2018
[1]
A. Abujabal, R. S. Roy, M. Yahya, and G. Weikum, “Never-Ending Learning for Open-Domain Question Answering over Knowledge Bases,” in Proceedings of the World Wide Web Conference (WWW 2018), Lyon, France, 2018.
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@inproceedings{AbujabalWWW_2018, TITLE = {Never-Ending Learning for Open-Domain Question Answering over Knowledge Bases}, AUTHOR = {Abujabal, Abdalghani and Roy, Rishiraj Saha and Yahya, Mohamed and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-5639-8}, DOI = {10.1145/3178876.3186004}, PUBLISHER = {ACM}, YEAR = {2018}, DATE = {2018}, BOOKTITLE = {Proceedings of the World Wide Web Conference (WWW 2018)}, EDITOR = {Champin, Pierre-Antoine and Gandon, Fabien and M{\'e}dini, Lionel and Lalmas, Mounia and Ipeirotis, Panagiotis G.}, PAGES = {1053--1062}, ADDRESS = {Lyon, France}, }
Endnote
%0 Conference Proceedings %A Abujabal, Abdalghani %A Roy, Rishiraj Saha %A Yahya, Mohamed %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Never-Ending Learning for Open-Domain Question Answering over Knowledge Bases : %G eng %U http://hdl.handle.net/21.11116/0000-0001-3C91-8 %R 10.1145/3178876.3186004 %D 2018 %B The Web Conference %Z date of event: 2018-04-23 - 2018-04-27 %C Lyon, France %B Proceedings of the World Wide Web Conference %E Champin, Pierre-Antoine; Gandon, Fabien; Médini, Lionel; Lalmas, Mounia; Ipeirotis, Panagiotis G. %P 1053 - 1062 %I ACM %@ 978-1-4503-5639-8
[2]
P. Agarwal, J. Strötgen, L. Del Corro, J. Hoffart, and G. Weikum, “diaNED: Time-Aware Named Entity Disambiguation for Diachronic Corpora,” in The 56th Annual Meeting of the Association for Computational Linguistics (ACL 2018), Melbourne, Australia, 2018.
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@inproceedings{AgrawalACL2018, TITLE = {{diaNED}: {T}ime-Aware Named Entity Disambiguation for Diachronic Corpora}, AUTHOR = {Agarwal, Prabal and Str{\"o}tgen, Jannik and Del Corro, Luciano and Hoffart, Johannes and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-948087-34-6}, PUBLISHER = {ACL}, YEAR = {2018}, BOOKTITLE = {The 56th Annual Meeting of the Association for Computational Linguistics (ACL 2018)}, PAGES = {686--693}, EID = {602}, ADDRESS = {Melbourne, Australia}, }
Endnote
%0 Conference Proceedings %A Agarwal, Prabal %A Strötgen, Jannik %A Del Corro, Luciano %A Hoffart, Johannes %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T diaNED: Time-Aware Named Entity Disambiguation for Diachronic Corpora : %G eng %U http://hdl.handle.net/21.11116/0000-0001-9055-C %D 2018 %B The 56th Annual Meeting of the Association for Computational Linguistics %Z date of event: 2018-07-15 - 2018-07-20 %C Melbourne, Australia %B The 56th Annual Meeting of the Association for Computational Linguistics %P 686 - 693 %Z sequence number: 602 %I ACL %@ 978-1-948087-34-6 %U http://aclweb.org/anthology/P18-2109
[3]
V. Balaraman, S. Razniewski, and W. Nutt, “Recoin: Relative Completeness in Wikidata,” in Companion of the World Wide Web Conference (WWW 2018), Lyon, France, 2018.
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@inproceedings{BalaramanWWW2017, TITLE = {Recoin: {R}elative Completeness in {W}ikidata}, AUTHOR = {Balaraman, Vevake and Razniewski, Simon and Nutt, Werner}, LANGUAGE = {eng}, ISBN = {978-1-4503-5640-4}, DOI = {10.1145/3184558.3191641}, PUBLISHER = {ACM}, YEAR = {2018}, DATE = {2018}, BOOKTITLE = {Companion of the World Wide Web Conference (WWW 2018)}, EDITOR = {Champin, Pierre-Antoine and Gandon, Fabien and M{\'e}dini, Lionel}, PAGES = {1787--1792}, ADDRESS = {Lyon, France}, }
Endnote
%0 Conference Proceedings %A Balaraman, Vevake %A Razniewski, Simon %A Nutt, Werner %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Recoin: Relative Completeness in Wikidata : %G eng %U http://hdl.handle.net/21.11116/0000-0001-414A-3 %R 10.1145/3184558.3191641 %D 2018 %B The Web Conference %Z date of event: 2018-04-23 - 2018-04-27 %C Lyon, France %B Companion of the World Wide Web Conference %E Champin, Pierre-Antoine; Gandon, Fabien; Médini, Lionel %P 1787 - 1792 %I ACM %@ 978-1-4503-5640-4
[4]
A. J. Biega, K. P. Gummadi, and G. Weikum, “Equity of Attention: Amortizing Individual Fairness in Rankings,” in SIGIR’18, 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, Ann Arbor, MI, USA, 2018.
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@inproceedings{BiegaSIGIR2018, TITLE = {Equity of Attention: {A}mortizing Individual Fairness in Rankings}, AUTHOR = {Biega, Asia J. and Gummadi, Krishna P. and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-5022-8}, DOI = {10.1145/3209978.3210063}, PUBLISHER = {ACM}, YEAR = {2018}, DATE = {2018}, BOOKTITLE = {SIGIR'18, 41st International ACM SIGIR Conference on Research and Development in Information Retrieval}, PAGES = {405--414}, ADDRESS = {Ann Arbor, MI, USA}, }
Endnote
%0 Conference Proceedings %A Biega, Asia J. %A Gummadi, Krishna P. %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Equity of Attention: Amortizing Individual Fairness in Rankings : %G eng %U http://hdl.handle.net/21.11116/0000-0002-0D8A-5 %R 10.1145/3209978.3210063 %D 2018 %B 41st International ACM SIGIR Conference on Research and Development in Information Retrieval %Z date of event: 2018-07-08 - 2018-07-12 %C Ann Arbor, MI, USA %B SIGIR'18 %P 405 - 414 %I ACM %@ 978-1-4503-5022-8
[5]
A. J. Biega, K. P. Gummadi, and G. Weikum, “Equity of Attention: Amortizing Individual Fairness in Rankings,” 2018. [Online]. Available: http://arxiv.org/abs/1805.01788. (arXiv: 1805.01788)
Abstract
Rankings of people and items are at the heart of selection-making, match-making, and recommender systems, ranging from employment sites to sharing economy platforms. As ranking positions influence the amount of attention the ranked subjects receive, biases in rankings can lead to unfair distribution of opportunities and resources, such as jobs or income. This paper proposes new measures and mechanisms to quantify and mitigate unfairness from a bias inherent to all rankings, namely, the position bias, which leads to disproportionately less attention being paid to low-ranked subjects. Our approach differs from recent fair ranking approaches in two important ways. First, existing works measure unfairness at the level of subject groups while our measures capture unfairness at the level of individual subjects, and as such subsume group unfairness. Second, as no single ranking can achieve individual attention fairness, we propose a novel mechanism that achieves amortized fairness, where attention accumulated across a series of rankings is proportional to accumulated relevance. We formulate the challenge of achieving amortized individual fairness subject to constraints on ranking quality as an online optimization problem and show that it can be solved as an integer linear program. Our experimental evaluation reveals that unfair attention distribution in rankings can be substantial, and demonstrates that our method can improve individual fairness while retaining high ranking quality.
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@online{Biega_arXiv1805.01788, TITLE = {Equity of Attention: Amortizing Individual Fairness in Rankings}, AUTHOR = {Biega, Asia J. and Gummadi, Krishna P. and Weikum, Gerhard}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1805.01788}, EPRINT = {1805.01788}, EPRINTTYPE = {arXiv}, YEAR = {2018}, ABSTRACT = {Rankings of people and items are at the heart of selection-making, match-making, and recommender systems, ranging from employment sites to sharing economy platforms. As ranking positions influence the amount of attention the ranked subjects receive, biases in rankings can lead to unfair distribution of opportunities and resources, such as jobs or income. This paper proposes new measures and mechanisms to quantify and mitigate unfairness from a bias inherent to all rankings, namely, the position bias, which leads to disproportionately less attention being paid to low-ranked subjects. Our approach differs from recent fair ranking approaches in two important ways. First, existing works measure unfairness at the level of subject groups while our measures capture unfairness at the level of individual subjects, and as such subsume group unfairness. Second, as no single ranking can achieve individual attention fairness, we propose a novel mechanism that achieves amortized fairness, where attention accumulated across a series of rankings is proportional to accumulated relevance. We formulate the challenge of achieving amortized individual fairness subject to constraints on ranking quality as an online optimization problem and show that it can be solved as an integer linear program. Our experimental evaluation reveals that unfair attention distribution in rankings can be substantial, and demonstrates that our method can improve individual fairness while retaining high ranking quality.}, }
Endnote
%0 Report %A Biega, Asia J. %A Gummadi, Krishna P. %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Equity of Attention: Amortizing Individual Fairness in Rankings : %G eng %U http://hdl.handle.net/21.11116/0000-0002-1563-7 %U http://arxiv.org/abs/1805.01788 %D 2018 %X Rankings of people and items are at the heart of selection-making, match-making, and recommender systems, ranging from employment sites to sharing economy platforms. As ranking positions influence the amount of attention the ranked subjects receive, biases in rankings can lead to unfair distribution of opportunities and resources, such as jobs or income. This paper proposes new measures and mechanisms to quantify and mitigate unfairness from a bias inherent to all rankings, namely, the position bias, which leads to disproportionately less attention being paid to low-ranked subjects. Our approach differs from recent fair ranking approaches in two important ways. First, existing works measure unfairness at the level of subject groups while our measures capture unfairness at the level of individual subjects, and as such subsume group unfairness. Second, as no single ranking can achieve individual attention fairness, we propose a novel mechanism that achieves amortized fairness, where attention accumulated across a series of rankings is proportional to accumulated relevance. We formulate the challenge of achieving amortized individual fairness subject to constraints on ranking quality as an online optimization problem and show that it can be solved as an integer linear program. Our experimental evaluation reveals that unfair attention distribution in rankings can be substantial, and demonstrates that our method can improve individual fairness while retaining high ranking quality. %K Computer Science, Information Retrieval, cs.IR,Computer Science, Computers and Society, cs.CY
[6]
N. Boldyrev, M. Spaniol, and G. Weikum, “Multi-Cultural Interlinking of Web Taxonomies with ACROSS,” The Journal of Web Science, vol. 4, no. 2, 2018.
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@article{Boldyrev2018, TITLE = {Multi-Cultural Interlinking of Web Taxonomies with {ACROSS}}, AUTHOR = {Boldyrev, Natalia and Spaniol, Marc and Weikum, Gerhard}, LANGUAGE = {eng}, DOI = {10.1561/106.00000012}, PUBLISHER = {Now Publishers}, ADDRESS = {Boston}, YEAR = {2018}, JOURNAL = {The Journal of Web Science}, VOLUME = {4}, NUMBER = {2}, PAGES = {20--33}, }
Endnote
%0 Journal Article %A Boldyrev, Natalia %A Spaniol, Marc %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Multi-Cultural Interlinking of Web Taxonomies with ACROSS : %G eng %U http://hdl.handle.net/21.11116/0000-0001-3CA4-3 %R 10.1561/106.00000012 %7 2018 %D 2018 %J The Journal of Web Science %O Web Science %V 4 %N 2 %& 20 %P 20 - 33 %I Now Publishers %C Boston
[7]
K. Budhathoki and J. Vreeken, “Origo: Causal Inference by Compression,” Knowledge and Information Systems, vol. 56, no. 2, 2018.
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@article{Budhathoki2018, TITLE = {Origo: {C}ausal Inference by Compression}, AUTHOR = {Budhathoki, Kailash and Vreeken, Jilles}, LANGUAGE = {eng}, ISSN = {0219-1377}, DOI = {10.1007/s10115-017-1130-5}, PUBLISHER = {Springer}, ADDRESS = {New York, NY}, YEAR = {2018}, DATE = {2018}, JOURNAL = {Knowledge and Information Systems}, VOLUME = {56}, NUMBER = {2}, PAGES = {285--307}, }
Endnote
%0 Journal Article %A Budhathoki, Kailash %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Origo: Causal Inference by Compression : %G eng %U http://hdl.handle.net/21.11116/0000-0001-AF2B-B %R 10.1007/s10115-017-1130-5 %7 2018 %D 2018 %J Knowledge and Information Systems %V 56 %N 2 %& 285 %P 285 - 307 %I Springer %C New York, NY %@ false
[8]
F. Darari, W. Nutt, and S. Razniewski, “Comparing Index Structures for Completeness Reasoning,” in International Workshop on Big Data and Information Security (IWBIS 2018), Jakarta, Indonesia, 2018.
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@inproceedings{DarariIWBIS2018, TITLE = {Comparing Index Structures for Completeness Reasoning}, AUTHOR = {Darari, Fariz and Nutt, Werner and Razniewski, Simon}, LANGUAGE = {eng}, YEAR = {2018}, BOOKTITLE = {International Workshop on Big Data and Information Security (IWBIS 2018)}, ADDRESS = {Jakarta, Indonesia}, }
Endnote
%0 Conference Proceedings %A Darari, Fariz %A Nutt, Werner %A Razniewski, Simon %+ External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Comparing Index Structures for Completeness Reasoning : %G eng %U http://hdl.handle.net/21.11116/0000-0001-E193-A %D 2018 %B International Workshop on Big Data and Information Security %Z date of event: 2018-05-12 - 2018-05-13 %C Jakarta, Indonesia %B International Workshop on Big Data and Information Security
[9]
F. Darari, W. Nutt, G. Pirrò, and S. Razniewski, “Completeness Management for RDF Data Sources,” ACM Transactions on the Web, vol. 12, no. 3, 2018.
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@article{Darari2018, TITLE = {Completeness Management for {RDF} Data Sources}, AUTHOR = {Darari, Fariz and Nutt, Werner and Pirr{\`o}, Giuseppe and Razniewski, Simon}, LANGUAGE = {eng}, DOI = {10.1145/3196248}, PUBLISHER = {ACM}, ADDRESS = {New York, NY}, YEAR = {2018}, DATE = {2018}, JOURNAL = {ACM Transactions on the Web}, VOLUME = {12}, NUMBER = {3}, EID = {18}, }
Endnote
%0 Journal Article %A Darari, Fariz %A Nutt, Werner %A Pirrò, Giuseppe %A Razniewski, Simon %+ External Organizations External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Completeness Management for RDF Data Sources : %G eng %U http://hdl.handle.net/21.11116/0000-0001-E17F-3 %R 10.1145/3196248 %7 2018 %D 2018 %J ACM Transactions on the Web %V 12 %N 3 %Z sequence number: 18 %I ACM %C New York, NY
[10]
S. Degaetano-Ortlieb and J. Strötgen, “Diachronic Variation of Temporal Expressions in Scientific Writing through the Lens of Relative Entropy,” in Language Technologies for the Challenges of the Digital Age (GSCL 2017), Berlin, Germany, 2018.
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@inproceedings{DegaetanoortliebStroetgen2017, TITLE = {Diachronic Variation of Temporal Expressions in Scientific Writing through the Lens of Relative Entropy}, AUTHOR = {Degaetano-Ortlieb, Stefania and Str{\"o}tgen, Jannik}, LANGUAGE = {eng}, ISBN = {978-3-319-73705-8}, DOI = {10.1007/978-3-319-73706-5_22}, PUBLISHER = {Springer}, YEAR = {2017}, DATE = {2018}, BOOKTITLE = {Language Technologies for the Challenges of the Digital Age (GSCL 2017)}, EDITOR = {Rehm, Georg and Declerck, Thierry}, PAGES = {259--275}, SERIES = {Lecture Notes in Artificial Intelligence}, VOLUME = {10713}, ADDRESS = {Berlin, Germany}, }
Endnote
%0 Conference Proceedings %A Degaetano-Ortlieb, Stefania %A Strötgen, Jannik %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Diachronic Variation of Temporal Expressions in Scientific Writing through the Lens of Relative Entropy : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-A8E8-5 %R 10.1007/978-3-319-73706-5_22 %D 2018 %B Conference of the German Society for Computational Linguistics and Language Technology %Z date of event: 2017-09-13 - 2017-09-14 %C Berlin, Germany %B Language Technologies for the Challenges of the Digital Age %E Rehm, Georg; Declerck, Thierry %P 259 - 275 %I Springer %@ 978-3-319-73705-8 %B Lecture Notes in Artificial Intelligence %N 10713
[11]
P. Ernst, “Biomedical Knowledge Base Construction from Text and its Applications in Knowledge-based Systems,” Universität des Saarlandes, Saarbrücken, 2018.
Abstract
While general-purpose Knowledge Bases (KBs) have gone a long way in compiling comprehensive knowledgee about people, events, places, etc., domain-specific KBs, such as on health, are equally important, but are less explored. Consequently, a comprehensive and expressive health KB that spans all aspects of biomedical knowledge is still missing. The main goal of this thesis is to develop principled methods for building such a KB and enabling knowledge-centric applications. We address several challenges and make the following contributions: - To construct a health KB, we devise a largely automated and scalable pattern-based knowledge extraction method covering a spectrum of different text genres and distilling a wide variety of facts from different biomedical areas. - To consider higher-arity relations, crucial for proper knowledge representation in advanced domain such as health, we generalize the fact-pattern duality paradigm of previous methods. A key novelty is the integration of facts with missing arguments by extending our framework to partial patterns and facts by reasoning over the composability of partial facts. - To demonstrate the benefits of a health KB, we devise systems for entity-aware search and analytics and for entity-relationship-oriented exploration. Extensive experiments and use-case studies demonstrate the viability of the proposed approaches.
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@phdthesis{Ernstphd2017, TITLE = {Biomedical Knowledge Base Construction from Text and its Applications in Knowledge-based Systems}, AUTHOR = {Ernst, Patrick}, LANGUAGE = {eng}, URL = {urn:nbn:de:bsz:291-scidok-ds-271051}, DOI = {10.22028/D291-27105}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2018}, ABSTRACT = {While general-purpose Knowledge Bases (KBs) have gone a long way in compiling comprehensive knowledgee about people, events, places, etc., domain-specific KBs, such as on health, are equally important, but are less explored. Consequently, a comprehensive and expressive health KB that spans all aspects of biomedical knowledge is still missing. The main goal of this thesis is to develop principled methods for building such a KB and enabling knowledge-centric applications. We address several challenges and make the following contributions: -- To construct a health KB, we devise a largely automated and scalable pattern-based knowledge extraction method covering a spectrum of different text genres and distilling a wide variety of facts from different biomedical areas. -- To consider higher-arity relations, crucial for proper knowledge representation in advanced domain such as health, we generalize the fact-pattern duality paradigm of previous methods. A key novelty is the integration of facts with missing arguments by extending our framework to partial patterns and facts by reasoning over the composability of partial facts. -- To demonstrate the benefits of a health KB, we devise systems for entity-aware search and analytics and for entity-relationship-oriented exploration. Extensive experiments and use-case studies demonstrate the viability of the proposed approaches.}, }
Endnote
%0 Thesis %A Ernst, Patrick %Y Weikum, Gerhard %A referee: Verspoor, Karin %A referee: Berberich, Klaus %+ Databases and Information Systems, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Biomedical Knowledge Base Construction from Text and its Applications in Knowledge-based Systems : %G eng %U http://hdl.handle.net/21.11116/0000-0001-1864-4 %U urn:nbn:de:bsz:291-scidok-ds-271051 %R 10.22028/D291-27105 %I Universität des Saarlandes %C Saarbrücken %D 2018 %8 20.02.2018 %P 147 p. %V phd %9 phd %X While general-purpose Knowledge Bases (KBs) have gone a long way in compiling comprehensive knowledgee about people, events, places, etc., domain-specific KBs, such as on health, are equally important, but are less explored. Consequently, a comprehensive and expressive health KB that spans all aspects of biomedical knowledge is still missing. The main goal of this thesis is to develop principled methods for building such a KB and enabling knowledge-centric applications. We address several challenges and make the following contributions: - To construct a health KB, we devise a largely automated and scalable pattern-based knowledge extraction method covering a spectrum of different text genres and distilling a wide variety of facts from different biomedical areas. - To consider higher-arity relations, crucial for proper knowledge representation in advanced domain such as health, we generalize the fact-pattern duality paradigm of previous methods. A key novelty is the integration of facts with missing arguments by extending our framework to partial patterns and facts by reasoning over the composability of partial facts. - To demonstrate the benefits of a health KB, we devise systems for entity-aware search and analytics and for entity-relationship-oriented exploration. Extensive experiments and use-case studies demonstrate the viability of the proposed approaches. %U https://publikationen.sulb.uni-saarland.de/handle/20.500.11880/26987
[12]
P. Ernst, A. Siu, and G. Weikum, “HighLife: Higher-arity Fact Harvesting,” in Proceedings of the World Wide Web Conference (WWW 2018), Lyon, France, 2018.
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@inproceedings{ErnstlWWW_2018, TITLE = {{HighLife}: Higher-arity Fact Harvesting}, AUTHOR = {Ernst, Patrick and Siu, Amy and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-5639-8}, DOI = {10.1145/3178876.3186000}, PUBLISHER = {ACM}, YEAR = {2018}, DATE = {2018}, BOOKTITLE = {Proceedings of the World Wide Web Conference (WWW 2018)}, EDITOR = {Champin, Pierre-Antoine and Gandon, Fabien and M{\'e}dini, Lionel and Lalmas, Mounia and Ipeirotis, Panagiotis G.}, PAGES = {1013--1022}, ADDRESS = {Lyon, France}, }
Endnote
%0 Conference Proceedings %A Ernst, Patrick %A Siu, Amy %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T HighLife: Higher-arity Fact Harvesting : %G eng %U http://hdl.handle.net/21.11116/0000-0001-3C96-3 %R 10.1145/3178876.3186000 %D 2018 %B The Web Conference %Z date of event: 2018-04-23 - 2018-04-27 %C Lyon, France %B Proceedings of the World Wide Web Conference %E Champin, Pierre-Antoine; Gandon, Fabien; Médini, Lionel; Lalmas, Mounia; Ipeirotis, Panagiotis G. %P 1013 - 1022 %I ACM %@ 978-1-4503-5639-8
[13]
A. K. Fischer, J. Vreeken, and D. Klakov, “Beyond Pairwise Similarity: Quantifying and Characterizing Linguistic Similarity between Groups of Languages by MDL,” Computación y Sistemas, vol. 21, no. 4, 2018.
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@article{Fischer2018, TITLE = {Beyond Pairwise Similarity: Quantifying and Characterizing Linguistic Similarity between Groups of Languages by {MDL}}, AUTHOR = {Fischer, Andrea K. and Vreeken, Jilles and Klakov, Dietrich}, LANGUAGE = {eng}, URL = {http://www.cys.cic.ipn.mx/ojs/index.php/CyS/article/view/2865}, YEAR = {2018}, JOURNAL = {Computaci{\'o}n y Sistemas}, VOLUME = {21}, NUMBER = {4}, }
Endnote
%0 Journal Article %A Fischer, Andrea K. %A Vreeken, Jilles %A Klakov, Dietrich %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Beyond Pairwise Similarity: Quantifying and Characterizing Linguistic Similarity between Groups of Languages by MDL : %G eng %U http://hdl.handle.net/21.11116/0000-0001-4156-5 %U http://www.cys.cic.ipn.mx/ojs/index.php/CyS/article/view/2865 %7 2018 %D 2018 %J Computación y Sistemas %V 21 %N 4
[14]
E. Galbrun and P. Miettinen, “Mining Redescriptions with Siren,” ACM Transactions on Knowledge Discovery from Data, vol. 12, no. 1, 2018.
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@article{galbrun17mining, TITLE = {Mining Redescriptions with {Siren}}, AUTHOR = {Galbrun, Esther and Miettinen, Pauli}, LANGUAGE = {eng}, DOI = {10.1145/3007212}, PUBLISHER = {ACM}, ADDRESS = {New York, NY}, YEAR = {2018}, JOURNAL = {ACM Transactions on Knowledge Discovery from Data}, VOLUME = {12}, NUMBER = {1}, EID = {6}, }
Endnote
%0 Journal Article %A Galbrun, Esther %A Miettinen, Pauli %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Mining Redescriptions with Siren : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-227B-F %R 10.1145/3007212 %7 2018 %D 2018 %J ACM Transactions on Knowledge Discovery from Data %V 12 %N 1 %Z sequence number: 6 %I ACM %C New York, NY
[15]
E. Gius, N. Reiter, J. Strötgen, and M. Willand, “SANTA: Systematische Analyse Narrativer Texte durch Annotation,” in Kritik der digitalen Vernunft (DHd 2018), Köln, Germany. (Accepted/in press)
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@inproceedings{GiusDHd2018, TITLE = {{{SANTA}: {Systematische Analyse Narrativer Texte durch Annotation}}}, AUTHOR = {Gius, Evelyn and Reiter, Nils and Str{\"o}tgen, Jannik and Willand, Marcus}, LANGUAGE = {deu}, URL = {http://dhd2018.uni-koeln.de/}, YEAR = {2018}, PUBLREMARK = {Accepted}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Kritik der digitalen Vernunft (DHd 2018)}, ADDRESS = {K{\"o}ln, Germany}, }
Endnote
%0 Conference Proceedings %A Gius, Evelyn %A Reiter, Nils %A Strötgen, Jannik %A Willand, Marcus %+ External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T SANTA: Systematische Analyse Narrativer Texte durch Annotation : %G deu %U http://hdl.handle.net/11858/00-001M-0000-002E-73EC-4 %D 2017 %B 5. Tagung des Verbands Digital Humanities im deutschsprachigen Raum e.V. %Z date of event: 2018-02-26 - 2018-03-02 %C Köln, Germany %B Kritik der digitalen Vernunft
[16]
D. Gupta, K. Berberich, J. Strötgen, and D. Zeinalipour-Yazti, “Generating Semantic Aspects for Queries,” in JCDL’18, Joint Conference on Digital Libraries, Fort Worth, TX, USA, 2018.
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@inproceedings{GuptaJCDL2018, TITLE = {Generating Semantic Aspects for Queries}, AUTHOR = {Gupta, Dhruv and Berberich, Klaus and Str{\"o}tgen, Jannik and Zeinalipour-Yazti, Demetrios}, LANGUAGE = {eng}, ISBN = {978-1-4503-5178-2}, DOI = {10.1145/3197026.3203900}, PUBLISHER = {ACM}, YEAR = {2018}, DATE = {2018}, BOOKTITLE = {JCDL'18, Joint Conference on Digital Libraries}, PAGES = {335--336}, ADDRESS = {Fort Worth, TX, USA}, }
Endnote
%0 Conference Proceedings %A Gupta, Dhruv %A Berberich, Klaus %A Strötgen, Jannik %A Zeinalipour-Yazti, Demetrios %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Generating Semantic Aspects for Queries : %G eng %U http://hdl.handle.net/21.11116/0000-0001-904D-6 %R 10.1145/3197026.3203900 %D 2018 %B Joint Conference on Digital Libraries %Z date of event: 2018-06-03 - 2018-06-07 %C Fort Worth, TX, USA %B JCDL'18 %P 335 - 336 %I ACM %@ 978-1-4503-5178-2
[17]
D. Gupta and K. Berberich, “Identifying Time Intervals for Knowledge Graph Facts,” in Companion of the World Wide Web Conference (WWW 2018), Lyon, France, 2018.
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@inproceedings{GuptaWWW2017, TITLE = {Identifying Time Intervals for Knowledge Graph Facts}, AUTHOR = {Gupta, Dhruv and Berberich, Klaus}, LANGUAGE = {eng}, ISBN = {978-1-4503-5640-4}, DOI = {10.1145/3184558.3186917}, PUBLISHER = {ACM}, YEAR = {2018}, DATE = {2018}, BOOKTITLE = {Companion of the World Wide Web Conference (WWW 2018)}, EDITOR = {Champin, Pierre-Antoine and Gandon, Fabien and M{\'e}dini, Lionel}, PAGES = {37--38}, ADDRESS = {Lyon, France}, }
Endnote
%0 Conference Proceedings %A Gupta, Dhruv %A Berberich, Klaus %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Identifying Time Intervals for Knowledge Graph Facts : %G eng %U http://hdl.handle.net/21.11116/0000-0001-411F-4 %R 10.1145/3184558.3186917 %D 2018 %B The Web Conference %Z date of event: 2018-04-23 - 2018-04-27 %C Lyon, France %B Companion of the World Wide Web Conference %E Champin, Pierre-Antoine; Gandon, Fabien; Médini, Lionel %P 37 - 38 %I ACM %@ 978-1-4503-5640-4
[18]
V. T. Ho, D. Stepanova, M. H. Gad-Elrab, and E. Kharlamov, “Learning Rules from Incomplete KGs using Embeddings,” in Proceedings of the 17th International Semantic Web Conference (ISWC 2018), Monterey, CA, USA. (Accepted/in press)
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@inproceedings{StepanovaISWC2018b, TITLE = {Learning Rules from Incomplete {KGs} using Embeddings}, AUTHOR = {Ho, Vinh Thinh and Stepanova, Daria and Gad-Elrab, Mohamed Hassan and Kharlamov, Evgeny}, LANGUAGE = {eng}, YEAR = {2018}, PUBLREMARK = {Accepted}, BOOKTITLE = {Proceedings of the 17th International Semantic Web Conference (ISWC 2018)}, ADDRESS = {Monterey, CA, USA}, }
Endnote
%0 Conference Proceedings %A Ho, Vinh Thinh %A Stepanova, Daria %A Gad-Elrab, Mohamed Hassan %A Kharlamov, Evgeny %+ 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 Learning Rules from Incomplete KGs using Embeddings : %G eng %U http://hdl.handle.net/21.11116/0000-0001-905B-6 %D 2018 %B The 17th International Semantic Web Conference %Z date of event: 2018-10-08 - 2018-10-12 %C Monterey, CA, USA %B Proceedings of the 17th International Semantic Web Conference
[19]
V. T. Ho, D. Stepanova, M. H. Gad-Elrab, and E. Kharlamov, “Rule Learning from Knowledge Graphs Guided by Embedding Models,” in Proceedings of the 17th International Semantic Web Conference (ISWC 2018), Monterey, CA, USA. (Accepted/in press)
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@inproceedings{StepanovaISWC2018, TITLE = {Rule Learning from Knowledge Graphs Guided by Embedding Models}, AUTHOR = {Ho, Vinh Thinh and Stepanova, Daria and Gad-Elrab, Mohamed Hassan and Kharlamov, Evgeny}, LANGUAGE = {eng}, YEAR = {2018}, PUBLREMARK = {Accepted}, BOOKTITLE = {Proceedings of the 17th International Semantic Web Conference (ISWC 2018)}, ADDRESS = {Monterey, CA, USA}, }
Endnote
%0 Conference Proceedings %A Ho, Vinh Thinh %A Stepanova, Daria %A Gad-Elrab, Mohamed Hassan %A Kharlamov, Evgeny %+ 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 Rule Learning from Knowledge Graphs Guided by Embedding Models : %G eng %U http://hdl.handle.net/21.11116/0000-0001-9058-9 %D 2018 %B The 17th International Semantic Web Conference %Z date of event: 2018-10-08 - 2018-10-12 %C Monterey, CA, USA %B Proceedings of the 17th International Semantic Web Conference
[20]
V. T. Ho, “An Embedding-based Approach to Rule Learning from Knowledge Graphs,” Universität des Saarlandes, Saarbrücken, 2018.
Abstract
Knowledge Graphs (KGs) play an important role in various information systems and have application in many fields such as Semantic Web Search, Question Answering and Information Retrieval. KGs present information in the form of entities and relationships between them. Modern KGs could contain up to millions of entities and billions of facts, and they are usually built using automatic construction methods. As a result, despite the huge size of KGs, a large number of facts between their entities are still missing. That is the reason why we see the importance of the task of Knowledge Graph Completion (a.k.a. Link Prediction), which concerns the prediction of those missing facts. Rules over a Knowledge Graph capture interpretable patterns in data and various methods for rule learning have been proposed. Since KGs are inherently incomplete, rules can be used to deduce missing facts. Statistical measures for learned rules such as confidence reflect rule quality well when the KG is reasonably complete; however, these measures might be misleading otherwise. So, it is difficult to learn high-quality rules from the KG alone, and scalability dictates that only a small set of candidate rules is generated. Therefore, the ranking and pruning of candidate rules are major problems. To address this issue, we propose a rule learning method that utilizes probabilistic representations of missing facts. In particular, we iteratively extend rules induced from a KG by relying on feedback from a precomputed embedding model over the KG and optionally external information sources including text corpora. The contributions of this thesis are as follows: • We introduce a framework for rule learning guided by external sources. • We propose a concrete instantiation of our framework to show how to learn high- quality rules by utilizing feedback from a pretrained embedding model. • We conducted experiments on real-world KGs that demonstrate the effectiveness of our novel approach with respect to both the quality of the learned rules and fact predictions that they produce.
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@misc{HoMaster2018, TITLE = {An Embedding-based Approach to Rule Learning from Knowledge Graphs}, AUTHOR = {Ho, Vinh Thinh}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2018}, DATE = {2018}, ABSTRACT = {Knowledge Graphs (KGs) play an important role in various information systems and have application in many {fi}elds such as Semantic Web Search, Question Answering and Information Retrieval. KGs present information in the form of entities and relationships between them. Modern KGs could contain up to millions of entities and billions of facts, and they are usually built using automatic construction methods. As a result, despite the huge size of KGs, a large number of facts between their entities are still missing. That is the reason why we see the importance of the task of Knowledge Graph Completion (a.k.a. Link Prediction), which concerns the prediction of those missing facts. Rules over a Knowledge Graph capture interpretable patterns in data and various methods for rule learning have been proposed. Since KGs are inherently incomplete, rules can be used to deduce missing facts. Statistical measures for learned rules such as con{fi}dence re{fl}ect rule quality well when the KG is reasonably complete; however, these measures might be misleading otherwise. So, it is difficult to learn high-quality rules from the KG alone, and scalability dictates that only a small set of candidate rules is generated. Therefore, the ranking and pruning of candidate rules are major problems. To address this issue, we propose a rule learning method that utilizes probabilistic representations of missing facts. In particular, we iteratively extend rules induced from a KG by relying on feedback from a precomputed embedding model over the KG and optionally external information sources including text corpora. The contributions of this thesis are as follows: \mbox{$\bullet$} We introduce a framework for rule learning guided by external sources. \mbox{$\bullet$} We propose a concrete instantiation of our framework to show how to learn high- quality rules by utilizing feedback from a pretrained embedding model. \mbox{$\bullet$} We conducted experiments on real-world KGs that demonstrate the effectiveness of our novel approach with respect to both the quality of the learned rules and fact predictions that they produce.}, }
Endnote
%0 Thesis %A Ho, Vinh Thinh %A referee: Weikum, Gerhard %Y Stepanova, Daria %+ Databases and Information Systems, MPI for Informatics, Max Planck Society International max planck research school, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T An Embedding-based Approach to Rule Learning from Knowledge Graphs : %G eng %U http://hdl.handle.net/21.11116/0000-0001-DE06-F %I Universität des Saarlandes %C Saarbrücken %D 2018 %P 60 %X Knowledge Graphs (KGs) play an important role in various information systems and have application in many fields such as Semantic Web Search, Question Answering and Information Retrieval. KGs present information in the form of entities and relationships between them. Modern KGs could contain up to millions of entities and billions of facts, and they are usually built using automatic construction methods. As a result, despite the huge size of KGs, a large number of facts between their entities are still missing. That is the reason why we see the importance of the task of Knowledge Graph Completion (a.k.a. Link Prediction), which concerns the prediction of those missing facts. Rules over a Knowledge Graph capture interpretable patterns in data and various methods for rule learning have been proposed. Since KGs are inherently incomplete, rules can be used to deduce missing facts. Statistical measures for learned rules such as confidence reflect rule quality well when the KG is reasonably complete; however, these measures might be misleading otherwise. So, it is difficult to learn high-quality rules from the KG alone, and scalability dictates that only a small set of candidate rules is generated. Therefore, the ranking and pruning of candidate rules are major problems. To address this issue, we propose a rule learning method that utilizes probabilistic representations of missing facts. In particular, we iteratively extend rules induced from a KG by relying on feedback from a precomputed embedding model over the KG and optionally external information sources including text corpora. The contributions of this thesis are as follows: • We introduce a framework for rule learning guided by external sources. • We propose a concrete instantiation of our framework to show how to learn high- quality rules by utilizing feedback from a pretrained embedding model. • We conducted experiments on real-world KGs that demonstrate the effectiveness of our novel approach with respect to both the quality of the learned rules and fact predictions that they produce.
[21]
K. Hui, A. Yates, K. Berberich, and G. de Melo, “Co-PACRR: A Context-Aware Neural IR Model for Ad-hoc Retrieval,” in WSDM’18, 11th ACM International Conference on Web Search and Data Mining, Marina Del Rey, CA, USA, 2018.
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@inproceedings{Hui_WSDM2018, TITLE = {Co-{PACRR}: {A} Context-Aware Neural {IR} Model for Ad-hoc Retrieval}, AUTHOR = {Hui, Kai and Yates, Andrew and Berberich, Klaus and de Melo, Gerard}, LANGUAGE = {eng}, ISBN = {978-1-4503-5581-0}, DOI = {10.1145/3159652.3159689}, PUBLISHER = {ACM}, YEAR = {2018}, DATE = {2018}, BOOKTITLE = {WSDM'18, 11th ACM International Conference on Web Search and Data Mining}, PAGES = {279--287}, ADDRESS = {Marina Del Rey, CA, USA}, }
Endnote
%0 Conference Proceedings %A Hui, Kai %A Yates, Andrew %A Berberich, Klaus %A de Melo, Gerard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Co-PACRR: A Context-Aware Neural IR Model for Ad-hoc Retrieval : %G eng %U http://hdl.handle.net/21.11116/0000-0000-6367-D %R 10.1145/3159652.3159689 %D 2018 %B 11th ACM International Conference on Web Search and Data Mining %Z date of event: 2018-02-05 - 2018-02-09 %C Marina Del Rey, CA, USA %B WSDM'18 %P 279 - 287 %I ACM %@ 978-1-4503-5581-0
[22]
H. Jhavar and P. Mirza, “EMOFIEL: Mapping Emotions of Relationships in a Story,” in Companion of the World Wide Web Conference (WWW 2018), Lyon, France, 2018.
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@inproceedings{JhavarWWW2018, TITLE = {{EMOFIEL}: {M}apping Emotions of Relationships in a Story}, AUTHOR = {Jhavar, Harshita and Mirza, Paramita}, LANGUAGE = {eng}, ISBN = {978-1-4503-5640-4}, DOI = {10.1145/3184558.3186989}, PUBLISHER = {ACM}, YEAR = {2018}, DATE = {2018}, BOOKTITLE = {Companion of the World Wide Web Conference (WWW 2018)}, EDITOR = {Champin, Pierre-Antoine and Gandon, Fabien and M{\'e}dini, Lionel}, PAGES = {243--246}, ADDRESS = {Lyon, France}, }
Endnote
%0 Conference Proceedings %A Jhavar, Harshita %A Mirza, Paramita %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T EMOFIEL: Mapping Emotions of Relationships in a Story : %G eng %U http://hdl.handle.net/21.11116/0000-0001-4B96-2 %R 10.1145/3184558.3186989 %D 2018 %B The Web Conference %Z date of event: 2018-04-23 - 2018-04-27 %C Lyon, France %B Companion of the World Wide Web Conference %E Champin, Pierre-Antoine; Gandon, Fabien; Médini, Lionel %P 243 - 246 %I ACM %@ 978-1-4503-5640-4
[23]
Z. Jia, A. Abujabal, R. S. Roy, J. Strötgen, and G. Weikum, “TempQuestions: A Benchmark for Temporal Question Answering,” in Companion of the World Wide Web Conference (WWW 2018), Lyon, France, 2018.
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@inproceedings{JiaWWW2017, TITLE = {{TempQuestions}: {A} Benchmark for Temporal Question Answering}, AUTHOR = {Jia, Zhen and Abujabal, Abdalghani and Roy, Rishiraj Saha and Str{\"o}tgen, Jannik and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-5640-4}, DOI = {10.1145/3184558.3191536}, PUBLISHER = {ACM}, YEAR = {2018}, DATE = {2018}, BOOKTITLE = {Companion of the World Wide Web Conference (WWW 2018)}, EDITOR = {Champin, Pierre-Antoine and Gandon, Fabien and M{\'e}dini, Lionel}, PAGES = {1057--1062}, ADDRESS = {Lyon, France}, }
Endnote
%0 Conference Proceedings %A Jia, Zhen %A Abujabal, Abdalghani %A Roy, Rishiraj Saha %A Strötgen, Jannik %A Weikum, Gerhard %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T TempQuestions: A Benchmark for Temporal Question Answering : %G eng %U http://hdl.handle.net/21.11116/0000-0001-3C80-B %R 10.1145/3184558.3191536 %D 2018 %B The Web Conference %Z date of event: 2018-04-23 - 2018-04-27 %C Lyon, France %B Companion of the World Wide Web Conference %E Champin, Pierre-Antoine; Gandon, Fabien; Médini, Lionel %P 1057 - 1062 %I ACM %@ 978-1-4503-5640-4
[24]
J. Kalofolias, E. Galbrun, and P. Miettinen, “From Sets of Good Redescriptions to Good Sets of Redescriptions,” Knowledge and Information Systems, 2018.
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@article{kalofolias18from, TITLE = {From Sets of Good Redescriptions to Good Sets of Redescriptions}, AUTHOR = {Kalofolias, Janis and Galbrun, Esther and Miettinen, Pauli}, LANGUAGE = {eng}, ISSN = {0219-1377}, DOI = {10.1007/s10115-017-1149-7}, PUBLISHER = {Springer}, ADDRESS = {New York, NY}, YEAR = {2018}, JOURNAL = {Knowledge and Information Systems}, }
Endnote
%0 Journal Article %A Kalofolias, Janis %A Galbrun, Esther %A Miettinen, Pauli %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T From Sets of Good Redescriptions to Good Sets of Redescriptions : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002E-90D1-5 %R 10.1007/s10115-017-1149-7 %7 2018-01-19 %D 2018 %8 19.01.2018 %J Knowledge and Information Systems %I Springer %C New York, NY %@ false
[25]
S. Karaev, J. Hook, and P. Miettinen, “Latitude: A Model for Mixed Linear-Tropical Matrix Factorization,” 2018. [Online]. Available: http://arxiv.org/abs/1801.06136. (arXiv: 1801.06136)
Abstract
Nonnegative matrix factorization (NMF) is one of the most frequently-used matrix factorization models in data analysis. A significant reason to the popularity of NMF is its interpretability and the `parts of whole' interpretation of its components. Recently, max-times, or subtropical, matrix factorization (SMF) has been introduced as an alternative model with equally interpretable `winner takes it all' interpretation. In this paper we propose a new mixed linear--tropical model, and a new algorithm, called Latitude, that combines NMF and SMF, being able to smoothly alternate between the two. In our model, the data is modeled using the latent factors and latent parameters that control whether the factors are interpreted as NMF or SMF features, or their mixtures. We present an algorithm for our novel matrix factorization. Our experiments show that our algorithm improves over both baselines, and can yield interpretable results that reveal more of the latent structure than either NMF or SMF alone.
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@online{Karaev2018, TITLE = {Latitude: A Model for Mixed Linear-Tropical Matrix Factorization}, AUTHOR = {Karaev, Sanjar and Hook, James and Miettinen, Pauli}, URL = {http://arxiv.org/abs/1801.06136}, EPRINT = {1801.06136}, EPRINTTYPE = {arXiv}, YEAR = {2018}, ABSTRACT = {Nonnegative matrix factorization (NMF) is one of the most frequently-used matrix factorization models in data analysis. A significant reason to the popularity of NMF is its interpretability and the `parts of whole' interpretation of its components. Recently, max-times, or subtropical, matrix factorization (SMF) has been introduced as an alternative model with equally interpretable `winner takes it all' interpretation. In this paper we propose a new mixed linear--tropical model, and a new algorithm, called Latitude, that combines NMF and SMF, being able to smoothly alternate between the two. In our model, the data is modeled using the latent factors and latent parameters that control whether the factors are interpreted as NMF or SMF features, or their mixtures. We present an algorithm for our novel matrix factorization. Our experiments show that our algorithm improves over both baselines, and can yield interpretable results that reveal more of the latent structure than either NMF or SMF alone.}, }
Endnote
%0 Report %A Karaev, Sanjar %A Hook, James %A Miettinen, Pauli %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Latitude: A Model for Mixed Linear-Tropical Matrix Factorization : %U http://hdl.handle.net/21.11116/0000-0000-636B-9 %U http://arxiv.org/abs/1801.06136 %D 2018 %X Nonnegative matrix factorization (NMF) is one of the most frequently-used matrix factorization models in data analysis. A significant reason to the popularity of NMF is its interpretability and the `parts of whole' interpretation of its components. Recently, max-times, or subtropical, matrix factorization (SMF) has been introduced as an alternative model with equally interpretable `winner takes it all' interpretation. In this paper we propose a new mixed linear--tropical model, and a new algorithm, called Latitude, that combines NMF and SMF, being able to smoothly alternate between the two. In our model, the data is modeled using the latent factors and latent parameters that control whether the factors are interpreted as NMF or SMF features, or their mixtures. We present an algorithm for our novel matrix factorization. Our experiments show that our algorithm improves over both baselines, and can yield interpretable results that reveal more of the latent structure than either NMF or SMF alone. %K Computer Science, Learning, cs.LG
[26]
P. Lahoti, G. Weikum, and K. P. Gummadi, “iFair: Learning Individually Fair Data Representations for Algorithmic Decision Making,” 2018. [Online]. Available: http://arxiv.org/abs/1806.01059. (arXiv: 1806.01059)
Abstract
People are rated and ranked, towards algorithmic decision making in an increasing number of applications, typically based on machine learning. Research on how to incorporate fairness into such tasks has prevalently pursued the paradigm of group fairness: ensuring that each ethnic or social group receives its fair share in the outcome of classifiers and rankings. In contrast, the alternative paradigm of individual fairness has received relatively little attention. This paper introduces a method for probabilistically clustering user records into a low-rank representation that captures individual fairness yet also achieves high accuracy in classification and regression models. Our notion of individual fairness requires that users who are similar in all task-relevant attributes such as job qualification, and disregarding all potentially discriminating attributes such as gender, should have similar outcomes. Since the case for fairness is ubiquitous across many tasks, we aim to learn general representations that can be applied to arbitrary downstream use-cases. We demonstrate the versatility of our method by applying it to classification and learning-to-rank tasks on two real-world datasets. Our experiments show substantial improvements over the best prior work for this setting.
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@online{Lahoti_arXiv1806.01059, TITLE = {{iFair}: {L}earning Individually Fair Data Representations for Algorithmic Decision Making}, AUTHOR = {Lahoti, Preethi and Weikum, Gerhard and Gummadi, Krishna P.}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1806.01059}, EPRINT = {1806.01059}, EPRINTTYPE = {arXiv}, YEAR = {2018}, ABSTRACT = {People are rated and ranked, towards algorithmic decision making in an increasing number of applications, typically based on machine learning. Research on how to incorporate fairness into such tasks has prevalently pursued the paradigm of group fairness: ensuring that each ethnic or social group receives its fair share in the outcome of classifiers and rankings. In contrast, the alternative paradigm of individual fairness has received relatively little attention. This paper introduces a method for probabilistically clustering user records into a low-rank representation that captures individual fairness yet also achieves high accuracy in classification and regression models. Our notion of individual fairness requires that users who are similar in all task-relevant attributes such as job qualification, and disregarding all potentially discriminating attributes such as gender, should have similar outcomes. Since the case for fairness is ubiquitous across many tasks, we aim to learn general representations that can be applied to arbitrary downstream use-cases. We demonstrate the versatility of our method by applying it to classification and learning-to-rank tasks on two real-world datasets. Our experiments show substantial improvements over the best prior work for this setting.}, }
Endnote
%0 Report %A Lahoti, Preethi %A Weikum, Gerhard %A Gummadi, Krishna P. %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T iFair: Learning Individually Fair Data Representations for Algorithmic Decision Making : %G eng %U http://hdl.handle.net/21.11116/0000-0002-1545-9 %U http://arxiv.org/abs/1806.01059 %D 2018 %X People are rated and ranked, towards algorithmic decision making in an increasing number of applications, typically based on machine learning. Research on how to incorporate fairness into such tasks has prevalently pursued the paradigm of group fairness: ensuring that each ethnic or social group receives its fair share in the outcome of classifiers and rankings. In contrast, the alternative paradigm of individual fairness has received relatively little attention. This paper introduces a method for probabilistically clustering user records into a low-rank representation that captures individual fairness yet also achieves high accuracy in classification and regression models. Our notion of individual fairness requires that users who are similar in all task-relevant attributes such as job qualification, and disregarding all potentially discriminating attributes such as gender, should have similar outcomes. Since the case for fairness is ubiquitous across many tasks, we aim to learn general representations that can be applied to arbitrary downstream use-cases. We demonstrate the versatility of our method by applying it to classification and learning-to-rank tasks on two real-world datasets. Our experiments show substantial improvements over the best prior work for this setting. %K Computer Science, Learning, cs.LG,Computer Science, Information Retrieval, cs.IR,Statistics, Machine Learning, stat.ML
[27]
P. Mirza, S. Razniewski, F. Darari, and G. Weikum, “Enriching Knowledge Bases with Counting Quantifiers,” in Proceedings of the 17th International Semantic Web Conference (ISWC 2018), Monterey, CA, USA. (Accepted/in press)
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@inproceedings{MirzaISWC2018, TITLE = {Enriching Knowledge Bases with Counting Quantifiers}, AUTHOR = {Mirza, Paramita and Razniewski, Simon and Darari, Fariz and Weikum, Gerhard}, LANGUAGE = {eng}, YEAR = {2018}, PUBLREMARK = {Accepted}, BOOKTITLE = {Proceedings of the 17th International Semantic Web Conference (ISWC 2018)}, ADDRESS = {Monterey, CA, USA}, }
Endnote
%0 Conference Proceedings %A Mirza, Paramita %A Razniewski, Simon %A Darari, Fariz %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Enriching Knowledge Bases with Counting Quantifiers : %G eng %U http://hdl.handle.net/21.11116/0000-0001-E170-2 %D 2018 %B The 17th International Semantic Web Conference %Z date of event: 2018-10-08 - 2018-10-12 %C Monterey, CA, USA %B Proceedings of the 17th International Semantic Web Conference
[28]
P. Mirza, S. Razniewski, F. Darari, and G. Weikum, “Enriching Knowledge Bases with Counting Quantifiers,” 2018. [Online]. Available: http://arxiv.org/abs/1807.03656. (arXiv: 1807.03656)
Abstract
Information extraction traditionally focuses on extracting relations between identifiable entities, such as <Monterey, locatedIn, California>. Yet, texts often also contain Counting information, stating that a subject is in a specific relation with a number of objects, without mentioning the objects themselves, for example, "California is divided into 58 counties". Such counting quantifiers can help in a variety of tasks such as query answering or knowledge base curation, but are neglected by prior work. This paper develops the first full-fledged system for extracting counting information from text, called CINEX. We employ distant supervision using fact counts from a knowledge base as training seeds, and develop novel techniques for dealing with several challenges: (i) non-maximal training seeds due to the incompleteness of knowledge bases, (ii) sparse and skewed observations in text sources, and (iii) high diversity of linguistic patterns. Experiments with five human-evaluated relations show that CINEX can achieve 60% average precision for extracting counting information. In a large-scale experiment, we demonstrate the potential for knowledge base enrichment by applying CINEX to 2,474 frequent relations in Wikidata. CINEX can assert the existence of 2.5M facts for 110 distinct relations, which is 28% more than the existing Wikidata facts for these relations.
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@online{Mirza_arXiv:1807.03656, TITLE = {Enriching Knowledge Bases with Counting Quantifiers}, AUTHOR = {Mirza, Paramita and Razniewski, Simon and Darari, Fariz and Weikum, Gerhard}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1807.03656}, EPRINT = {1807.03656}, EPRINTTYPE = {arXiv}, YEAR = {2018}, ABSTRACT = {Information extraction traditionally focuses on extracting relations between identifiable entities, such as <Monterey, locatedIn, California>. Yet, texts often also contain Counting information, stating that a subject is in a specific relation with a number of objects, without mentioning the objects themselves, for example, "California is divided into 58 counties". Such counting quantifiers can help in a variety of tasks such as query answering or knowledge base curation, but are neglected by prior work. This paper develops the first full-fledged system for extracting counting information from text, called CINEX. We employ distant supervision using fact counts from a knowledge base as training seeds, and develop novel techniques for dealing with several challenges: (i) non-maximal training seeds due to the incompleteness of knowledge bases, (ii) sparse and skewed observations in text sources, and (iii) high diversity of linguistic patterns. Experiments with five human-evaluated relations show that CINEX can achieve 60% average precision for extracting counting information. In a large-scale experiment, we demonstrate the potential for knowledge base enrichment by applying CINEX to 2,474 frequent relations in Wikidata. CINEX can assert the existence of 2.5M facts for 110 distinct relations, which is 28% more than the existing Wikidata facts for these relations.}, }
Endnote
%0 Report %A Mirza, Paramita %A Razniewski, Simon %A Darari, Fariz %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Enriching Knowledge Bases with Counting Quantifiers : %G eng %U http://hdl.handle.net/21.11116/0000-0001-E16D-7 %U http://arxiv.org/abs/1807.03656 %D 2018 %X Information extraction traditionally focuses on extracting relations between identifiable entities, such as <Monterey, locatedIn, California>. Yet, texts often also contain Counting information, stating that a subject is in a specific relation with a number of objects, without mentioning the objects themselves, for example, "California is divided into 58 counties". Such counting quantifiers can help in a variety of tasks such as query answering or knowledge base curation, but are neglected by prior work. This paper develops the first full-fledged system for extracting counting information from text, called CINEX. We employ distant supervision using fact counts from a knowledge base as training seeds, and develop novel techniques for dealing with several challenges: (i) non-maximal training seeds due to the incompleteness of knowledge bases, (ii) sparse and skewed observations in text sources, and (iii) high diversity of linguistic patterns. Experiments with five human-evaluated relations show that CINEX can achieve 60% average precision for extracting counting information. In a large-scale experiment, we demonstrate the potential for knowledge base enrichment by applying CINEX to 2,474 frequent relations in Wikidata. CINEX can assert the existence of 2.5M facts for 110 distinct relations, which is 28% more than the existing Wikidata facts for these relations. %K Computer Science, Computation and Language, cs.CL
[29]
A. Mishra, “Leveraging Semantic Annotations for Event-focused Search & Summarization,” Universität des Saarlandes, Saarbrücken, 2018.
Abstract
Today in this Big Data era, overwhelming amounts of textual information across different sources with a high degree of redundancy has made it hard for a consumer to retrospect on past events. A plausible solution is to link semantically similar information contained across the different sources to enforce a structure thereby providing multiple access paths to relevant information. Keeping this larger goal in view, this work uses Wikipedia and online news articles as two prominent yet disparate information sources to address the following three problems: • We address a linking problem to connect Wikipedia excerpts to news articles by casting it into an IR task. Our novel approach integrates time, geolocations, and entities with text to identify relevant documents that can be linked to a given excerpt. • We address an unsupervised extractive multi-document summarization task to generate a fixed-length event digest that facilitates efficient consumption of information contained within a large set of documents. Our novel approach proposes an ILP for global inference across text, time, geolocations, and entities associated with the event. • To estimate temporal focus of short event descriptions, we present a semi-supervised approach that leverages redundancy within a longitudinal news collection to estimate accurate probabilistic time models. Extensive experimental evaluations demonstrate the effectiveness and viability of our proposed approaches towards achieving the larger goal.
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@phdthesis{Mishraphd2018, TITLE = {Leveraging Semantic Annotations for Event-focused Search \& Summarization}, AUTHOR = {Mishra, Arunav}, LANGUAGE = {eng}, URL = {urn:nbn:de:bsz:291-scidok-ds-271081}, DOI = {10.22028/D291-27108}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2018}, ABSTRACT = {Today in this Big Data era, overwhelming amounts of textual information across different sources with a high degree of redundancy has made it hard for a consumer to retrospect on past events. A plausible solution is to link semantically similar information contained across the different sources to enforce a structure thereby providing multiple access paths to relevant information. Keeping this larger goal in view, this work uses Wikipedia and online news articles as two prominent yet disparate information sources to address the following three problems: \mbox{$\bullet$} We address a linking problem to connect Wikipedia excerpts to news articles by casting it into an IR task. Our novel approach integrates time, geolocations, and entities with text to identify relevant documents that can be linked to a given excerpt. \mbox{$\bullet$} We address an unsupervised extractive multi-document summarization task to generate a fixed-length event digest that facilitates efficient consumption of information contained within a large set of documents. Our novel approach proposes an ILP for global inference across text, time, geolocations, and entities associated with the event. \mbox{$\bullet$} To estimate temporal focus of short event descriptions, we present a semi-supervised approach that leverages redundancy within a longitudinal news collection to estimate accurate probabilistic time models. Extensive experimental evaluations demonstrate the effectiveness and viability of our proposed approaches towards achieving the larger goal.}, }
Endnote
%0 Thesis %A Mishra, Arunav %Y Berberich, Klaus %A referee: Weikum, Gerhard %A referee: Hauff, Claudia %+ Databases and Information Systems, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Leveraging Semantic Annotations for Event-focused Search & Summarization : %G eng %U http://hdl.handle.net/21.11116/0000-0001-1844-8 %U urn:nbn:de:bsz:291-scidok-ds-271081 %R 10.22028/D291-27108 %I Universit&#228;t des Saarlandes %C Saarbr&#252;cken %D 2018 %8 08.02.2018 %P 252 p. %V phd %9 phd %X Today in this Big Data era, overwhelming amounts of textual information across different sources with a high degree of redundancy has made it hard for a consumer to retrospect on past events. A plausible solution is to link semantically similar information contained across the different sources to enforce a structure thereby providing multiple access paths to relevant information. Keeping this larger goal in view, this work uses Wikipedia and online news articles as two prominent yet disparate information sources to address the following three problems: &#8226; We address a linking problem to connect Wikipedia excerpts to news articles by casting it into an IR task. Our novel approach integrates time, geolocations, and entities with text to identify relevant documents that can be linked to a given excerpt. &#8226; We address an unsupervised extractive multi-document summarization task to generate a fixed-length event digest that facilitates efficient consumption of information contained within a large set of documents. Our novel approach proposes an ILP for global inference across text, time, geolocations, and entities associated with the event. &#8226; To estimate temporal focus of short event descriptions, we present a semi-supervised approach that leverages redundancy within a longitudinal news collection to estimate accurate probabilistic time models. Extensive experimental evaluations demonstrate the effectiveness and viability of our proposed approaches towards achieving the larger goal. %U https://publikationen.sulb.uni-saarland.de/handle/20.500.11880/26995
[30]
S. Nag Chowdhury, N. Tandon, H. Ferhatosmanoglu, and G. Weikum, “VISIR: Visual and Semantic Image Label Refinement,” in WSDM’18, 11th ACM International Conference on Web Search and Data Mining, Marina Del Rey, CA, USA, 2018.
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@inproceedings{NagChowdhury_WSDM2018, TITLE = {{VISIR}: {V}isual and Semantic Image Label Refinement}, AUTHOR = {Nag Chowdhury, Sreyasi and Tandon, Niket and Ferhatosmanoglu, Hakan and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-5581-0}, DOI = {10.1145/3159652.3159693}, PUBLISHER = {ACM}, YEAR = {2018}, DATE = {2018}, BOOKTITLE = {WSDM'18, 11th ACM International Conference on Web Search and Data Mining}, PAGES = {117--125}, ADDRESS = {Marina Del Rey, CA, USA}, }
Endnote
%0 Conference Proceedings %A Nag Chowdhury, Sreyasi %A Tandon, Niket %A Ferhatosmanoglu, Hakan %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T VISIR: Visual and Semantic Image Label Refinement : %G eng %U http://hdl.handle.net/21.11116/0000-0001-3CA2-5 %R 10.1145/3159652.3159693 %D 2018 %B 11th ACM International Conference on Web Search and Data Mining %Z date of event: 2018-02-05 - 2018-02-09 %C Marina Del Rey, CA, USA %B WSDM'18 %P 117 - 125 %I ACM %@ 978-1-4503-5581-0
[31]
T. Pellissier Tanon, D. Stepanova, S. Razniewski, P. Mirza, and G. Weikum, “Completeness-aware Rule Learning from Knowledge Graphs,” in Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI 2018), Stockholm, Sweden, 2018.
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@inproceedings{PellissierIJCAI2018, TITLE = {Completeness-aware Rule Learning from Knowledge Graphs}, AUTHOR = {Pellissier Tanon, Thomas and Stepanova, Daria and Razniewski, Simon and Mirza, Paramita and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-0-9992411-2-7}, DOI = {10.24963/ijcai.2018/749}, PUBLISHER = {IJCAI}, YEAR = {2018}, BOOKTITLE = {Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI 2018)}, EDITOR = {Lang, J{\'e}r{\^o}me}, PAGES = {5339--5343}, ADDRESS = {Stockholm, Sweden}, }
Endnote
%0 Conference Proceedings %A Pellissier Tanon, Thomas %A Stepanova, Daria %A Razniewski, Simon %A Mirza, Paramita %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Completeness-aware Rule Learning from Knowledge Graphs : %G eng %U http://hdl.handle.net/21.11116/0000-0001-9070-D %R 10.24963/ijcai.2018/749 %D 2018 %B 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence %Z date of event: 2018-07-13 - 2018-07-19 %C Stockholm, Sweden %B Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence %E Lang, J&#233;r&#244;me %P 5339 - 5343 %I IJCAI %@ 978-0-9992411-2-7 %U https://doi.org/10.24963/ijcai.2018/749
[32]
K. Popat, S. Mukherjee, J. Strötgen, and G. Weikum, “CredEye: A Credibility Lens for Analyzing and Explaining Misinformation,” in Companion of the Word Wide Web Conference (WWW 2018), Lyon, France, 2018.
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@inproceedings{PopatWWW2017, TITLE = {{CredEye}: {A} Credibility Lens for Analyzing and Explaining Misinformation}, AUTHOR = {Popat, Kashyap and Mukherjee, Subhabrata and Str{\"o}tgen, Jannik and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-5640-4}, DOI = {10.1145/3184558.3186967}, PUBLISHER = {ACM}, YEAR = {2018}, DATE = {2018}, BOOKTITLE = {Companion of the Word Wide Web Conference (WWW 2018)}, EDITOR = {Champin, Pierre-Antoine and Gandon, Fabien and M{\'e}dini, Lionel}, PAGES = {155--158}, ADDRESS = {Lyon, France}, }
Endnote
%0 Conference Proceedings %A Popat, Kashyap %A Mukherjee, Subhabrata %A Str&#246;tgen, Jannik %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T CredEye: A Credibility Lens for Analyzing and Explaining Misinformation : %G eng %U http://hdl.handle.net/21.11116/0000-0000-B546-5 %R 10.1145/3184558.3186967 %D 2018 %B The Web Conference %Z date of event: 2018-04-23 - 2018-04-27 %C Lyon, France %B Companion of the Word Wide Web Conference %E Champin, Pierre-Antoine; Gandon , Fabien; M&#233;dini, Lionel %P 155 - 158 %I ACM %@ 978-1-4503-5640-4
[33]
S. Razniewski and G. Weikum, “Knowledge Base Recall: Detecting and Resolving the Unknown Unknowns,” ACM SIGWEB Newsletter, no. Spring, 2018.
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@article{Razniewski2018, TITLE = {Knowledge Base Recall: Detecting and Resolving the Unknown Unknowns}, AUTHOR = {Razniewski, Simon and Weikum, Gerhard}, LANGUAGE = {eng}, DOI = {10.1145/3210578.3210581}, PUBLISHER = {ACM}, ADDRESS = {New York, NY}, YEAR = {2018}, JOURNAL = {ACM SIGWEB Newsletter}, NUMBER = {Spring}, EID = {3}, }
Endnote
%0 Journal Article %A Razniewski, Simon %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Knowledge Base Recall: Detecting and Resolving the Unknown Unknowns : %G eng %U http://hdl.handle.net/21.11116/0000-0001-E175-D %R 10.1145/3210578.3210581 %7 2018 %D 2018 %J ACM SIGWEB Newsletter %N Spring %Z sequence number: 3 %I ACM %C New York, NY
[34]
D. Seyler, T. Dembelova, L. Del Corro, J. Hoffart, and G. Weikum, “A Study of the Importance of External Knowledge in the Named Entity Recognition Task,” in The 56th Annual Meeting of the Association for Computational Linguistics (ACL 2018), Melbourne, Australia, 2018.
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@inproceedings{AgrawalACL2018, TITLE = {A Study of the Importance of External Knowledge in the Named Entity Recognition Task}, AUTHOR = {Seyler, Dominic and Dembelova, Tatiana and Del Corro, Luciano and Hoffart, Johannes and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-948087-34-6}, PUBLISHER = {ACL}, YEAR = {2018}, BOOKTITLE = {The 56th Annual Meeting of the Association for Computational Linguistics (ACL 2018)}, PAGES = {241--246}, EID = {602}, ADDRESS = {Melbourne, Australia}, }
Endnote
%0 Conference Proceedings %A Seyler, Dominic %A Dembelova, Tatiana %A Del Corro, Luciano %A Hoffart, Johannes %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T A Study of the Importance of External Knowledge in the Named Entity Recognition Task : %G eng %U http://hdl.handle.net/21.11116/0000-0002-0C65-0 %D 2018 %B The 56th Annual Meeting of the Association for Computational Linguistics %Z date of event: 2018-07-15 - 2018-07-20 %C Melbourne, Australia %B The 56th Annual Meeting of the Association for Computational Linguistics %P 241 - 246 %Z sequence number: 602 %I ACL %@ 978-1-948087-34-6 %U http://aclweb.org/anthology/P18-2039
[35]
M. Singh, A. Mishra, Y. Oualil, K. Berberich, and D. Klakow, “Long-Span Language Models for Query-Focused Unsupervised Extractive Text Summarization,” in Advances in Information Retrieval (ECIR 2018), Grenoble, France, 2018.
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@inproceedings{SinghECIR2ss18, TITLE = {Long-Span Language Models for Query-Focused Unsupervised Extractive Text Summarization}, AUTHOR = {Singh, Mittul and Mishra, Arunav and Oualil, Youssef and Berberich, Klaus and Klakow, Dietrich}, LANGUAGE = {eng}, ISBN = {978-3-319-76940-0}, DOI = {10.1007/978-3-319-76941-7_59}, PUBLISHER = {Springer}, YEAR = {2018}, DATE = {2018}, BOOKTITLE = {Advances in Information Retrieval (ECIR 2018)}, EDITOR = {Pasi, Gabriella and Piwowarski, Benjamin and Azzopardi, Leif and Hanbury, Allan}, PAGES = {657--664}, SERIES = {Lecture Notes in Computer Science}, VOLUME = {10772}, ADDRESS = {Grenoble, France}, }
Endnote
%0 Conference Proceedings %A Singh, Mittul %A Mishra, Arunav %A Oualil, Youssef %A Berberich, Klaus %A Klakow, Dietrich %+ External Organizations External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Long-Span Language Models for Query-Focused Unsupervised Extractive Text Summarization : %G eng %U http://hdl.handle.net/21.11116/0000-0001-413D-2 %R 10.1007/978-3-319-76941-7_59 %D 2018 %B 40th European Conference on IR Research %Z date of event: 2018-03-26 - 2018-03-29 %C Grenoble, France %B Advances in Information Retrieval %E Pasi, Gabriella; Piwowarski, Benjamin; Azzopardi, Leif; Hanbury, Allan %P 657 - 664 %I Springer %@ 978-3-319-76940-0 %B Lecture Notes in Computer Science %N 10772
[36]
A. Spitz, J. Strötgen, and M. Gertz, “Predicting Document Creation Times in News Citation Networks,” in Companion of the World Wide Web Conference (WWW 2018), Lyon, France, 2018.
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@inproceedings{SpitzWWW2017, TITLE = {Predicting Document Creation Times in News Citation Networks}, AUTHOR = {Spitz, Andreas and Str{\"o}tgen, Jannik and Gertz, Michael}, LANGUAGE = {eng}, ISBN = {978-1-4503-5640-4}, DOI = {10.1145/3184558.3191633}, PUBLISHER = {ACM}, YEAR = {2018}, DATE = {2018}, BOOKTITLE = {Companion of the World Wide Web Conference (WWW 2018)}, EDITOR = {Champin, Pierre-Antoine and Gandon, Fabien and M{\'e}dini, Lionel}, PAGES = {1731--1736}, ADDRESS = {Lyon, France}, }
Endnote
%0 Conference Proceedings %A Spitz, Andreas %A Str&#246;tgen, Jannik %A Gertz, Michael %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Predicting Document Creation Times in News Citation Networks : %G eng %U http://hdl.handle.net/21.11116/0000-0000-B544-7 %R 10.1145/3184558.3191633 %D 2018 %B The Web Conference %Z date of event: 2018-04-23 - 2018-04-27 %C Lyon, France %B Companion of the World Wide Web Conference %E Champin, Pierre-Antoine; Gandon, Fabien; M&#233;dini, Lionel %P 1731 - 1736 %I ACM %@ 978-1-4503-5640-4
[37]
D. Stepanova, V. T. Ho, and M. H. Gad-Elrab, “Rule Induction and Reasoning over Knowledge Graphs,” in Proceedings of the 14th International Summer School on Reasoning Web, Luxembourg, Luxembourg. (Accepted/in press)
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@inproceedings{StepanovaRW2018, TITLE = {Rule Induction and Reasoning over Knowledge Graphs}, AUTHOR = {Stepanova, Daria and Ho, Vinh Thinh and Gad-Elrab, Mohamed Hassan}, LANGUAGE = {eng}, YEAR = {2018}, PUBLREMARK = {Accepted}, BOOKTITLE = {Proceedings of the 14th International Summer School on Reasoning Web}, ADDRESS = {Luxembourg, Luxembourg}, }
Endnote
%0 Conference Proceedings %A Stepanova, Daria %A Ho, Vinh Thinh %A Gad-Elrab, Mohamed Hassan %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Rule Induction and Reasoning over Knowledge Graphs : %G eng %U http://hdl.handle.net/21.11116/0000-0001-9066-9 %D 2018 %B 14th Reasoning Web Summer School %Z date of event: 2018-09-22 - 2018-09-26 %C Luxembourg, Luxembourg %B Proceedings of the 14th International Summer School on Reasoning Web
[38]
J. Strötgen, R. Andrade, and D. Gupta, “Putting Dates on the Map: Harvesting and Analyzing Street Names with Date Mentions and their Explanations,” in JCDL’18, Joint Conference on Digital Libraries, Fort Worth, TX, USA, 2018.
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@inproceedings{StroetgenJCDL2018, TITLE = {Putting Dates on the Map: {H}arvesting and Analyzing Street Names with Date Mentions and their Explanations}, AUTHOR = {Str{\"o}tgen, Jannik and Andrade, Rosita and Gupta, Dhruv}, LANGUAGE = {eng}, ISBN = {978-1-4503-5178-2}, DOI = {10.1145/3197026.3197035}, PUBLISHER = {ACM}, YEAR = {2018}, DATE = {2018}, BOOKTITLE = {JCDL'18, Joint Conference on Digital Libraries}, PAGES = {79--88}, ADDRESS = {Fort Worth, TX, USA}, }
Endnote
%0 Conference Proceedings %A Str&#246;tgen, Jannik %A Andrade, Rosita %A Gupta, Dhruv %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Putting Dates on the Map: Harvesting and Analyzing Street Names with Date Mentions and their Explanations : %G eng %U http://hdl.handle.net/21.11116/0000-0000-B548-3 %R 10.1145/3197026.3197035 %D 2018 %B Joint Conference on Digital Libraries %Z date of event: 2018-06-03 - 2018-06-07 %C Fort Worth, TX, USA %B JCDL'18 %P 79 - 88 %I ACM %@ 978-1-4503-5178-2
[39]
J. Strötgen, A.-L. Minard, L. Lange, M. Speranza, and B. Magnini, “KRAUTS: A German Temporally Annotated News Corpus,” in Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan, 2018.
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@inproceedings{StroetgenELREC2018, TITLE = {{KRAUTS}: {A German} Temporally Annotated News Corpus}, AUTHOR = {Str{\"o}tgen, Jannik and Minard, Anne-Lyse and Lange, Lukas and Speranza, Manuela and Magnini, Bernardo}, LANGUAGE = {eng}, ISBN = {979-10-95546-00-9}, URL = {http://lrec2018.lrec-conf.org/en/}, PUBLISHER = {ELRA}, YEAR = {2018}, BOOKTITLE = {Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, EDITOR = {Calzolari, Nicoletta and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Hasida, Koiti}, PAGES = {536--540}, ADDRESS = {Miyazaki, Japan}, }
Endnote
%0 Conference Proceedings %A Str&#246;tgen, Jannik %A Minard, Anne-Lyse %A Lange, Lukas %A Speranza, Manuela %A Magnini, Bernardo %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T KRAUTS: A German Temporally Annotated News Corpus : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002E-8B8C-E %U http://lrec2018.lrec-conf.org/en/ %D 2018 %B 11th Language Resources and Evaluation Conference %Z date of event: 2018-05-07 - 2018-05-12 %C Miyazaki, Japan %B Eleventh International Conference on Language Resources and Evaluation %E Calzolari, Nicoletta; Choukri, Khalid; Cieri, Christopher; Declerck, Thierry; Goggi, Sara; Hasida, Koiti %P 536 - 540 %I ELRA %@ 979-10-95546-00-9
[40]
H. Wu, Y. Ning, P. Chakraborty, J. Vreeken, N. Tatti, and N. Ramakrishnan, “Generating Realistic Synthetic Population Datasets,” ACM Transactions on Knowledge Discovery from Data, vol. 12, no. 4, 2018.
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@article{Wu_2018, TITLE = {Generating Realistic Synthetic Population Datasets}, AUTHOR = {Wu, Hao and Ning, Yue and Chakraborty, Prithwish and Vreeken, Jilles and Tatti, Nikolaj and Ramakrishnan, Naren}, LANGUAGE = {eng}, DOI = {10.1145/3182383}, PUBLISHER = {ACM}, ADDRESS = {New York, NY}, YEAR = {2018}, DATE = {2018}, JOURNAL = {ACM Transactions on Knowledge Discovery from Data}, VOLUME = {12}, NUMBER = {4}, PAGES = {1--22}, EID = {45}, }
Endnote
%0 Journal Article %A Wu, Hao %A Ning, Yue %A Chakraborty, Prithwish %A Vreeken, Jilles %A Tatti, Nikolaj %A Ramakrishnan, Naren %+ External Organizations External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T Generating Realistic Synthetic Population Datasets : %G eng %U http://hdl.handle.net/21.11116/0000-0002-16ED-B %R 10.1145/3182383 %7 2018 %D 2018 %J ACM Transactions on Knowledge Discovery from Data %O TKDD %V 12 %N 4 %& 1 %P 1 - 22 %Z sequence number: 45 %I ACM %C New York, NY