUAGE = {eng}, ISBN = {9781450368223}, DOI = {10.1145/3336191.3371868}, PUBLISHER = {ACM}, YEAR = {2020}, BOOKTITLE = {WSDM'19, 13th International Conference on Web Search and Data Mining}, EDITOR = {Caverlee, James and Hu, Xia Ben}, PAGES = {861--864}, ADDRESS = {Houston, TX, USA}, }
Endnote
%0 Conference Proceedings %A Yates, Andrew %A Arora, Siddhant %A Zhang, Xinyu %A Yang, Wei %A Jose, Kevin Martin %A Lin, Jimmy %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Capreolus: A Toolkit for End-to-End Neural Ad Hoc Retrieval : %G eng %U http://hdl.handle.net/21.11116/0000-0006-A28E-3 %R 10.1145/3336191.3371868 %D 2020 %B 13th International Conference on Web Search and Data Mining %Z date of event: 2020-02-03 - 2020-02-07 %C Houston, TX, USA %B WSDM'19 %E Caverlee, James; Hu, Xia Ben %P 861 - 864 %I ACM %@ 9781450368223