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}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {Proceedings of the World Wide Web Conference (WWW 2018)}, EDITOR = {Champin, Pierre-Antoine and Gandon, Fabien and M{\'e}dini, Lionel and Lalmas, Mounia and Ipeirotis, Panagiotis G.}, PAGES = {1053--1062}, ADDRESS = {Lyon, France}, }
Endnote
%0 Conference Proceedings %A Abujabal, Abdalghani %A 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]
A. Abujabal, R. S. Roy, M. Yahya, and G. Weikum, “ComQA: A Community-sourced Dataset for Complex Factoid Question Answering with Paraphrase Clusters,” 2018. [Online]. Available: http://arxiv.org/abs/1809.09528. (arXiv: 1809.09528)
Abstract
To bridge the gap between the capabilities of the state-of-the-art in factoid question answering (QA) and what real users ask, we need large datasets of real user questions that capture the various question phenomena users are interested in, and the diverse ways in which these questions are formulated. We introduce ComQA, a large dataset of real user questions that exhibit different challenging aspects such as temporal reasoning, compositionality, etc. ComQA questions come from the WikiAnswers community QA platform. Through a large crowdsourcing effort, we clean the question dataset, group questions into paraphrase clusters, and annotate clusters with their answers. ComQA contains 11,214 questions grouped into 4,834 paraphrase clusters. We detail the process of constructing ComQA, including the measures taken to ensure its high quality while making effective use of crowdsourcing. We also present an extensive analysis of the dataset and the results achieved by state-of-the-art systems on ComQA, demonstrating that our dataset can be a driver of future research on QA.
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@online{Abujabal_arXiv1809.09528, TITLE = {{ComQA}: {A} Community-sourced Dataset for Complex Factoid Question Answering with Paraphrase Clusters}, AUTHOR = {Abujabal, Abdalghani and Roy, Rishiraj Saha and Yahya, Mohamed and Weikum, Gerhard}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1809.09528}, EPRINT = {1809.09528}, EPRINTTYPE = {arXiv}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, ABSTRACT = {To bridge the gap between the capabilities of the state-of-the-art in factoid question answering (QA) and what real users ask, we need large datasets of real user questions that capture the various question phenomena users are interested in, and the diverse ways in which these questions are formulated. We introduce ComQA, a large dataset of real user questions that exhibit different challenging aspects such as temporal reasoning, compositionality, etc. ComQA questions come from the WikiAnswers community QA platform. Through a large crowdsourcing effort, we clean the question dataset, group questions into paraphrase clusters, and annotate clusters with their answers. ComQA contains 11,214 questions grouped into 4,834 paraphrase clusters. We detail the process of constructing ComQA, including the measures taken to ensure its high quality while making effective use of crowdsourcing. We also present an extensive analysis of the dataset and the results achieved by state-of-the-art systems on ComQA, demonstrating that our dataset can be a driver of future research on QA.}, }
Endnote
%0 Report %A Abujabal, Abdalghani %A 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 ComQA: A Community-sourced Dataset for Complex Factoid Question Answering with Paraphrase Clusters : %G eng %U http://hdl.handle.net/21.11116/0000-0002-A0FE-B %U http://arxiv.org/abs/1809.09528 %D 2018 %X To bridge the gap between the capabilities of the state-of-the-art in factoid question answering (QA) and what real users ask, we need large datasets of real user questions that capture the various question phenomena users are interested in, and the diverse ways in which these questions are formulated. We introduce ComQA, a large dataset of real user questions that exhibit different challenging aspects such as temporal reasoning, compositionality, etc. ComQA questions come from the WikiAnswers community QA platform. Through a large crowdsourcing effort, we clean the question dataset, group questions into paraphrase clusters, and annotate clusters with their answers. ComQA contains 11,214 questions grouped into 4,834 paraphrase clusters. We detail the process of constructing ComQA, including the measures taken to ensure its high quality while making effective use of crowdsourcing. We also present an extensive analysis of the dataset and the results achieved by state-of-the-art systems on ComQA, demonstrating that our dataset can be a driver of future research on QA. %K Computer Science, Computation and Language, cs.CL
[3]
P. Agarwal, J. Strötgen, L. Del Corro, J. Hoffart, and G. Weikum, “diaNED: Time-Aware Named Entity Disambiguation for Diachronic Corpora,” in The 56th Annual Meeting of the Association for Computational Linguistics (ACL 2018), Melbourne, Australia, 2018.
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@inproceedings{AgrawalACL2018a, TITLE = {{diaNED}: {T}ime-Aware Named Entity Disambiguation for Diachronic Corpora}, AUTHOR = {Agarwal, Prabal and Str{\"o}tgen, Jannik and Del Corro, Luciano and Hoffart, Johannes and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-948087-34-6}, URL = {https://aclanthology.coli.uni-saarland.de/volumes/proceedings-of-the-56th-annual-meeting-of-the-association-for-computational-linguistics-volume-2-short-papers}, PUBLISHER = {ACL}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {The 56th Annual Meeting of the Association for Computational Linguistics (ACL 2018)}, EDITOR = {Gurevych, Iryna and Miyao, Yusuke}, PAGES = {686--693}, EID = {602}, ADDRESS = {Melbourne, Australia}, }
Endnote
%0 Conference Proceedings %A Agarwal, Prabal %A Strötgen, Jannik %A Del Corro, Luciano %A Hoffart, Johannes %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T diaNED: Time-Aware Named Entity Disambiguation for Diachronic Corpora : %G eng %U http://hdl.handle.net/21.11116/0000-0001-9055-C %D 2018 %B The 56th Annual Meeting of the Association for Computational Linguistics %Z date of event: 2018-07-15 - 2018-07-20 %C Melbourne, Australia %B The 56th Annual Meeting of the Association for Computational Linguistics %E Gurevych, Iryna; Miyao, Yusuke %P 686 - 693 %Z sequence number: 602 %I ACL %@ 978-1-948087-34-6 %U http://aclweb.org/anthology/P18-2109
[4]
M. Antenore, G. Leone, A. Panconesi, and E. Terolli, “Together We Buy, Alone I Quit: Some Experimental Studies of Online Persuaders,” in DTUC’18 Digital Tools & Uses Congres, Paris, France, 2018.
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@inproceedings{Antenore:2018:TWB:3240117.3240119, TITLE = {Together We Buy, Alone {I} Quit: {S}ome Experimental Studies of Online Persuaders}, AUTHOR = {Antenore, Marzia and Leone, Giovanna and Panconesi, Alessandro and Terolli, Erisa}, LANGUAGE = {eng}, ISBN = {978-1-4503-6451-5}, DOI = {10.1145/3240117.3240119}, PUBLISHER = {ACM}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {DTUC'18 Digital Tools \& Uses Congres}, EDITOR = {Reyes, E. and Szoniecky, S. and Mkadmi, A. and Kembellec, G. and Fournier-S'niehotta, R. and Siala-Kallel, F. and Ammi, M. and Labelle, S.}, EID = {2}, ADDRESS = {Paris, France}, }
Endnote
%0 Conference Proceedings %A Antenore, Marzia %A Leone, Giovanna %A Panconesi, Alessandro %A Terolli, Erisa %+ External Organizations External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Together We Buy, Alone I Quit: Some Experimental Studies of Online Persuaders : %G eng %U http://hdl.handle.net/21.11116/0000-0002-A89D-0 %R 10.1145/3240117.3240119 %D 2018 %B First International Digital Tools & Uses Congress %Z date of event: 2018-10-03 - 2018-10-05 %C Paris, France %B DTUC'18 Digital Tools & Uses Congres %E Reyes, E.; Szoniecky, S.; Mkadmi, A.; Kembellec, G.; Fournier-S'niehotta, R.; Siala-Kallel, F.; Ammi, M.; Labelle, S. %Z sequence number: 2 %I ACM %@ 978-1-4503-6451-5
[5]
O. Balalau, C. Castillo, and M. Sozio, “EviDense: A Graph-Based Method for Finding Unique High-Impact Events with Succinct Keyword-Based Descriptions,” in Proceedings of the Twelfth International AAAI Conference on Web and Social Media (ICWSM 2018), Stanford, CA, USA, 2018.
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@inproceedings{Balalau_ICWSM2018, TITLE = {{EviDense}: {A} Graph-Based Method for Finding Unique High-Impact Events with Succinct Keyword-Based Descriptions}, AUTHOR = {Balalau, Oana and Castillo, Carlos and Sozio, Mauro}, LANGUAGE = {eng}, ISBN = {978-1-57735-798-8}, PUBLISHER = {AAAI}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of the Twelfth International AAAI Conference on Web and Social Media (ICWSM 2018)}, PAGES = {560--563}, ADDRESS = {Stanford, CA, USA}, }
Endnote
%0 Conference Proceedings %A Balalau, Oana %A Castillo, Carlos %A Sozio, Mauro %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T EviDense: A Graph-Based Method for Finding Unique High-Impact Events with Succinct Keyword-Based Descriptions : %G eng %U http://hdl.handle.net/21.11116/0000-0002-9CE8-9 %D 2018 %B 12th International AAAI Conference on Web and Social Media %Z date of event: 2018-06-25 - 2018-06-28 %C Stanford, CA, USA %B Proceedings of the Twelfth International AAAI Conference on Web and Social Media %P 560 - 563 %I AAAI %@ 978-1-57735-798-8 %U https://aaai.org/ocs/index.php/ICWSM/ICWSM18/paper/view/17889
[6]
V. Balaraman, S. Razniewski, and W. Nutt, “Recoin: Relative Completeness in Wikidata,” in Companion of the World Wide Web Conference (WWW 2018), Lyon, France, 2018.
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@inproceedings{BalaramanWWW2017, TITLE = {Recoin: {R}elative Completeness in {W}ikidata}, AUTHOR = {Balaraman, Vevake and Razniewski, Simon and Nutt, Werner}, LANGUAGE = {eng}, ISBN = {978-1-4503-5640-4}, DOI = {10.1145/3184558.3191641}, PUBLISHER = {ACM}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {Companion of the World Wide Web Conference (WWW 2018)}, EDITOR = {Champin, Pierre-Antoine and Gandon, Fabien and M{\'e}dini, Lionel}, PAGES = {1787--1792}, ADDRESS = {Lyon, France}, }
Endnote
%0 Conference Proceedings %A Balaraman, Vevake %A Razniewski, Simon %A Nutt, Werner %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Recoin: Relative Completeness in Wikidata : %G eng %U http://hdl.handle.net/21.11116/0000-0001-414A-3 %R 10.1145/3184558.3191641 %D 2018 %B The Web Conference %Z date of event: 2018-04-23 - 2018-04-27 %C Lyon, France %B Companion of the World Wide Web Conference %E Champin, Pierre-Antoine; Gandon, Fabien; Médini, Lionel %P 1787 - 1792 %I ACM %@ 978-1-4503-5640-4
[7]
A. J. Biega, K. P. Gummadi, and G. Weikum, “Equity of Attention: Amortizing Individual Fairness in Rankings,” in SIGIR’18, 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, Ann Arbor, MI, USA, 2018.
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@inproceedings{BiegaSIGIR2018, TITLE = {Equity of Attention: {A}mortizing Individual Fairness in Rankings}, AUTHOR = {Biega, Asia J. and Gummadi, Krishna P. and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-5022-8}, DOI = {10.1145/3209978.3210063}, PUBLISHER = {ACM}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {SIGIR'18, 41st International ACM SIGIR Conference on Research and Development in Information Retrieval}, PAGES = {405--414}, ADDRESS = {Ann Arbor, MI, USA}, }
Endnote
%0 Conference Proceedings %A Biega, Asia J. %A Gummadi, Krishna P. %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Equity of Attention: Amortizing Individual Fairness in Rankings : %G eng %U http://hdl.handle.net/21.11116/0000-0002-0D8A-5 %R 10.1145/3209978.3210063 %D 2018 %B 41st International ACM SIGIR Conference on Research and Development in Information Retrieval %Z date of event: 2018-07-08 - 2018-07-12 %C Ann Arbor, MI, USA %B SIGIR'18 %P 405 - 414 %I ACM %@ 978-1-4503-5022-8
[8]
A. J. Biega, K. P. Gummadi, and G. Weikum, “Equity of Attention: Amortizing Individual Fairness in Rankings,” 2018. [Online]. Available: http://arxiv.org/abs/1805.01788. (arXiv: 1805.01788)
Abstract
Rankings of people and items are at the heart of selection-making, match-making, and recommender systems, ranging from employment sites to sharing economy platforms. As ranking positions influence the amount of attention the ranked subjects receive, biases in rankings can lead to unfair distribution of opportunities and resources, such as jobs or income. This paper proposes new measures and mechanisms to quantify and mitigate unfairness from a bias inherent to all rankings, namely, the position bias, which leads to disproportionately less attention being paid to low-ranked subjects. Our approach differs from recent fair ranking approaches in two important ways. First, existing works measure unfairness at the level of subject groups while our measures capture unfairness at the level of individual subjects, and as such subsume group unfairness. Second, as no single ranking can achieve individual attention fairness, we propose a novel mechanism that achieves amortized fairness, where attention accumulated across a series of rankings is proportional to accumulated relevance. We formulate the challenge of achieving amortized individual fairness subject to constraints on ranking quality as an online optimization problem and show that it can be solved as an integer linear program. Our experimental evaluation reveals that unfair attention distribution in rankings can be substantial, and demonstrates that our method can improve individual fairness while retaining high ranking quality.
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@online{Biega_arXiv1805.01788, TITLE = {Equity of Attention: Amortizing Individual Fairness in Rankings}, AUTHOR = {Biega, Asia J. and Gummadi, Krishna P. and Weikum, Gerhard}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1805.01788}, EPRINT = {1805.01788}, EPRINTTYPE = {arXiv}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Rankings of people and items are at the heart of selection-making, match-making, and recommender systems, ranging from employment sites to sharing economy platforms. As ranking positions influence the amount of attention the ranked subjects receive, biases in rankings can lead to unfair distribution of opportunities and resources, such as jobs or income. This paper proposes new measures and mechanisms to quantify and mitigate unfairness from a bias inherent to all rankings, namely, the position bias, which leads to disproportionately less attention being paid to low-ranked subjects. Our approach differs from recent fair ranking approaches in two important ways. First, existing works measure unfairness at the level of subject groups while our measures capture unfairness at the level of individual subjects, and as such subsume group unfairness. Second, as no single ranking can achieve individual attention fairness, we propose a novel mechanism that achieves amortized fairness, where attention accumulated across a series of rankings is proportional to accumulated relevance. We formulate the challenge of achieving amortized individual fairness subject to constraints on ranking quality as an online optimization problem and show that it can be solved as an integer linear program. Our experimental evaluation reveals that unfair attention distribution in rankings can be substantial, and demonstrates that our method can improve individual fairness while retaining high ranking quality.}, }
Endnote
%0 Report %A Biega, Asia J. %A Gummadi, Krishna P. %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Equity of Attention: Amortizing Individual Fairness in Rankings : %G eng %U http://hdl.handle.net/21.11116/0000-0002-1563-7 %U http://arxiv.org/abs/1805.01788 %D 2018 %X Rankings of people and items are at the heart of selection-making, match-making, and recommender systems, ranging from employment sites to sharing economy platforms. As ranking positions influence the amount of attention the ranked subjects receive, biases in rankings can lead to unfair distribution of opportunities and resources, such as jobs or income. This paper proposes new measures and mechanisms to quantify and mitigate unfairness from a bias inherent to all rankings, namely, the position bias, which leads to disproportionately less attention being paid to low-ranked subjects. Our approach differs from recent fair ranking approaches in two important ways. First, existing works measure unfairness at the level of subject groups while our measures capture unfairness at the level of individual subjects, and as such subsume group unfairness. Second, as no single ranking can achieve individual attention fairness, we propose a novel mechanism that achieves amortized fairness, where attention accumulated across a series of rankings is proportional to accumulated relevance. We formulate the challenge of achieving amortized individual fairness subject to constraints on ranking quality as an online optimization problem and show that it can be solved as an integer linear program. Our experimental evaluation reveals that unfair attention distribution in rankings can be substantial, and demonstrates that our method can improve individual fairness while retaining high ranking quality. %K Computer Science, Information Retrieval, cs.IR,Computer Science, Computers and Society, cs.CY
[9]
N. Boldyrev, M. Spaniol, and G. Weikum, “Multi-Cultural Interlinking of Web Taxonomies with ACROSS,” The Journal of Web Science, vol. 4, no. 2, 2018.
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@article{Boldyrev2018, TITLE = {Multi-Cultural Interlinking of Web Taxonomies with {ACROSS}}, AUTHOR = {Boldyrev, Natalia and Spaniol, Marc and Weikum, Gerhard}, LANGUAGE = {eng}, DOI = {10.1561/106.00000012}, PUBLISHER = {Now Publishers}, ADDRESS = {Boston}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, JOURNAL = {The Journal of Web Science}, VOLUME = {4}, NUMBER = {2}, PAGES = {20--33}, }
Endnote
%0 Journal Article %A Boldyrev, Natalia %A Spaniol, Marc %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Multi-Cultural Interlinking of Web Taxonomies with ACROSS : %G eng %U http://hdl.handle.net/21.11116/0000-0001-3CA4-3 %R 10.1561/106.00000012 %7 2018 %D 2018 %J The Journal of Web Science %O Web Science %V 4 %N 2 %& 20 %P 20 - 33 %I Now Publishers %C Boston
[10]
K. Budhathoki and J. Vreeken, “Origo: Causal Inference by Compression,” Knowledge and Information Systems, vol. 56, no. 2, 2018.
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@article{Budhathoki2018, TITLE = {Origo: {C}ausal Inference by Compression}, AUTHOR = {Budhathoki, Kailash and Vreeken, Jilles}, LANGUAGE = {eng}, ISSN = {0219-1377}, DOI = {10.1007/s10115-017-1130-5}, PUBLISHER = {Springer}, ADDRESS = {New York, NY}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, JOURNAL = {Knowledge and Information Systems}, VOLUME = {56}, NUMBER = {2}, PAGES = {285--307}, }
Endnote
%0 Journal Article %A Budhathoki, Kailash %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Origo: Causal Inference by Compression : %G eng %U http://hdl.handle.net/21.11116/0000-0001-AF2B-B %R 10.1007/s10115-017-1130-5 %7 2018 %D 2018 %J Knowledge and Information Systems %V 56 %N 2 %& 285 %P 285 - 307 %I Springer %C New York, NY %@ false
[11]
K. Budhathoki and J. Vreeken, “Causal Inference on Event Sequences,” in Proceedings of the 2018 SIAM International Conference on Data Mining (SDM 2018), San Diego, CA, USA, 2018.
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@inproceedings{budhathoki_SDM2018, TITLE = {Causal Inference on Event Sequences}, AUTHOR = {Budhathoki, Kailash and Vreeken, Jilles}, LANGUAGE = {eng}, ISBN = {978-1-61197-532-1}, DOI = {10.1137/1.9781611975321.7}, PUBLISHER = {SIAM}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {Proceedings of the 2018 SIAM International Conference on Data Mining (SDM 2018)}, EDITOR = {Ester, Martin and Pedreschi, Dino}, PAGES = {55--63}, ADDRESS = {San Diego, CA, USA}, }
Endnote
%0 Conference Proceedings %A Budhathoki, Kailash %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Causal Inference on Event Sequences : %G eng %U http://hdl.handle.net/21.11116/0000-0002-5F34-A %R 10.1137/1.9781611975321.7 %D 2018 %B SIAM International Conference on Data Mining %Z date of event: 2018-05-03 - 2018-05-05 %C San Diego, CA, USA %B Proceedings of the 2018 SIAM International Conference on Data Mining %E Ester, Martin; Pedreschi, Dino %P 55 - 63 %I SIAM %@ 978-1-61197-532-1
[12]
K. Budhathoki and J. Vreeken, “Accurate Causal Inference on Discrete Data,” in IEEE International Conference on Data Mining (ICDM 2018), Singapore, Singapore. (Accepted/in press)
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@inproceedings{budhathoki:18:acid, TITLE = {Accurate Causal Inference on Discrete Data}, AUTHOR = {Budhathoki, Kailash and Vreeken, Jilles}, LANGUAGE = {eng}, PUBLISHER = {IEEE}, YEAR = {2018}, PUBLREMARK = {Accepted}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {IEEE International Conference on Data Mining (ICDM 2018)}, ADDRESS = {Singapore, Singapore}, }
Endnote
%0 Conference Proceedings %A Budhathoki, Kailash %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Accurate Causal Inference on Discrete Data : %G eng %U http://hdl.handle.net/21.11116/0000-0002-9E96-3 %D 2018 %B IEEE International Conference on Data Mining %Z date of event: 2018-11-17 - 2018-11-20 %C Singapore, Singapore %B IEEE International Conference on Data Mining %I IEEE
[13]
K. Budhathoki, M. Boley, and J. Vreeken, “Rule Discovery for Exploratory Causal Reasoning,” in Proceedings of the NeurIPS 2018 workshop on Causal Learning (NeurIPS CL 2018), Montréal, Canada, 2018.
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@inproceedings{budhathoki:18:dice, TITLE = {Rule Discovery for Exploratory Causal Reasoning}, AUTHOR = {Budhathoki, Kailash and Boley, Mario and Vreeken, Jilles}, LANGUAGE = {eng}, URL = {https://drive.google.com/file/d/1r-KTsok3VLIz-wUh0YtsK5YaEu53DcTf/view}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of the NeurIPS 2018 workshop on Causal Learning (NeurIPS CL 2018)}, EID = {14}, ADDRESS = {Montr{\'e}al, Canada}, }
Endnote
%0 Conference Proceedings %A Budhathoki, Kailash %A Boley, Mario %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Rule Discovery for Exploratory Causal Reasoning : %G eng %U http://hdl.handle.net/21.11116/0000-0002-9EBC-9 %U https://drive.google.com/file/d/1r-KTsok3VLIz-wUh0YtsK5YaEu53DcTf/view %D 2018 %B NeurIPS 2018 Workshop on Causal Learning %Z date of event: 2018-12-07 - 2018-12-07 %C Montréal, Canada %B Proceedings of the NeurIPS 2018 workshop on Causal Learning %Z sequence number: 14
[14]
A. Cohan, B. Desmet, A. Yates, L. Soldaini, S. MacAvaney, and N. Goharian, “SMHD: a Large-Scale Resource for Exploring Online Language Usage for Multiple Mental Health Conditions,” in The 27th International Conference on Computational Linguistics (COLING 2018), Santa Fe, NM, USA, 2018.
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@inproceedings{Cohan_COLING2018, TITLE = {{SMHD}: {A} Large-Scale Resource for Exploring Online Language Usage for Multiple Mental Health Conditions}, AUTHOR = {Cohan, Arman and Desmet, Bart and Yates, Andrew and Soldaini, Luca and MacAvaney, Sean and Goharian, Nazli}, LANGUAGE = {eng}, ISBN = {978-1-948087-50-6}, URL = {http://aclweb.org/anthology/C18-1126}, PUBLISHER = {ACL}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {The 27th International Conference on Computational Linguistics (COLING 2018)}, EDITOR = {Bender, Emily M. and Derczynski, Leon and Isabelle, Pierre}, PAGES = {1485--1497}, ADDRESS = {Santa Fe, NM, USA}, }
Endnote
%0 Conference Proceedings %A Cohan, Arman %A Desmet, Bart %A Yates, Andrew %A Soldaini, Luca %A MacAvaney, Sean %A Goharian, Nazli %+ External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations %T SMHD: a Large-Scale Resource for Exploring Online Language Usage for Multiple Mental Health Conditions : %G eng %U http://hdl.handle.net/21.11116/0000-0002-5E91-1 %U http://aclweb.org/anthology/C18-1126 %D 2018 %B 27th International Conference on Computational Linguistics %Z date of event: 2018-08-20 - 2018-08-26 %C Santa Fe, NM, USA %B The 27th International Conference on Computational Linguistics %E Bender, Emily M.; Derczynski, Leon; Isabelle, Pierre %P 1485 - 1497 %I ACL %@ 978-1-948087-50-6
[15]
A. Cohan, B. Desmet, A. Yates, L. Soldaini, S. MacAvaney, and N. Goharian, “SMHD: A Large-Scale Resource for Exploring Online Language Usage for Multiple Mental Health Conditions,” 2018. [Online]. Available: http://arxiv.org/abs/1806.05258. (arXiv: 1806.05258)
Abstract
Mental health is a significant and growing public health concern. As language usage can be leveraged to obtain crucial insights into mental health conditions, there is a need for large-scale, labeled, mental health-related datasets of users who have been diagnosed with one or more of such conditions. In this paper, we investigate the creation of high-precision patterns to identify self-reported diagnoses of nine different mental health conditions, and obtain high-quality labeled data without the need for manual labelling. We introduce the SMHD (Self-reported Mental Health Diagnoses) dataset and make it available. SMHD is a novel large dataset of social media posts from users with one or multiple mental health conditions along with matched control users. We examine distinctions in users' language, as measured by linguistic and psychological variables. We further explore text classification methods to identify individuals with mental conditions through their language.
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@online{cohan_arXiv1806.05258, TITLE = {{SMHD}: A Large-Scale Resource for Exploring Online Language Usage for Multiple Mental Health Conditions}, AUTHOR = {Cohan, Arman and Desmet, Bart and Yates, Andrew and Soldaini, Luca and MacAvaney, Sean and Goharian, Nazli}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1806.05258}, EPRINT = {1806.05258}, EPRINTTYPE = {arXiv}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Mental health is a significant and growing public health concern. As language usage can be leveraged to obtain crucial insights into mental health conditions, there is a need for large-scale, labeled, mental health-related datasets of users who have been diagnosed with one or more of such conditions. In this paper, we investigate the creation of high-precision patterns to identify self-reported diagnoses of nine different mental health conditions, and obtain high-quality labeled data without the need for manual labelling. We introduce the SMHD (Self-reported Mental Health Diagnoses) dataset and make it available. SMHD is a novel large dataset of social media posts from users with one or multiple mental health conditions along with matched control users. We examine distinctions in users' language, as measured by linguistic and psychological variables. We further explore text classification methods to identify individuals with mental conditions through their language.}, }
Endnote
%0 Report %A Cohan, Arman %A Desmet, Bart %A Yates, Andrew %A Soldaini, Luca %A MacAvaney, Sean %A Goharian, Nazli %+ External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations %T SMHD: A Large-Scale Resource for Exploring Online Language Usage for Multiple Mental Health Conditions : %G eng %U http://hdl.handle.net/21.11116/0000-0002-5ED4-6 %U http://arxiv.org/abs/1806.05258 %D 2018 %X Mental health is a significant and growing public health concern. As language usage can be leveraged to obtain crucial insights into mental health conditions, there is a need for large-scale, labeled, mental health-related datasets of users who have been diagnosed with one or more of such conditions. In this paper, we investigate the creation of high-precision patterns to identify self-reported diagnoses of nine different mental health conditions, and obtain high-quality labeled data without the need for manual labelling. We introduce the SMHD (Self-reported Mental Health Diagnoses) dataset and make it available. SMHD is a novel large dataset of social media posts from users with one or multiple mental health conditions along with matched control users. We examine distinctions in users' language, as measured by linguistic and psychological variables. We further explore text classification methods to identify individuals with mental conditions through their language. %K Computer Science, Computation and Language, cs.CL
[16]
M. Danisch, O. Balalau, and M. Sozio, “Listing k-cliques in Sparse Real-World Graphs,” in Companion of the World Wide Web Conference (WWW 2018), Lyon, France, 2018.
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@inproceedings{Danisch_WWW2018, TITLE = {Listing k-cliques in Sparse Real-World Graphs}, AUTHOR = {Danisch, Maximilien and Balalau, Oana and Sozio, Mauro}, LANGUAGE = {eng}, ISBN = {978-1-4503-5640-4}, DOI = {10.1145/3178876.3186125}, PUBLISHER = {ACM}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {Companion of the World Wide Web Conference (WWW 2018)}, EDITOR = {Champin, Pierre-Antoine and Gandon, Fabien and M{\'e}dini, Lionel}, PAGES = {589--598}, ADDRESS = {Lyon, France}, }
Endnote
%0 Conference Proceedings %A Danisch, Maximilien %A Balalau, Oana %A Sozio, Mauro %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Listing k-cliques in Sparse Real-World Graphs : %G eng %U http://hdl.handle.net/21.11116/0000-0002-9CDE-5 %R 10.1145/3178876.3186125 %D 2018 %B The Web Conference %Z date of event: 2018-04-23 - 2018-04-27 %C Lyon, France %B Companion of the World Wide Web Conference %E Champin, Pierre-Antoine; Gandon, Fabien; Médini, Lionel %P 589 - 598 %I ACM %@ 978-1-4503-5640-4
[17]
F. Darari, W. Nutt, and S. Razniewski, “Comparing Index Structures for Completeness Reasoning,” in IWBIS 2018, International Workshop on Big Data and Information Security, Jakarta, Indonesia, 2018.
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@inproceedings{DarariIWBIS2018, TITLE = {Comparing Index Structures for Completeness Reasoning}, AUTHOR = {Darari, Fariz and Nutt, Werner and Razniewski, Simon}, LANGUAGE = {eng}, ISBN = {978-1-5386-5525-2}, DOI = {10.1109/IWBIS.2018.8471712}, PUBLISHER = {IEEE}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {IWBIS 2018, International Workshop on Big Data and Information Security}, PAGES = {49--56}, ADDRESS = {Jakarta, Indonesia}, }
Endnote
%0 Conference Proceedings %A Darari, Fariz %A Nutt, Werner %A Razniewski, Simon %+ External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Comparing Index Structures for Completeness Reasoning : %G eng %U http://hdl.handle.net/21.11116/0000-0001-E193-A %R 10.1109/IWBIS.2018.8471712 %D 2018 %B International Workshop on Big Data and Information Security %Z date of event: 2018-05-12 - 2018-05-13 %C Jakarta, Indonesia %B IWBIS 2018 %P 49 - 56 %I IEEE %@ 978-1-5386-5525-2
[18]
F. Darari, W. Nutt, G. Pirrò, and S. Razniewski, “Completeness Management for RDF Data Sources,” ACM Transactions on the Web, vol. 12, no. 3, 2018.
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@article{Darari2018, TITLE = {Completeness Management for {RDF} Data Sources}, AUTHOR = {Darari, Fariz and Nutt, Werner and Pirr{\`o}, Giuseppe and Razniewski, Simon}, LANGUAGE = {eng}, DOI = {10.1145/3196248}, PUBLISHER = {ACM}, ADDRESS = {New York, NY}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, JOURNAL = {ACM Transactions on the Web}, VOLUME = {12}, NUMBER = {3}, EID = {18}, }
Endnote
%0 Journal Article %A Darari, Fariz %A Nutt, Werner %A Pirrò, Giuseppe %A Razniewski, Simon %+ External Organizations External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Completeness Management for RDF Data Sources : %G eng %U http://hdl.handle.net/21.11116/0000-0001-E17F-3 %R 10.1145/3196248 %7 2018 %D 2018 %J ACM Transactions on the Web %V 12 %N 3 %Z sequence number: 18 %I ACM %C New York, NY
[19]
S. Degaetano-Ortlieb and J. Strötgen, “Diachronic Variation of Temporal Expressions in Scientific Writing through the Lens of Relative Entropy,” in Language Technologies for the Challenges of the Digital Age (GSCL 2017), Berlin, Germany, 2018.
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@inproceedings{DegaetanoortliebStroetgen2017, TITLE = {Diachronic Variation of Temporal Expressions in Scientific Writing through the Lens of Relative Entropy}, AUTHOR = {Degaetano-Ortlieb, Stefania and Str{\"o}tgen, Jannik}, LANGUAGE = {eng}, ISBN = {978-3-319-73705-8}, DOI = {10.1007/978-3-319-73706-5_22}, PUBLISHER = {Springer}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {Language Technologies for the Challenges of the Digital Age (GSCL 2017)}, EDITOR = {Rehm, Georg and Declerck, Thierry}, PAGES = {259--275}, SERIES = {Lecture Notes in Artificial Intelligence}, VOLUME = {10713}, ADDRESS = {Berlin, Germany}, }
Endnote
%0 Conference Proceedings %A Degaetano-Ortlieb, Stefania %A Strötgen, Jannik %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Diachronic Variation of Temporal Expressions in Scientific Writing through the Lens of Relative Entropy : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-A8E8-5 %R 10.1007/978-3-319-73706-5_22 %D 2018 %B Conference of the German Society for Computational Linguistics and Language Technology %Z date of event: 2017-09-13 - 2017-09-14 %C Berlin, Germany %B Language Technologies for the Challenges of the Digital Age %E Rehm, Georg; Declerck, Thierry %P 259 - 275 %I Springer %@ 978-3-319-73705-8 %B Lecture Notes in Artificial Intelligence %N 10713
[20]
P. Ernst, “Biomedical Knowledge Base Construction from Text and its Applications in Knowledge-based Systems,” Universität des Saarlandes, Saarbrücken, 2018.
Abstract
While general-purpose Knowledge Bases (KBs) have gone a long way in compiling comprehensive knowledgee about people, events, places, etc., domain-specific KBs, such as on health, are equally important, but are less explored. Consequently, a comprehensive and expressive health KB that spans all aspects of biomedical knowledge is still missing. The main goal of this thesis is to develop principled methods for building such a KB and enabling knowledge-centric applications. We address several challenges and make the following contributions: - To construct a health KB, we devise a largely automated and scalable pattern-based knowledge extraction method covering a spectrum of different text genres and distilling a wide variety of facts from different biomedical areas. - To consider higher-arity relations, crucial for proper knowledge representation in advanced domain such as health, we generalize the fact-pattern duality paradigm of previous methods. A key novelty is the integration of facts with missing arguments by extending our framework to partial patterns and facts by reasoning over the composability of partial facts. - To demonstrate the benefits of a health KB, we devise systems for entity-aware search and analytics and for entity-relationship-oriented exploration. Extensive experiments and use-case studies demonstrate the viability of the proposed approaches.
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@phdthesis{Ernstphd2017, TITLE = {Biomedical Knowledge Base Construction from Text and its Applications in Knowledge-based Systems}, AUTHOR = {Ernst, Patrick}, LANGUAGE = {eng}, URL = {urn:nbn:de:bsz:291-scidok-ds-271051}, DOI = {10.22028/D291-27105}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, ABSTRACT = {While general-purpose Knowledge Bases (KBs) have gone a long way in compiling comprehensive knowledgee about people, events, places, etc., domain-specific KBs, such as on health, are equally important, but are less explored. Consequently, a comprehensive and expressive health KB that spans all aspects of biomedical knowledge is still missing. The main goal of this thesis is to develop principled methods for building such a KB and enabling knowledge-centric applications. We address several challenges and make the following contributions: -- To construct a health KB, we devise a largely automated and scalable pattern-based knowledge extraction method covering a spectrum of different text genres and distilling a wide variety of facts from different biomedical areas. -- To consider higher-arity relations, crucial for proper knowledge representation in advanced domain such as health, we generalize the fact-pattern duality paradigm of previous methods. A key novelty is the integration of facts with missing arguments by extending our framework to partial patterns and facts by reasoning over the composability of partial facts. -- To demonstrate the benefits of a health KB, we devise systems for entity-aware search and analytics and for entity-relationship-oriented exploration. Extensive experiments and use-case studies demonstrate the viability of the proposed approaches.}, }
Endnote
%0 Thesis %A Ernst, Patrick %Y Weikum, Gerhard %A referee: Verspoor, Karin %A referee: Berberich, Klaus %+ Databases and Information Systems, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Biomedical Knowledge Base Construction from Text and its Applications in Knowledge-based Systems : %G eng %U http://hdl.handle.net/21.11116/0000-0001-1864-4 %U urn:nbn:de:bsz:291-scidok-ds-271051 %R 10.22028/D291-27105 %I Universität des Saarlandes %C Saarbrücken %D 2018 %8 20.02.2018 %P 147 p. %V phd %9 phd %X While general-purpose Knowledge Bases (KBs) have gone a long way in compiling comprehensive knowledgee about people, events, places, etc., domain-specific KBs, such as on health, are equally important, but are less explored. Consequently, a comprehensive and expressive health KB that spans all aspects of biomedical knowledge is still missing. The main goal of this thesis is to develop principled methods for building such a KB and enabling knowledge-centric applications. We address several challenges and make the following contributions: - To construct a health KB, we devise a largely automated and scalable pattern-based knowledge extraction method covering a spectrum of different text genres and distilling a wide variety of facts from different biomedical areas. - To consider higher-arity relations, crucial for proper knowledge representation in advanced domain such as health, we generalize the fact-pattern duality paradigm of previous methods. A key novelty is the integration of facts with missing arguments by extending our framework to partial patterns and facts by reasoning over the composability of partial facts. - To demonstrate the benefits of a health KB, we devise systems for entity-aware search and analytics and for entity-relationship-oriented exploration. Extensive experiments and use-case studies demonstrate the viability of the proposed approaches. %U https://publikationen.sulb.uni-saarland.de/handle/20.500.11880/26987
[21]
P. Ernst, A. Siu, and G. Weikum, “HighLife: Higher-arity Fact Harvesting,” in Proceedings of the World Wide Web Conference (WWW 2018), Lyon, France, 2018.
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@inproceedings{ErnstlWWW_2018, TITLE = {{HighLife}: Higher-arity Fact Harvesting}, AUTHOR = {Ernst, Patrick and Siu, Amy and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-5639-8}, DOI = {10.1145/3178876.3186000}, PUBLISHER = {ACM}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {Proceedings of the World Wide Web Conference (WWW 2018)}, EDITOR = {Champin, Pierre-Antoine and Gandon, Fabien and M{\'e}dini, Lionel and Lalmas, Mounia and Ipeirotis, Panagiotis G.}, PAGES = {1013--1022}, ADDRESS = {Lyon, France}, }
Endnote
%0 Conference Proceedings %A Ernst, Patrick %A Siu, Amy %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T HighLife: Higher-arity Fact Harvesting : %G eng %U http://hdl.handle.net/21.11116/0000-0001-3C96-3 %R 10.1145/3178876.3186000 %D 2018 %B The Web Conference %Z date of event: 2018-04-23 - 2018-04-27 %C Lyon, France %B Proceedings of the World Wide Web Conference %E Champin, Pierre-Antoine; Gandon, Fabien; Médini, Lionel; Lalmas, Mounia; Ipeirotis, Panagiotis G. %P 1013 - 1022 %I ACM %@ 978-1-4503-5639-8
[22]
A. K. Fischer, J. Vreeken, and D. Klakov, “Beyond Pairwise Similarity: Quantifying and Characterizing Linguistic Similarity between Groups of Languages by MDL,” Computación y Sistemas, vol. 21, no. 4, 2018.
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@article{Fischer2018, TITLE = {Beyond Pairwise Similarity: Quantifying and Characterizing Linguistic Similarity between Groups of Languages by {MDL}}, AUTHOR = {Fischer, Andrea K. and Vreeken, Jilles and Klakov, Dietrich}, LANGUAGE = {eng}, DOI = {10.13053/CyS-21-4-2865}, PUBLISHER = {Instituto Polit{\'e}cnico Nacional}, ADDRESS = {M{\'e}xico}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, JOURNAL = {Computaci{\'o}n y Sistemas}, VOLUME = {21}, NUMBER = {4}, PAGES = {829--839}, }
Endnote
%0 Journal Article %A Fischer, Andrea K. %A Vreeken, Jilles %A Klakov, Dietrich %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Beyond Pairwise Similarity: Quantifying and Characterizing Linguistic Similarity between Groups of Languages by MDL : %G eng %U http://hdl.handle.net/21.11116/0000-0001-4156-5 %R 10.13053/CyS-21-4-2865 %7 2018 %D 2018 %J Computación y Sistemas %V 21 %N 4 %& 829 %P 829 - 839 %I Instituto Politécnico Nacional %C México %U http://www.redalyc.org/articulo.oa?id=61553900023
[23]
E. Galbrun and P. Miettinen, “Mining Redescriptions with Siren,” ACM Transactions on Knowledge Discovery from Data, vol. 12, no. 1, 2018.
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@article{galbrun17mining, TITLE = {Mining Redescriptions with {Siren}}, AUTHOR = {Galbrun, Esther and Miettinen, Pauli}, LANGUAGE = {eng}, DOI = {10.1145/3007212}, PUBLISHER = {ACM}, ADDRESS = {New York, NY}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, JOURNAL = {ACM Transactions on Knowledge Discovery from Data}, VOLUME = {12}, NUMBER = {1}, EID = {6}, }
Endnote
%0 Journal Article %A Galbrun, Esther %A Miettinen, Pauli %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Mining Redescriptions with Siren : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-227B-F %R 10.1145/3007212 %7 2018 %D 2018 %J ACM Transactions on Knowledge Discovery from Data %V 12 %N 1 %Z sequence number: 6 %I ACM %C New York, NY
[24]
E. Gius, N. Reiter, J. Strötgen, and M. Willand, “SANTA: Systematische Analyse Narrativer Texte durch Annotation,” in DHd 2018, 5. Tagung des Verbands Digital Humanities im deutschsprachigen Raum e.V., Köln, Germany, 2018.
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@inproceedings{GiusDHd2018, TITLE = {{{SANTA}: {Systematische Analyse Narrativer Texte durch Annotation}}}, AUTHOR = {Gius, Evelyn and Reiter, Nils and Str{\"o}tgen, Jannik and Willand, Marcus}, LANGUAGE = {deu}, ISBN = {978-3-946275-02-2}, URL = {http://dhd2018.uni-koeln.de/}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {DHd 2018, 5. Tagung des Verbands Digital Humanities im deutschsprachigen Raum e.V.}, PAGES = {302--305}, ADDRESS = {K{\"o}ln, Germany}, }
Endnote
%0 Conference Proceedings %A Gius, Evelyn %A Reiter, Nils %A Strötgen, Jannik %A Willand, Marcus %+ External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T SANTA: Systematische Analyse Narrativer Texte durch Annotation : %G deu %U http://hdl.handle.net/11858/00-001M-0000-002E-73EC-4 %D 2018 %B 5. Tagung des Verbands Digital Humanities im deutschsprachigen Raum e.V. %Z date of event: 2018-02-26 - 2018-03-02 %C Köln, Germany %B DHd 2018 %P 302 - 305 %@ 978-3-946275-02-2
[25]
D. Gupta, K. Berberich, J. Strötgen, and D. Zeinalipour-Yazti, “Generating Semantic Aspects for Queries,” in JCDL’18, Joint Conference on Digital Libraries, Fort Worth, TX, USA, 2018.
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@inproceedings{GuptaJCDL2018, TITLE = {Generating Semantic Aspects for Queries}, AUTHOR = {Gupta, Dhruv and Berberich, Klaus and Str{\"o}tgen, Jannik and Zeinalipour-Yazti, Demetrios}, LANGUAGE = {eng}, ISBN = {978-1-4503-5178-2}, DOI = {10.1145/3197026.3203900}, PUBLISHER = {ACM}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {JCDL'18, Joint Conference on Digital Libraries}, PAGES = {335--336}, ADDRESS = {Fort Worth, TX, USA}, }
Endnote
%0 Conference Proceedings %A Gupta, Dhruv %A Berberich, Klaus %A Strötgen, Jannik %A Zeinalipour-Yazti, Demetrios %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Generating Semantic Aspects for Queries : %G eng %U http://hdl.handle.net/21.11116/0000-0001-904D-6 %R 10.1145/3197026.3203900 %D 2018 %B Joint Conference on Digital Libraries %Z date of event: 2018-06-03 - 2018-06-07 %C Fort Worth, TX, USA %B JCDL'18 %P 335 - 336 %I ACM %@ 978-1-4503-5178-2
[26]
D. Gupta and K. Berberich, “Identifying Time Intervals for Knowledge Graph Facts,” in Companion of the World Wide Web Conference (WWW 2018), Lyon, France, 2018.
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@inproceedings{GuptaWWW2017, TITLE = {Identifying Time Intervals for Knowledge Graph Facts}, AUTHOR = {Gupta, Dhruv and Berberich, Klaus}, LANGUAGE = {eng}, ISBN = {978-1-4503-5640-4}, DOI = {10.1145/3184558.3186917}, PUBLISHER = {ACM}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {Companion of the World Wide Web Conference (WWW 2018)}, EDITOR = {Champin, Pierre-Antoine and Gandon, Fabien and M{\'e}dini, Lionel}, PAGES = {37--38}, ADDRESS = {Lyon, France}, }
Endnote
%0 Conference Proceedings %A Gupta, Dhruv %A Berberich, Klaus %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Identifying Time Intervals for Knowledge Graph Facts : %G eng %U http://hdl.handle.net/21.11116/0000-0001-411F-4 %R 10.1145/3184558.3186917 %D 2018 %B The Web Conference %Z date of event: 2018-04-23 - 2018-04-27 %C Lyon, France %B Companion of the World Wide Web Conference %E Champin, Pierre-Antoine; Gandon, Fabien; Médini, Lionel %P 37 - 38 %I ACM %@ 978-1-4503-5640-4
[27]
D. Gupta and K. Berberich, “GYANI: An Indexing Infrastructure for Knowledge-Centric Tasks,” in CIKM’18, 27th ACM International Conference on Information and Knowledge Management, Torino, Italy, 2018.
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@inproceedings{Gupta_CIKM2018, TITLE = {{GYANI}: {A}n Indexing Infrastructure for Knowledge-Centric Tasks}, AUTHOR = {Gupta, Dhruv and Berberich, Klaus}, LANGUAGE = {eng}, ISBN = {978-1-4503-6014-2}, DOI = {10.1145/3269206.3271745}, PUBLISHER = {ACM}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {CIKM'18, 27th ACM International Conference on Information and Knowledge Management}, EDITOR = {Cuzzocrea, Alfredo and Allan, James and Paton, Norman and Srivastava, Divesh and Agrawal, Rakesh and Broder, Andrei and Zaki, Mohamed and Candan, Selcuk and Labrinidis, Alexandros and Schuster, Assaf and Wang, Haixun}, PAGES = {487--496}, ADDRESS = {Torino, Italy}, }
Endnote
%0 Conference Proceedings %A Gupta, Dhruv %A Berberich, Klaus %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T GYANI: An Indexing Infrastructure for Knowledge-Centric Tasks : %G eng %U http://hdl.handle.net/21.11116/0000-0002-A8B7-2 %R 10.1145/3269206.3271745 %D 2018 %B 27th ACM International Conference on Information and Knowledge Management %Z date of event: 2018-10-22 - 2018-10-26 %C Torino, Italy %B CIKM'18 %E Cuzzocrea, Alfredo; Allan, James; Paton, Norman; Srivastava, Divesh; Agrawal, Rakesh; Broder, Andrei; Zaki, Mohamed; Candan, Selcuk; Labrinidis, Alexandros; Schuster, Assaf; Wang, Haixun %P 487 - 496 %I ACM %@ 978-1-4503-6014-2
[28]
A. Horňáková, M. List, J. Vreeken, and M. H. Schulz, “JAMI: Fast Computation of Conditional Mutual Information for ceRNA Network Analysis,” Bioinformatics, vol. 34, no. 17, 2018.
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@article{Hornakova_Bioinformatics2018, TITLE = {{JAMI}: {F}ast Computation of Conditional Mutual Information for {ceRNA} Network Analysis}, AUTHOR = {Hor{\v n}{\'a}kov{\'a}, Andrea and List, Markus and Vreeken, Jilles and Schulz, Marcel H.}, LANGUAGE = {eng}, ISSN = {1367-4803}, DOI = {10.1093/bioinformatics/bty221}, PUBLISHER = {Oxford University Press}, ADDRESS = {Oxford}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, JOURNAL = {Bioinformatics}, VOLUME = {34}, NUMBER = {17}, PAGES = {3050--3051}, }
Endnote
%0 Journal Article %A Horňáková, Andrea %A List, Markus %A Vreeken, Jilles %A Schulz, Marcel H. %+ Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society %T JAMI: Fast Computation of Conditional Mutual Information for ceRNA Network Analysis : %G eng %U http://hdl.handle.net/21.11116/0000-0002-573A-C %R 10.1093/bioinformatics/bty221 %7 2018 %D 2018 %J Bioinformatics %V 34 %N 17 %& 3050 %P 3050 - 3051 %I Oxford University Press %C Oxford %@ false
[29]
V. T. Ho, D. Stepanova, M. H. Gad-Elrab, E. Kharlamov, and G. Weikum, “Learning Rules from Incomplete KGs using Embeddings,” in ISWC 2018 Posters & Demonstrations, Industry and Blue Sky Ideas Tracks (ISWC-P&D-Industry-BlueSky 2018), Monterey, CA, USA, 2018.
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@inproceedings{StepanovaISWC2018b, TITLE = {Learning Rules from Incomplete {KGs} using Embeddings}, AUTHOR = {Ho, Vinh Thinh and Stepanova, Daria and Gad-Elrab, Mohamed Hassan and Kharlamov, Evgeny and Weikum, Gerhard}, LANGUAGE = {eng}, URL = {http://ceur-ws.org/Vol-2180/paper-25.pdf; urn:nbn:de:0074-2180-3}, PUBLISHER = {ceur.ws.org}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {ISWC 2018 Posters \& Demonstrations, Industry and Blue Sky Ideas Tracks (ISWC-P\&D-Industry-BlueSky 2018)}, EDITOR = {van Erp, Marieke and Atre, Medha and Lopez, Vanessa and Srinivas, Kavitha and Fortuna, Carolina}, EID = {25}, SERIES = {CEUR Workshop Proceedings}, VOLUME = {2180}, ADDRESS = {Monterey, CA, USA}, }
Endnote
%0 Conference Proceedings %A Ho, Vinh Thinh %A Stepanova, Daria %A Gad-Elrab, Mohamed Hassan %A Kharlamov, Evgeny %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Learning Rules from Incomplete KGs using Embeddings : %G eng %U http://hdl.handle.net/21.11116/0000-0001-905B-6 %U http://ceur-ws.org/Vol-2180/paper-25.pdf %D 2018 %B The 17th International Semantic Web Conference %Z date of event: 2018-10-08 - 2018-10-12 %C Monterey, CA, USA %B ISWC 2018 Posters & Demonstrations, Industry and Blue Sky Ideas Tracks %E van Erp, Marieke; Atre, Medha; Lopez, Vanessa; Srinivas, Kavitha; Fortuna, Carolina %Z sequence number: 25 %I ceur.ws.org %B CEUR Workshop Proceedings %N 2180
[30]
V. T. Ho, D. Stepanova, M. H. Gad-Elrab, E. Kharlamov, and G. Weikum, “Rule Learning from Knowledge Graphs Guided by Embedding Models,” in The Semantic Web -- ISWC 2018, Monterey, CA, USA, 2018.
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@inproceedings{StepanovaISWC2018, TITLE = {Rule Learning from Knowledge Graphs Guided by Embedding Models}, AUTHOR = {Ho, Vinh Thinh and Stepanova, Daria and Gad-Elrab, Mohamed Hassan and Kharlamov, Evgeny and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-3-030-00670-9}, DOI = {10.1007/978-3-030-00671-6_5}, PUBLISHER = {Springer}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {The Semantic Web -- ISWC 2018}, EDITOR = {Vrande{\v c}i{\'c}, Denny and Bontcheva, Kalina and Su{\'a}rez-Figueroa, Mari Carmen and Presutti, Valentina and Celino, Irene and Sabou, Marta and Kaffee, Lucie-Aim{\'e}e and Simperl, Elena}, PAGES = {72--90}, SERIES = {Lecture Notes in Computer Science}, VOLUME = {11136}, ADDRESS = {Monterey, CA, USA}, }
Endnote
%0 Conference Proceedings %A Ho, Vinh Thinh %A Stepanova, Daria %A Gad-Elrab, Mohamed Hassan %A Kharlamov, Evgeny %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Rule Learning from Knowledge Graphs Guided by Embedding Models : %G eng %U http://hdl.handle.net/21.11116/0000-0001-9058-9 %R 10.1007/978-3-030-00671-6_5 %D 2018 %B The 17th International Semantic Web Conference %Z date of event: 2018-10-08 - 2018-10-12 %C Monterey, CA, USA %B The Semantic Web -- ISWC 2018 %E Vrandečić, Denny; Bontcheva, Kalina; Suárez-Figueroa, Mari Carmen; Presutti, Valentina; Celino, Irene; Sabou, Marta; Kaffee, Lucie-Aimée; Simperl, Elena %P 72 - 90 %I Springer %@ 978-3-030-00670-9 %B Lecture Notes in Computer Science %N 11136
[31]
V. T. Ho, “An Embedding-based Approach to Rule Learning from Knowledge Graphs,” Universität des Saarlandes, Saarbrücken, 2018.
Abstract
Knowledge Graphs (KGs) play an important role in various information systems and have application in many fields such as Semantic Web Search, Question Answering and Information Retrieval. KGs present information in the form of entities and relationships between them. Modern KGs could contain up to millions of entities and billions of facts, and they are usually built using automatic construction methods. As a result, despite the huge size of KGs, a large number of facts between their entities are still missing. That is the reason why we see the importance of the task of Knowledge Graph Completion (a.k.a. Link Prediction), which concerns the prediction of those missing facts. Rules over a Knowledge Graph capture interpretable patterns in data and various methods for rule learning have been proposed. Since KGs are inherently incomplete, rules can be used to deduce missing facts. Statistical measures for learned rules such as confidence reflect rule quality well when the KG is reasonably complete; however, these measures might be misleading otherwise. So, it is difficult to learn high-quality rules from the KG alone, and scalability dictates that only a small set of candidate rules is generated. Therefore, the ranking and pruning of candidate rules are major problems. To address this issue, we propose a rule learning method that utilizes probabilistic representations of missing facts. In particular, we iteratively extend rules induced from a KG by relying on feedback from a precomputed embedding model over the KG and optionally external information sources including text corpora. The contributions of this thesis are as follows: • We introduce a framework for rule learning guided by external sources. • We propose a concrete instantiation of our framework to show how to learn high- quality rules by utilizing feedback from a pretrained embedding model. • We conducted experiments on real-world KGs that demonstrate the effectiveness of our novel approach with respect to both the quality of the learned rules and fact predictions that they produce.
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@mastersthesis{HoMaster2018, TITLE = {An Embedding-based Approach to Rule Learning from Knowledge Graphs}, AUTHOR = {Ho, Vinh Thinh}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, ABSTRACT = {Knowledge Graphs (KGs) play an important role in various information systems and have application in many {fi}elds such as Semantic Web Search, Question Answering and Information Retrieval. KGs present information in the form of entities and relationships between them. Modern KGs could contain up to millions of entities and billions of facts, and they are usually built using automatic construction methods. As a result, despite the huge size of KGs, a large number of facts between their entities are still missing. That is the reason why we see the importance of the task of Knowledge Graph Completion (a.k.a. Link Prediction), which concerns the prediction of those missing facts. Rules over a Knowledge Graph capture interpretable patterns in data and various methods for rule learning have been proposed. Since KGs are inherently incomplete, rules can be used to deduce missing facts. Statistical measures for learned rules such as con{fi}dence re{fl}ect rule quality well when the KG is reasonably complete; however, these measures might be misleading otherwise. So, it is difficult to learn high-quality rules from the KG alone, and scalability dictates that only a small set of candidate rules is generated. Therefore, the ranking and pruning of candidate rules are major problems. To address this issue, we propose a rule learning method that utilizes probabilistic representations of missing facts. In particular, we iteratively extend rules induced from a KG by relying on feedback from a precomputed embedding model over the KG and optionally external information sources including text corpora. The contributions of this thesis are as follows: \mbox{$\bullet$} We introduce a framework for rule learning guided by external sources. \mbox{$\bullet$} We propose a concrete instantiation of our framework to show how to learn high- quality rules by utilizing feedback from a pretrained embedding model. \mbox{$\bullet$} We conducted experiments on real-world KGs that demonstrate the effectiveness of our novel approach with respect to both the quality of the learned rules and fact predictions that they produce.}, }
Endnote
%0 Thesis %A Ho, Vinh Thinh %A referee: Weikum, Gerhard %Y Stepanova, Daria %+ Databases and Information Systems, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T An Embedding-based Approach to Rule Learning from Knowledge Graphs : %G eng %U http://hdl.handle.net/21.11116/0000-0001-DE06-F %I Universität des Saarlandes %C Saarbrücken %D 2018 %P 60 %V master %9 master %X Knowledge Graphs (KGs) play an important role in various information systems and have application in many fields such as Semantic Web Search, Question Answering and Information Retrieval. KGs present information in the form of entities and relationships between them. Modern KGs could contain up to millions of entities and billions of facts, and they are usually built using automatic construction methods. As a result, despite the huge size of KGs, a large number of facts between their entities are still missing. That is the reason why we see the importance of the task of Knowledge Graph Completion (a.k.a. Link Prediction), which concerns the prediction of those missing facts. Rules over a Knowledge Graph capture interpretable patterns in data and various methods for rule learning have been proposed. Since KGs are inherently incomplete, rules can be used to deduce missing facts. Statistical measures for learned rules such as confidence reflect rule quality well when the KG is reasonably complete; however, these measures might be misleading otherwise. So, it is difficult to learn high-quality rules from the KG alone, and scalability dictates that only a small set of candidate rules is generated. Therefore, the ranking and pruning of candidate rules are major problems. To address this issue, we propose a rule learning method that utilizes probabilistic representations of missing facts. In particular, we iteratively extend rules induced from a KG by relying on feedback from a precomputed embedding model over the KG and optionally external information sources including text corpora. The contributions of this thesis are as follows: • We introduce a framework for rule learning guided by external sources. • We propose a concrete instantiation of our framework to show how to learn high- quality rules by utilizing feedback from a pretrained embedding model. • We conducted experiments on real-world KGs that demonstrate the effectiveness of our novel approach with respect to both the quality of the learned rules and fact predictions that they produce.
[32]
K. Hui, A. Yates, K. Berberich, and G. de Melo, “Co-PACRR: A Context-Aware Neural IR Model for Ad-hoc Retrieval,” in WSDM’18, 11th ACM International Conference on Web Search and Data Mining, Marina Del Rey, CA, USA, 2018.
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@inproceedings{Hui_WSDM2018, TITLE = {Co-{PACRR}: {A} Context-Aware Neural {IR} Model for Ad-hoc Retrieval}, AUTHOR = {Hui, Kai and Yates, Andrew and Berberich, Klaus and de Melo, Gerard}, LANGUAGE = {eng}, ISBN = {978-1-4503-5581-0}, DOI = {10.1145/3159652.3159689}, PUBLISHER = {ACM}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {WSDM'18, 11th ACM International Conference on Web Search and Data Mining}, PAGES = {279--287}, ADDRESS = {Marina Del Rey, CA, USA}, }
Endnote
%0 Conference Proceedings %A Hui, Kai %A Yates, Andrew %A Berberich, Klaus %A de Melo, Gerard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Co-PACRR: A Context-Aware Neural IR Model for Ad-hoc Retrieval : %G eng %U http://hdl.handle.net/21.11116/0000-0000-6367-D %R 10.1145/3159652.3159689 %D 2018 %B 11th ACM International Conference on Web Search and Data Mining %Z date of event: 2018-02-05 - 2018-02-09 %C Marina Del Rey, CA, USA %B WSDM'18 %P 279 - 287 %I ACM %@ 978-1-4503-5581-0
[33]
H. Jhavar and P. Mirza, “EMOFIEL: Mapping Emotions of Relationships in a Story,” in Companion of the World Wide Web Conference (WWW 2018), Lyon, France, 2018.
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@inproceedings{JhavarWWW2018, TITLE = {{EMOFIEL}: {M}apping Emotions of Relationships in a Story}, AUTHOR = {Jhavar, Harshita and Mirza, Paramita}, LANGUAGE = {eng}, ISBN = {978-1-4503-5640-4}, DOI = {10.1145/3184558.3186989}, PUBLISHER = {ACM}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {Companion of the World Wide Web Conference (WWW 2018)}, EDITOR = {Champin, Pierre-Antoine and Gandon, Fabien and M{\'e}dini, Lionel}, PAGES = {243--246}, ADDRESS = {Lyon, France}, }
Endnote
%0 Conference Proceedings %A Jhavar, Harshita %A Mirza, Paramita %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T EMOFIEL: Mapping Emotions of Relationships in a Story : %G eng %U http://hdl.handle.net/21.11116/0000-0001-4B96-2 %R 10.1145/3184558.3186989 %D 2018 %B The Web Conference %Z date of event: 2018-04-23 - 2018-04-27 %C Lyon, France %B Companion of the World Wide Web Conference %E Champin, Pierre-Antoine; Gandon, Fabien; Médini, Lionel %P 243 - 246 %I ACM %@ 978-1-4503-5640-4
[34]
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}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {Companion of the World Wide Web Conference (WWW 2018)}, EDITOR = {Champin, Pierre-Antoine and Gandon, Fabien and M{\'e}dini, Lionel}, PAGES = {1057--1062}, ADDRESS = {Lyon, France}, }
Endnote
%0 Conference Proceedings %A Jia, Zhen %A Abujabal, Abdalghani %A 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
[35]
Z. Jia, A. Abujabal, R. S. Roy, J. Strötgen, and G. Weikum, “TEQUILA: Temporal Question Answering over Knowledge Bases,” in CIKM’18, 27th ACM International Conference on Information and Knowledge Management, Torino, Italy, 2018.
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@inproceedings{Jia_CIKM2018, TITLE = {{TEQUILA}: {T}emporal Question Answering over Knowledge Bases}, AUTHOR = {Jia, Zhen and Abujabal, Abdalghani and Roy, Rishiraj Saha and Str{\"o}tgen, Jannik and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-6014-2}, DOI = {10.1145/3269206.3269247}, PUBLISHER = {ACM}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {CIKM'18, 27th ACM International Conference on Information and Knowledge Management}, EDITOR = {Cuzzocrea, Alfredo and Allan, James and Paton, Norman and Srivastava, Divesh and Agrawal, Rakesh and Broder, Andrei and Zaki, Mohamed and Candan, Selcuk and Labrinidis, Alexandros and Schuster, Assaf and Wang, Haixun}, PAGES = {1807--1810}, ADDRESS = {Torino, Italy}, }
Endnote
%0 Conference Proceedings %A Jia, Zhen %A Abujabal, Abdalghani %A 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 External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T TEQUILA: Temporal Question Answering over Knowledge Bases : %G eng %U http://hdl.handle.net/21.11116/0000-0002-A106-1 %R 10.1145/3269206.3269247 %D 2018 %B 27th ACM International Conference on Information and Knowledge Management %Z date of event: 2018-10-22 - 2018-10-26 %C Torino, Italy %B CIKM'18 %E Cuzzocrea, Alfredo; Allan, James; Paton, Norman; Srivastava, Divesh; Agrawal, Rakesh; Broder, Andrei; Zaki, Mohamed; Candan, Selcuk; Labrinidis, Alexandros; Schuster, Assaf; Wang, Haixun %P 1807 - 1810 %I ACM %@ 978-1-4503-6014-2
[36]
J. Kalofolias, E. Galbrun, and P. Miettinen, “From Sets of Good Redescriptions to Good Sets of Redescriptions,” Knowledge and Information Systems, vol. 57, no. 1, 2018.
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@article{kalofolias18from, TITLE = {From Sets of Good Redescriptions to Good Sets of Redescriptions}, AUTHOR = {Kalofolias, Janis and Galbrun, Esther and Miettinen, Pauli}, LANGUAGE = {eng}, ISSN = {0219-1377}, DOI = {10.1007/s10115-017-1149-7}, PUBLISHER = {Springer}, ADDRESS = {New York, NY}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, JOURNAL = {Knowledge and Information Systems}, VOLUME = {57}, NUMBER = {1}, PAGES = {21--54}, }
Endnote
%0 Journal Article %A Kalofolias, Janis %A Galbrun, Esther %A Miettinen, Pauli %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T From Sets of Good Redescriptions to Good Sets of Redescriptions : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002E-90D1-5 %R 10.1007/s10115-017-1149-7 %7 2018-01-19 %D 2018 %J Knowledge and Information Systems %V 57 %N 1 %& 21 %P 21 - 54 %I Springer %C New York, NY %@ false
[37]
S. Karaev, J. Hook, and P. Miettinen, “Latitude: A Model for Mixed Linear-Tropical Matrix Factorization,” 2018. [Online]. Available: http://arxiv.org/abs/1801.06136. (arXiv: 1801.06136)
Abstract
Nonnegative matrix factorization (NMF) is one of the most frequently-used matrix factorization models in data analysis. A significant reason to the popularity of NMF is its interpretability and the `parts of whole' interpretation of its components. Recently, max-times, or subtropical, matrix factorization (SMF) has been introduced as an alternative model with equally interpretable `winner takes it all' interpretation. In this paper we propose a new mixed linear--tropical model, and a new algorithm, called Latitude, that combines NMF and SMF, being able to smoothly alternate between the two. In our model, the data is modeled using the latent factors and latent parameters that control whether the factors are interpreted as NMF or SMF features, or their mixtures. We present an algorithm for our novel matrix factorization. Our experiments show that our algorithm improves over both baselines, and can yield interpretable results that reveal more of the latent structure than either NMF or SMF alone.
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@online{Karaev2018, TITLE = {Latitude: A Model for Mixed Linear-Tropical Matrix Factorization}, AUTHOR = {Karaev, Sanjar and Hook, James and Miettinen, Pauli}, URL = {http://arxiv.org/abs/1801.06136}, EPRINT = {1801.06136}, EPRINTTYPE = {arXiv}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Nonnegative matrix factorization (NMF) is one of the most frequently-used matrix factorization models in data analysis. A significant reason to the popularity of NMF is its interpretability and the `parts of whole' interpretation of its components. Recently, max-times, or subtropical, matrix factorization (SMF) has been introduced as an alternative model with equally interpretable `winner takes it all' interpretation. In this paper we propose a new mixed linear--tropical model, and a new algorithm, called Latitude, that combines NMF and SMF, being able to smoothly alternate between the two. In our model, the data is modeled using the latent factors and latent parameters that control whether the factors are interpreted as NMF or SMF features, or their mixtures. We present an algorithm for our novel matrix factorization. Our experiments show that our algorithm improves over both baselines, and can yield interpretable results that reveal more of the latent structure than either NMF or SMF alone.}, }
Endnote
%0 Report %A Karaev, Sanjar %A Hook, James %A Miettinen, Pauli %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Latitude: A Model for Mixed Linear-Tropical Matrix Factorization : %U http://hdl.handle.net/21.11116/0000-0000-636B-9 %U http://arxiv.org/abs/1801.06136 %D 2018 %X Nonnegative matrix factorization (NMF) is one of the most frequently-used matrix factorization models in data analysis. A significant reason to the popularity of NMF is its interpretability and the `parts of whole' interpretation of its components. Recently, max-times, or subtropical, matrix factorization (SMF) has been introduced as an alternative model with equally interpretable `winner takes it all' interpretation. In this paper we propose a new mixed linear--tropical model, and a new algorithm, called Latitude, that combines NMF and SMF, being able to smoothly alternate between the two. In our model, the data is modeled using the latent factors and latent parameters that control whether the factors are interpreted as NMF or SMF features, or their mixtures. We present an algorithm for our novel matrix factorization. Our experiments show that our algorithm improves over both baselines, and can yield interpretable results that reveal more of the latent structure than either NMF or SMF alone. %K Computer Science, Learning, cs.LG
[38]
S. Karaev, J. Hook, and P. Miettinen, “Latitude: A Model for Mixed Linear-Tropical Matrix Factorization,” in Proceedings of the 2018 SIAM International Conference on Data Mining (SDM 2018), San Diego, CA, USA, 2018.
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@inproceedings{Karaev_SDM2018, TITLE = {Latitude: A Model for Mixed Linear-Tropical Matrix Factorization}, AUTHOR = {Karaev, Sanjar and Hook, James and Miettinen, Pauli}, LANGUAGE = {eng}, ISBN = {978-1-61197-532-1}, DOI = {10.1137/1.9781611975321.41}, PUBLISHER = {SIAM}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {Proceedings of the 2018 SIAM International Conference on Data Mining (SDM 2018)}, EDITOR = {Ester, Martin and Pedreschi, Dino}, PAGES = {360--368}, ADDRESS = {San Diego, CA, USA}, }
Endnote
%0 Conference Proceedings %A Karaev, Sanjar %A Hook, James %A Miettinen, Pauli %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Latitude: A Model for Mixed Linear-Tropical Matrix Factorization : %G eng %U http://hdl.handle.net/21.11116/0000-0002-5E2D-4 %R 10.1137/1.9781611975321.41 %D 2018 %B SIAM International Conference on Data Mining %Z date of event: 2018-05-03 - 2018-05-05 %C San Diego, CA, USA %B Proceedings of the 2018 SIAM International Conference on Data Mining %E Ester, Martin; Pedreschi, Dino %P 360 - 368 %I SIAM %@ 978-1-61197-532-1
[39]
S. Karaev and P. Miettinen, “Algorithms for Approximate Subtropical Matrix Factorization,” Data Mining and Knowledge Discovery. (Accepted/in press)
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@article{Karaev_DMKD2018, TITLE = {Algorithms for Approximate Subtropical Matrix Factorization}, AUTHOR = {Karaev, Sanjar and Miettinen, Pauli}, LANGUAGE = {eng}, DOI = {10.1007/s10618-018-0599-1}, PUBLISHER = {Springer}, ADDRESS = {New York, NY}, YEAR = {2018}, PUBLREMARK = {Accepted}, MARGINALMARK = {$\bullet$}, JOURNAL = {Data Mining and Knowledge Discovery}, }
Endnote
%0 Journal Article %A Karaev, Sanjar %A Miettinen, Pauli %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Algorithms for Approximate Subtropical Matrix Factorization : %G eng %U http://hdl.handle.net/21.11116/0000-0002-9FD5-B %R 10.1007/s10618-018-0599-1 %D 2018 %J Data Mining and Knowledge Discovery %O DMKD %I Springer %C New York, NY
[40]
S. Karaev, S. Metzler, and P. Miettinen, “Logistic-Tropical Decompositions and Nested Subgraphs,” in Proceedings of the 14th International Workshop on Mining and Learning with Graphs (MLG 2018), London, UK, 2018.
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@inproceedings{Karaev_MLG2018, TITLE = {Logistic-Tropical Decompositions and Nested Subgraphs}, AUTHOR = {Karaev, Sanjar and Metzler, Saskia and Miettinen, Pauli}, LANGUAGE = {eng}, PUBLISHER = {MLG Workshop}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of the 14th International Workshop on Mining and Learning with Graphs (MLG 2018)}, EID = {35}, ADDRESS = {London, UK}, }
Endnote
%0 Conference Proceedings %A Karaev, Sanjar %A Metzler, Saskia %A Miettinen, Pauli %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Logistic-Tropical Decompositions and Nested Subgraphs : %G eng %U http://hdl.handle.net/21.11116/0000-0002-A91F-E %D 2018 %B 14th International Workshop on Mining and Learning with Graphs %Z date of event: 2018-08-20 - 2018-08-20 %C London, UK %B Proceedings of the 14th International Workshop on Mining and Learning with Graphs %Z sequence number: 35 %I MLG Workshop %U http://www.mlgworkshop.org/2018/papers/MLG2018_paper_35.pdf
[41]
P. Lahoti, G. Weikum, and K. P. Gummadi, “iFair: Learning Individually Fair Data Representations for Algorithmic Decision Making,” 2018. [Online]. Available: http://arxiv.org/abs/1806.01059. (arXiv: 1806.01059)
Abstract
People are rated and ranked, towards algorithmic decision making in an increasing number of applications, typically based on machine learning. Research on how to incorporate fairness into such tasks has prevalently pursued the paradigm of group fairness: ensuring that each ethnic or social group receives its fair share in the outcome of classifiers and rankings. In contrast, the alternative paradigm of individual fairness has received relatively little attention. This paper introduces a method for probabilistically clustering user records into a low-rank representation that captures individual fairness yet also achieves high accuracy in classification and regression models. Our notion of individual fairness requires that users who are similar in all task-relevant attributes such as job qualification, and disregarding all potentially discriminating attributes such as gender, should have similar outcomes. Since the case for fairness is ubiquitous across many tasks, we aim to learn general representations that can be applied to arbitrary downstream use-cases. We demonstrate the versatility of our method by applying it to classification and learning-to-rank tasks on two real-world datasets. Our experiments show substantial improvements over the best prior work for this setting.
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@online{Lahoti_arXiv1806.01059, TITLE = {{iFair}: {L}earning Individually Fair Data Representations for Algorithmic Decision Making}, AUTHOR = {Lahoti, Preethi and Weikum, Gerhard and Gummadi, Krishna P.}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1806.01059}, EPRINT = {1806.01059}, EPRINTTYPE = {arXiv}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, ABSTRACT = {People are rated and ranked, towards algorithmic decision making in an increasing number of applications, typically based on machine learning. Research on how to incorporate fairness into such tasks has prevalently pursued the paradigm of group fairness: ensuring that each ethnic or social group receives its fair share in the outcome of classifiers and rankings. In contrast, the alternative paradigm of individual fairness has received relatively little attention. This paper introduces a method for probabilistically clustering user records into a low-rank representation that captures individual fairness yet also achieves high accuracy in classification and regression models. Our notion of individual fairness requires that users who are similar in all task-relevant attributes such as job qualification, and disregarding all potentially discriminating attributes such as gender, should have similar outcomes. Since the case for fairness is ubiquitous across many tasks, we aim to learn general representations that can be applied to arbitrary downstream use-cases. We demonstrate the versatility of our method by applying it to classification and learning-to-rank tasks on two real-world datasets. Our experiments show substantial improvements over the best prior work for this setting.}, }
Endnote
%0 Report %A Lahoti, Preethi %A Weikum, Gerhard %A Gummadi, Krishna P. %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T iFair: Learning Individually Fair Data Representations for Algorithmic Decision Making : %G eng %U http://hdl.handle.net/21.11116/0000-0002-1545-9 %U http://arxiv.org/abs/1806.01059 %D 2018 %X People are rated and ranked, towards algorithmic decision making in an increasing number of applications, typically based on machine learning. Research on how to incorporate fairness into such tasks has prevalently pursued the paradigm of group fairness: ensuring that each ethnic or social group receives its fair share in the outcome of classifiers and rankings. In contrast, the alternative paradigm of individual fairness has received relatively little attention. This paper introduces a method for probabilistically clustering user records into a low-rank representation that captures individual fairness yet also achieves high accuracy in classification and regression models. Our notion of individual fairness requires that users who are similar in all task-relevant attributes such as job qualification, and disregarding all potentially discriminating attributes such as gender, should have similar outcomes. Since the case for fairness is ubiquitous across many tasks, we aim to learn general representations that can be applied to arbitrary downstream use-cases. We demonstrate the versatility of our method by applying it to classification and learning-to-rank tasks on two real-world datasets. Our experiments show substantial improvements over the best prior work for this setting. %K Computer Science, Learning, cs.LG,Computer Science, Information Retrieval, cs.IR,Statistics, Machine Learning, stat.ML
[42]
P. Lahoti, K. Garimella, and A. Gionis, “Joint Non-negative Matrix Factorization for Learning Ideological Leaning on Twitter,” in WSDM’18, 11th ACM International Conference on Web Search and Data Mining, Marina Del Rey, CA, USA, 2018.
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@inproceedings{Lahoti_WSDM2018, TITLE = {Joint Non-negative Matrix Factorization for Learning Ideological Leaning on {T}witter}, AUTHOR = {Lahoti, Preethi and Garimella, Kiran and Gionis, Aristides}, LANGUAGE = {eng}, ISBN = {978-1-4503-5581-0}, DOI = {10.1145/3159652.3159669}, PUBLISHER = {ACM}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {WSDM'18, 11th ACM International Conference on Web Search and Data Mining}, PAGES = {351--359}, ADDRESS = {Marina Del Rey, CA, USA}, }
Endnote
%0 Conference Proceedings %A Lahoti, Preethi %A Garimella, Kiran %A Gionis, Aristides %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T Joint Non-negative Matrix Factorization for Learning Ideological Leaning on Twitter : %G eng %U http://hdl.handle.net/21.11116/0000-0002-9C4F-7 %R 10.1145/3159652.3159669 %D 2018 %B 11th ACM International Conference on Web Search and Data Mining %Z date of event: 2018-02-05 - 2018-02-09 %C Marina Del Rey, CA, USA %B WSDM'18 %P 351 - 359 %I ACM %@ 978-1-4503-5581-0
[43]
S. MacAvaney, A. Yates, A. Cohan, L. Soldaini, K. Hui, N. Goharian, and O. Frieder, “Overcoming Low-Utility Facets for Complex Answer Retrieval,” in SIGIR 2018 Workshops: ProfS, KG4IR, and DATA:SEARCH (ProfS-KG4IR-Data:Search 2018), Ann Arbor, MI, USA, 2018.
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@inproceedings{MacAvaney_KG4IR2018, TITLE = {Overcoming Low-Utility Facets for Complex Answer Retrieval}, AUTHOR = {MacAvaney, Sean and Yates, Andrew and Cohan, Arman and Soldaini, Luca and Hui, Kai and Goharian, Nazli and Frieder, Ophir}, LANGUAGE = {eng}, URL = {http://ceur-ws.org/Vol-2127/paper1-kg4ir.pdf; urn:nbn:de:0074-2127-8}, PUBLISHER = {ceur.ws.org}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {SIGIR 2018 Workshops: ProfS, KG4IR, and DATA:SEARCH (ProfS-KG4IR-Data:Search 2018)}, EDITOR = {Dietz, Laura and Koetzen, Laura and Verberne, Suzan}, PAGES = {46--47}, SERIES = {CEUR Workshop Proceedings}, VOLUME = {2127}, ADDRESS = {Ann Arbor, MI, USA}, }
Endnote
%0 Conference Proceedings %A MacAvaney, Sean %A Yates, Andrew %A Cohan, Arman %A Soldaini, Luca %A Hui, Kai %A Goharian, Nazli %A Frieder, Ophir %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations External Organizations External Organizations %T Overcoming Low-Utility Facets for Complex Answer Retrieval : %G eng %U http://hdl.handle.net/21.11116/0000-0002-5E9C-6 %U http://ceur-ws.org/Vol-2127/paper1-kg4ir.pdf %D 2018 %B Second Workshop on Knowledge Graphs and Semantics for Text Retrieval, Analysis, and Understanding %Z date of event: 2018-07-12 - 2018-07-12 %C Ann Arbor, MI, USA %B SIGIR 2018 Workshops: ProfS, KG4IR, and DATA:SEARCH %E Dietz, Laura; Koetzen, Laura; Verberne, Suzan %P 46 - 47 %I ceur.ws.org %B CEUR Workshop Proceedings %N 2127 %U http://ceur-ws.org/Vol-2127/paper1-kg4ir.pdf
[44]
S. MacAvaney, B. Desmet, A. Cohan, L. Soldaini, A. Yates, A. Zirikly, and N. Goharian, “RSDD-Time: Temporal Annotation of Self-Reported Mental Health Diagnoses,” in Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2018), New Orleans, LA, USA, 2018.
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@inproceedings{MacAvaney_NAACL_HLT2018, TITLE = {{RSDD}-Time: {T}emporal Annotation of Self-Reported Mental Health Diagnoses}, AUTHOR = {MacAvaney, Sean and Desmet, Bart and Cohan, Arman and Soldaini, Luca and Yates, Andrew and Zirikly, Ayah and Goharian, Nazli}, LANGUAGE = {eng}, ISBN = {978-1-948087-12-4}, URL = {http://aclweb.org/anthology/W18-0618}, DOI = {10.18653/v1/W18-0618}, PUBLISHER = {ACL}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2018)}, EDITOR = {Loveys, Kate and Niederhoffer, Kate and Prud'hommeaux, Emily and Resnik, Rebecca and Resnik, Philip}, PAGES = {168--173}, ADDRESS = {New Orleans, LA, USA}, }
Endnote
%0 Conference Proceedings %A MacAvaney, Sean %A Desmet, Bart %A Cohan, Arman %A Soldaini, Luca %A Yates, Andrew %A Zirikly, Ayah %A Goharian, Nazli %+ External Organizations External Organizations External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T RSDD-Time: Temporal Annotation of Self-Reported Mental Health Diagnoses : %G eng %U http://hdl.handle.net/21.11116/0000-0002-5E8C-8 %U http://aclweb.org/anthology/W18-0618 %R 10.18653/v1/W18-0618 %D 2018 %B Fifth Workshop on Computational Linguistics and Clinical Psychology %Z date of event: 2018-06-05 - 2018-06-05 %C New Orleans, LA, USA %B Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology %E Loveys, Kate; Niederhoffer, Kate; Prud'hommeaux, Emily; Resnik, Rebecca; Resnik, Philip %P 168 - 173 %I ACL %@ 978-1-948087-12-4 %U https://aclanthology.info/papers/W18-0618/w18-0618
[45]
S. MacAvaney, A. Yates, A. Cohan, L. Soldaini, K. Hui, N. Goharian, and O. Frieder, “Characterizing Question Facets for Complex Answer Retrieval,” in SIGIR’18, 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, Ann Arbor, MI, USA, 2018.
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@inproceedings{MacAvaney_SIGIR2018, TITLE = {Characterizing Question Facets for Complex Answer Retrieval}, AUTHOR = {MacAvaney, Sean and Yates, Andrew and Cohan, Arman and Soldaini, Luca and Hui, Kai and Goharian, Nazli and Frieder, Ophir}, LANGUAGE = {eng}, ISBN = {978-1-4503-5657-2}, DOI = {10.1145/3209978.3210135}, PUBLISHER = {ACM}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {SIGIR'18, 41st International ACM SIGIR Conference on Research and Development in Information Retrieval}, PAGES = {1205--1208}, ADDRESS = {Ann Arbor, MI, USA}, }
Endnote
%0 Conference Proceedings %A MacAvaney, Sean %A Yates, Andrew %A Cohan, Arman %A Soldaini, Luca %A Hui, Kai %A Goharian, Nazli %A Frieder, Ophir %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations External Organizations External Organizations %T Characterizing Question Facets for Complex Answer Retrieval : %G eng %U http://hdl.handle.net/21.11116/0000-0002-5ECA-2 %R 10.1145/3209978.3210135 %D 2018 %B 41st International ACM SIGIR Conference on Research and Development in Information Retrieval %Z date of event: 2018-07-08 - 2018-07-12 %C Ann Arbor, MI, USA %B SIGIR'18 %P 1205 - 1208 %I ACM %@ 978-1-4503-5657-2
[46]
S. MacAvaney, A. Yates, A. Cohan, L. Soldaini, K. Hui, N. Goharian, and O. Frieder, “Characterizing Question Facets for Complex Answer Retrieval,” 2018. [Online]. Available: http://arxiv.org/abs/1805.00791. (arXiv: 1805.00791)
Abstract
Complex answer retrieval (CAR) is the process of retrieving answers to questions that have multifaceted or nuanced answers. In this work, we present two novel approaches for CAR based on the observation that question facets can vary in utility: from structural (facets that can apply to many similar topics, such as 'History') to topical (facets that are specific to the question's topic, such as the 'Westward expansion' of the United States). We first explore a way to incorporate facet utility into ranking models during query term score combination. We then explore a general approach to reform the structure of ranking models to aid in learning of facet utility in the query-document term matching phase. When we use our techniques with a leading neural ranker on the TREC CAR dataset, our methods rank first in the 2017 TREC CAR benchmark, and yield up to 26% higher performance than the next best method.
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@online{MacAvernay_arXIv1805.00791, TITLE = {Characterizing Question Facets for Complex Answer Retrieval}, AUTHOR = {MacAvaney, Sean and Yates, Andrew and Cohan, Arman and Soldaini, Luca and Hui, Kai and Goharian, Nazli and Frieder, Ophir}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1805.00791}, EPRINT = {1805.00791}, EPRINTTYPE = {arXiv}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Complex answer retrieval (CAR) is the process of retrieving answers to questions that have multifaceted or nuanced answers. In this work, we present two novel approaches for CAR based on the observation that question facets can vary in utility: from structural (facets that can apply to many similar topics, such as 'History') to topical (facets that are specific to the question's topic, such as the 'Westward expansion' of the United States). We first explore a way to incorporate facet utility into ranking models during query term score combination. We then explore a general approach to reform the structure of ranking models to aid in learning of facet utility in the query-document term matching phase. When we use our techniques with a leading neural ranker on the TREC CAR dataset, our methods rank first in the 2017 TREC CAR benchmark, and yield up to 26% higher performance than the next best method.}, }
Endnote
%0 Report %A MacAvaney, Sean %A Yates, Andrew %A Cohan, Arman %A Soldaini, Luca %A Hui, Kai %A Goharian, Nazli %A Frieder, Ophir %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations External Organizations External Organizations %T Characterizing Question Facets for Complex Answer Retrieval : %G eng %U http://hdl.handle.net/21.11116/0000-0002-5ECE-E %U http://arxiv.org/abs/1805.00791 %D 2018 %X Complex answer retrieval (CAR) is the process of retrieving answers to questions that have multifaceted or nuanced answers. In this work, we present two novel approaches for CAR based on the observation that question facets can vary in utility: from structural (facets that can apply to many similar topics, such as 'History') to topical (facets that are specific to the question's topic, such as the 'Westward expansion' of the United States). We first explore a way to incorporate facet utility into ranking models during query term score combination. We then explore a general approach to reform the structure of ranking models to aid in learning of facet utility in the query-document term matching phase. When we use our techniques with a leading neural ranker on the TREC CAR dataset, our methods rank first in the 2017 TREC CAR benchmark, and yield up to 26% higher performance than the next best method. %K Computer Science, Information Retrieval, cs.IR
[47]
S. MacAvaney, B. Desmet, A. Cohan, L. Soldaini, A. Yates, A. Zirikly, and N. Goharian, “RSDD-Time: Temporal Annotation of Self-Reported Mental Health Diagnoses,” 2018. [Online]. Available: http://arxiv.org/abs/1806.07916. (arXiv: 1806.07916)
Abstract
Self-reported diagnosis statements have been widely employed in studying language related to mental health in social media. However, existing research has largely ignored the temporality of mental health diagnoses. In this work, we introduce RSDD-Time: a new dataset of 598 manually annotated self-reported depression diagnosis posts from Reddit that include temporal information about the diagnosis. Annotations include whether a mental health condition is present and how recently the diagnosis happened. Furthermore, we include exact temporal spans that relate to the date of diagnosis. This information is valuable for various computational methods to examine mental health through social media because one's mental health state is not static. We also test several baseline classification and extraction approaches, which suggest that extracting temporal information from self-reported diagnosis statements is challenging.
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@online{MacAveray_arXiv1806.07916, TITLE = {{RSDD}-Time: Temporal Annotation of Self-Reported Mental Health Diagnoses}, AUTHOR = {MacAvaney, Sean and Desmet, Bart and Cohan, Arman and Soldaini, Luca and Yates, Andrew and Zirikly, Ayah and Goharian, Nazli}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1806.07916}, EPRINT = {1806.07916}, EPRINTTYPE = {arXiv}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Self-reported diagnosis statements have been widely employed in studying language related to mental health in social media. However, existing research has largely ignored the temporality of mental health diagnoses. In this work, we introduce RSDD-Time: a new dataset of 598 manually annotated self-reported depression diagnosis posts from Reddit that include temporal information about the diagnosis. Annotations include whether a mental health condition is present and how recently the diagnosis happened. Furthermore, we include exact temporal spans that relate to the date of diagnosis. This information is valuable for various computational methods to examine mental health through social media because one's mental health state is not static. We also test several baseline classification and extraction approaches, which suggest that extracting temporal information from self-reported diagnosis statements is challenging.}, }
Endnote
%0 Report %A MacAvaney, Sean %A Desmet, Bart %A Cohan, Arman %A Soldaini, Luca %A Yates, Andrew %A Zirikly, Ayah %A Goharian, Nazli %+ External Organizations External Organizations External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T RSDD-Time: Temporal Annotation of Self-Reported Mental Health Diagnoses : %G eng %U http://hdl.handle.net/21.11116/0000-0002-5ED9-1 %U http://arxiv.org/abs/1806.07916 %D 2018 %X Self-reported diagnosis statements have been widely employed in studying language related to mental health in social media. However, existing research has largely ignored the temporality of mental health diagnoses. In this work, we introduce RSDD-Time: a new dataset of 598 manually annotated self-reported depression diagnosis posts from Reddit that include temporal information about the diagnosis. Annotations include whether a mental health condition is present and how recently the diagnosis happened. Furthermore, we include exact temporal spans that relate to the date of diagnosis. This information is valuable for various computational methods to examine mental health through social media because one's mental health state is not static. We also test several baseline classification and extraction approaches, which suggest that extracting temporal information from self-reported diagnosis statements is challenging. %K Computer Science, Computation and Language, cs.CL
[48]
P. Mandros, M. Boley, and J. Vreeken, “Discovering Reliable Dependencies from Data: Hardness and Improved Algorithms,” 2018. [Online]. Available: http://arxiv.org/abs/1809.05467. (arXiv: 1809.05467)
Abstract
The reliable fraction of information is an attractive score for quantifying (functional) dependencies in high-dimensional data. In this paper, we systematically explore the algorithmic implications of using this measure for optimization. We show that the problem is NP-hard, which justifies the usage of worst-case exponential-time as well as heuristic search methods. We then substantially improve the practical performance for both optimization styles by deriving a novel admissible bounding function that has an unbounded potential for additional pruning over the previously proposed one. Finally, we empirically investigate the approximation ratio of the greedy algorithm and show that it produces highly competitive results in a fraction of time needed for complete branch-and-bound style search.
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@online{Mandros_arXiv1809.05467, TITLE = {Discovering Reliable Dependencies from Data: Hardness and Improved Algorithms}, AUTHOR = {Mandros, Panagiotis and Boley, Mario and Vreeken, Jilles}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1809.05467}, EPRINT = {1809.05467}, EPRINTTYPE = {arXiv}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, ABSTRACT = {The reliable fraction of information is an attractive score for quantifying (functional) dependencies in high-dimensional data. In this paper, we systematically explore the algorithmic implications of using this measure for optimization. We show that the problem is NP-hard, which justifies the usage of worst-case exponential-time as well as heuristic search methods. We then substantially improve the practical performance for both optimization styles by deriving a novel admissible bounding function that has an unbounded potential for additional pruning over the previously proposed one. Finally, we empirically investigate the approximation ratio of the greedy algorithm and show that it produces highly competitive results in a fraction of time needed for complete branch-and-bound style search.}, }
Endnote
%0 Report %A Mandros, Panagiotis %A Boley, Mario %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Discovering Reliable Dependencies from Data: Hardness and Improved Algorithms : %G eng %U http://hdl.handle.net/21.11116/0000-0002-9EC9-A %U http://arxiv.org/abs/1809.05467 %D 2018 %X The reliable fraction of information is an attractive score for quantifying (functional) dependencies in high-dimensional data. In this paper, we systematically explore the algorithmic implications of using this measure for optimization. We show that the problem is NP-hard, which justifies the usage of worst-case exponential-time as well as heuristic search methods. We then substantially improve the practical performance for both optimization styles by deriving a novel admissible bounding function that has an unbounded potential for additional pruning over the previously proposed one. Finally, we empirically investigate the approximation ratio of the greedy algorithm and show that it produces highly competitive results in a fraction of time needed for complete branch-and-bound style search. %K Computer Science, Artificial Intelligence, cs.AI,Computer Science, Databases, cs.DB,Computer Science, Information Theory, cs.IT,Mathematics, Information Theory, math.IT
[49]
P. Mandros, M. Boley, and J. Vreeken, “Discovering Reliable Dependencies from Data: Hardness and Improved Algorithms,” in IEEE International Conference on Data Mining (ICDM 2018), Singapore, Singapore. (Accepted/in press)
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@inproceedings{mandros:18:fedora, TITLE = {Discovering Reliable Dependencies from Data: {H}ardness and Improved Algorithms}, AUTHOR = {Mandros, Panagiotis and Boley, Mario and Vreeken, Jilles}, LANGUAGE = {eng}, PUBLISHER = {IEEE}, YEAR = {2018}, PUBLREMARK = {Accepted}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {IEEE International Conference on Data Mining (ICDM 2018)}, ADDRESS = {Singapore, Singapore}, }
Endnote
%0 Conference Proceedings %A Mandros, Panagiotis %A Boley, Mario %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Discovering Reliable Dependencies from Data: Hardness and Improved Algorithms : %G eng %U http://hdl.handle.net/21.11116/0000-0002-9EA2-5 %D 2018 %B IEEE International Conference on Data Mining %Z date of event: 2018-11-17 - 2018-11-20 %C Singapore, Singapore %B IEEE International Conference on Data Mining %I IEEE
[50]
A. Marx and J. Vreeken, “Causal Discovery by Telling Apart Parents and Children,” 2018. [Online]. Available: http://arxiv.org/abs/1808.06356. (arXiv: 1808.06356)
Abstract
We consider the problem of inferring the directed, causal graph from observational data, assuming no hidden confounders. We take an information theoretic approach, and make three main contributions. First, we show how through algorithmic information theory we can obtain SCI, a highly robust, effective and computationally efficient test for conditional independence---and show it outperforms the state of the art when applied in constraint-based inference methods such as stable PC. Second, building upon on SCI, we show how to tell apart the parents and children of a given node based on the algorithmic Markov condition. We give the Climb algorithm to efficiently discover the directed, causal Markov blanket---and show it is at least as accurate as inferring the global network, while being much more efficient. Last, but not least, we detail how we can use the Climb score to direct those edges that state of the art causal discovery algorithms based on PC or GES leave undirected---and show this improves their precision, recall and F1 scores by up to 20%.
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@online{Marx_arXiv1808.06356, TITLE = {Causal Discovery by Telling Apart Parents and Children}, AUTHOR = {Marx, Alexander and Vreeken, Jilles}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1808.06356}, EPRINT = {1808.06356}, EPRINTTYPE = {arXiv}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, ABSTRACT = {We consider the problem of inferring the directed, causal graph from observational data, assuming no hidden confounders. We take an information theoretic approach, and make three main contributions. First, we show how through algorithmic information theory we can obtain SCI, a highly robust, effective and computationally efficient test for conditional independence---and show it outperforms the state of the art when applied in constraint-based inference methods such as stable PC. Second, building upon on SCI, we show how to tell apart the parents and children of a given node based on the algorithmic Markov condition. We give the Climb algorithm to efficiently discover the directed, causal Markov blanket---and show it is at least as accurate as inferring the global network, while being much more efficient. Last, but not least, we detail how we can use the Climb score to direct those edges that state of the art causal discovery algorithms based on PC or GES leave undirected---and show this improves their precision, recall and F1 scores by up to 20%.}, }
Endnote
%0 Report %A Marx, Alexander %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Causal Discovery by Telling Apart Parents and Children : %G eng %U http://hdl.handle.net/21.11116/0000-0002-5F36-8 %U http://arxiv.org/abs/1808.06356 %D 2018 %X We consider the problem of inferring the directed, causal graph from observational data, assuming no hidden confounders. We take an information theoretic approach, and make three main contributions. First, we show how through algorithmic information theory we can obtain SCI, a highly robust, effective and computationally efficient test for conditional independence---and show it outperforms the state of the art when applied in constraint-based inference methods such as stable PC. Second, building upon on SCI, we show how to tell apart the parents and children of a given node based on the algorithmic Markov condition. We give the Climb algorithm to efficiently discover the directed, causal Markov blanket---and show it is at least as accurate as inferring the global network, while being much more efficient. Last, but not least, we detail how we can use the Climb score to direct those edges that state of the art causal discovery algorithms based on PC or GES leave undirected---and show this improves their precision, recall and F1 scores by up to 20%. %K Statistics, Machine Learning, stat.ML,Computer Science, Learning, cs.LG
[51]
A. Marx and J. Vreeken, “Causal Inference on Event Sequences,” in Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2018), Dublin, Ireland. (Accepted/in press)
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@inproceedings{marx:18:crack, TITLE = {Causal Inference on Event Sequences}, AUTHOR = {Marx, Alexander and Vreeken, Jilles}, LANGUAGE = {eng}, PUBLISHER = {Springer}, YEAR = {2018}, PUBLREMARK = {Accepted}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2018)}, SERIES = {Lecture Notes in Artificial Intelligence}, ADDRESS = {Dublin, Ireland}, }
Endnote
%0 Conference Proceedings %A Marx, Alexander %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Causal Inference on Event Sequences : %G eng %U http://hdl.handle.net/21.11116/0000-0002-9E86-5 %D 2018 %B European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases %Z date of event: 2018-09-10 - 2018-09-14 %C Dublin, Ireland %B Machine Learning and Knowledge Discovery in Databases %I Springer %B Lecture Notes in Artificial Intelligence
[52]
A. Marx and J. Vreeken, “Stochastic Complexity for Testing Conditional Independence on Discrete Data,” in Proceedings of the NeurIPS 2018 workshop on Causal Learning (NeurIPS CL 2018), Montréal, Canada, 2018.
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@inproceedings{marx:18:dice, TITLE = {Stochastic Complexity for Testing Conditional Independence on Discrete Data}, AUTHOR = {Marx, Alexander and Vreeken, Jilles}, LANGUAGE = {eng}, URL = {https://drive.google.com/file/d/1mMkO5YZ5gkBRRFbfYb4DDRCsCN243eb2/view}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of the NeurIPS 2018 workshop on Causal Learning (NeurIPS CL 2018)}, EID = {10}, ADDRESS = {Montr{\'e}al, Canada}, }
Endnote
%0 Conference Proceedings %A Marx, Alexander %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Stochastic Complexity for Testing Conditional Independence on Discrete Data : %G eng %U http://hdl.handle.net/21.11116/0000-0002-9EC2-1 %U https://drive.google.com/file/d/1mMkO5YZ5gkBRRFbfYb4DDRCsCN243eb2/view %D 2018 %B NeurIPS 2018 Workshop on Causal Learning %Z date of event: 2018-12-07 - 2018-12-07 %C Montréal, Canada %B Proceedings of the NeurIPS 2018 workshop on Causal Learning %Z sequence number: 10
[53]
A. Marx and J. Vreeken, “Telling Cause from Effect by Local and Global Regression,” Knowledge and Information Systems. (Accepted/in press)
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@article{marx:19:crack, TITLE = {Telling Cause from Effect by Local and Global Regression}, AUTHOR = {Marx, Alexander and Vreeken, Jilles}, LANGUAGE = {eng}, ISSN = {0219-1377}, PUBLISHER = {Springer}, YEAR = {2018}, PUBLREMARK = {Accepted}, MARGINALMARK = {$\bullet$}, JOURNAL = {Knowledge and Information Systems}, }
Endnote
%0 Journal Article %A Marx, Alexander %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Telling Cause from Effect by Local and Global Regression : %G eng %U http://hdl.handle.net/21.11116/0000-0002-9EAD-A %D 2018 %J Knowledge and Information Systems %I Springer %@ false
[54]
S. Metzler and P. Miettinen, “Random Graph Generators for Hyperbolic Community Structures,” in Complex Networks and Their Applications VII, Cambridge, UK, 2018.
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@inproceedings{Metzler_COMPLEXNETWORKS2018, TITLE = {Random Graph Generators for Hyperbolic Community Structures}, AUTHOR = {Metzler, Saskia and Miettinen, Pauli}, LANGUAGE = {eng}, ISBN = {978-3-030-05410-6; 978-3-030-05411-3}, DOI = {10.1007/978-3-030-05411-3_54}, PUBLISHER = {Springer}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Complex Networks and Their Applications VII}, EDITOR = {Aiello, Luca Maria and Cherifi, Chantal and Cherifi, Hocine and Lambiotte, Renaud and Li{\'o}, Pietro and Rocha, Luis M.}, PAGES = {680--693}, SERIES = {Studies in Computational Intelligence}, VOLUME = {812}, ADDRESS = {Cambridge, UK}, }
Endnote
%0 Conference Proceedings %A Metzler, Saskia %A Miettinen, Pauli %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Random Graph Generators for Hyperbolic Community Structures : %G eng %U http://hdl.handle.net/21.11116/0000-0002-A929-2 %R 10.1007/978-3-030-05411-3_54 %D 2018 %B 7th International Conference on Complex Networks and Their Applications %Z date of event: 2018-12-11 - 2018-12-13 %C Cambridge, UK %B Complex Networks and Their Applications VII %E Aiello, Luca Maria; Cherifi, Chantal; Cherifi, Hocine; Lambiotte, Renaud; Lió, Pietro; Rocha, Luis M. %P 680 - 693 %I Springer %@ 978-3-030-05410-6 978-3-030-05411-3 %B Studies in Computational Intelligence %N 812
[55]
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}, MARGINALMARK = {$\bullet$}, 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
[56]
P. Mirza, S. Razniewski, F. Darari, and G. Weikum, “Enriching Knowledge Bases with Counting Quantifiers,” 2018. [Online]. Available: http://arxiv.org/abs/1807.03656. (arXiv: 1807.03656)
Abstract
Information extraction traditionally focuses on extracting relations between identifiable entities, such as <Monterey, locatedIn, California>. Yet, texts often also contain Counting information, stating that a subject is in a specific relation with a number of objects, without mentioning the objects themselves, for example, "California is divided into 58 counties". Such counting quantifiers can help in a variety of tasks such as query answering or knowledge base curation, but are neglected by prior work. This paper develops the first full-fledged system for extracting counting information from text, called CINEX. We employ distant supervision using fact counts from a knowledge base as training seeds, and develop novel techniques for dealing with several challenges: (i) non-maximal training seeds due to the incompleteness of knowledge bases, (ii) sparse and skewed observations in text sources, and (iii) high diversity of linguistic patterns. Experiments with five human-evaluated relations show that CINEX can achieve 60% average precision for extracting counting information. In a large-scale experiment, we demonstrate the potential for knowledge base enrichment by applying CINEX to 2,474 frequent relations in Wikidata. CINEX can assert the existence of 2.5M facts for 110 distinct relations, which is 28% more than the existing Wikidata facts for these relations.
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@online{Mirza_arXiv:1807.03656, TITLE = {Enriching Knowledge Bases with Counting Quantifiers}, AUTHOR = {Mirza, Paramita and Razniewski, Simon and Darari, Fariz and Weikum, Gerhard}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1807.03656}, EPRINT = {1807.03656}, EPRINTTYPE = {arXiv}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Information extraction traditionally focuses on extracting relations between identifiable entities, such as <Monterey, locatedIn, California>. Yet, texts often also contain Counting information, stating that a subject is in a specific relation with a number of objects, without mentioning the objects themselves, for example, "California is divided into 58 counties". Such counting quantifiers can help in a variety of tasks such as query answering or knowledge base curation, but are neglected by prior work. This paper develops the first full-fledged system for extracting counting information from text, called CINEX. We employ distant supervision using fact counts from a knowledge base as training seeds, and develop novel techniques for dealing with several challenges: (i) non-maximal training seeds due to the incompleteness of knowledge bases, (ii) sparse and skewed observations in text sources, and (iii) high diversity of linguistic patterns. Experiments with five human-evaluated relations show that CINEX can achieve 60% average precision for extracting counting information. In a large-scale experiment, we demonstrate the potential for knowledge base enrichment by applying CINEX to 2,474 frequent relations in Wikidata. CINEX can assert the existence of 2.5M facts for 110 distinct relations, which is 28% more than the existing Wikidata facts for these relations.}, }
Endnote
%0 Report %A Mirza, Paramita %A Razniewski, Simon %A Darari, Fariz %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Enriching Knowledge Bases with Counting Quantifiers : %G eng %U http://hdl.handle.net/21.11116/0000-0001-E16D-7 %U http://arxiv.org/abs/1807.03656 %D 2018 %X Information extraction traditionally focuses on extracting relations between identifiable entities, such as <Monterey, locatedIn, California>. Yet, texts often also contain Counting information, stating that a subject is in a specific relation with a number of objects, without mentioning the objects themselves, for example, "California is divided into 58 counties". Such counting quantifiers can help in a variety of tasks such as query answering or knowledge base curation, but are neglected by prior work. This paper develops the first full-fledged system for extracting counting information from text, called CINEX. We employ distant supervision using fact counts from a knowledge base as training seeds, and develop novel techniques for dealing with several challenges: (i) non-maximal training seeds due to the incompleteness of knowledge bases, (ii) sparse and skewed observations in text sources, and (iii) high diversity of linguistic patterns. Experiments with five human-evaluated relations show that CINEX can achieve 60% average precision for extracting counting information. In a large-scale experiment, we demonstrate the potential for knowledge base enrichment by applying CINEX to 2,474 frequent relations in Wikidata. CINEX can assert the existence of 2.5M facts for 110 distinct relations, which is 28% more than the existing Wikidata facts for these relations. %K Computer Science, Computation and Language, cs.CL
[57]
P. Mirza, F. Darari, and R. Mahendra, “KOI at SemEval-2018 Task 5: Building Knowledge Graph of Incidents,” in Proceedings of the 12th International Workshop on Semantic Evaluation (SemEval-2018), New Orleans, LA, 2018.
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@inproceedings{S18-1010, TITLE = {{KOI} at {SemEval}-2018 Task 5: {B}uilding Knowledge Graph of Incidents}, AUTHOR = {Mirza, Paramita and Darari, Fariz and Mahendra, Rahmad}, LANGUAGE = {eng}, ISBN = {978-1-948087-20-9}, DOI = {10.18653/v1/S18-1010}, PUBLISHER = {ACL}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of the 12th International Workshop on Semantic Evaluation (SemEval-2018)}, EDITOR = {Apidianaki, Marianna and Mohammad, Saif M. and May, Jonathan and Shutova, Ekatarina and Bethard, Steven and Carpuat, Marine}, PAGES = {81--87}, ADDRESS = {New Orleans, LA}, }
Endnote
%0 Conference Proceedings %A Mirza, Paramita %A Darari, Fariz %A Mahendra, Rahmad %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T KOI at SemEval-2018 Task 5: Building Knowledge Graph of Incidents : %G eng %U http://hdl.handle.net/21.11116/0000-0002-A818-6 %R 10.18653/v1/S18-1010 %D 2018 %B Twelfth International Workshop on Semantic Evaluation %Z date of event: 2018-06-05 - 2018-06-06 %C New Orleans, LA %B Proceedings of the 12th International Workshop on Semantic Evaluation %E Apidianaki, Marianna; Mohammad, Saif M.; May, Jonathan; Shutova, Ekatarina; Bethard, Steven; Carpuat, Marine %P 81 - 87 %I ACL %@ 978-1-948087-20-9 %U http://aclweb.org/anthology/S18-1010
[58]
A. Mishra, “Leveraging Semantic Annotations for Event-focused Search & Summarization,” Universität des Saarlandes, Saarbrücken, 2018.
Abstract
Today in this Big Data era, overwhelming amounts of textual information across different sources with a high degree of redundancy has made it hard for a consumer to retrospect on past events. A plausible solution is to link semantically similar information contained across the different sources to enforce a structure thereby providing multiple access paths to relevant information. Keeping this larger goal in view, this work uses Wikipedia and online news articles as two prominent yet disparate information sources to address the following three problems: • We address a linking problem to connect Wikipedia excerpts to news articles by casting it into an IR task. Our novel approach integrates time, geolocations, and entities with text to identify relevant documents that can be linked to a given excerpt. • We address an unsupervised extractive multi-document summarization task to generate a fixed-length event digest that facilitates efficient consumption of information contained within a large set of documents. Our novel approach proposes an ILP for global inference across text, time, geolocations, and entities associated with the event. • To estimate temporal focus of short event descriptions, we present a semi-supervised approach that leverages redundancy within a longitudinal news collection to estimate accurate probabilistic time models. Extensive experimental evaluations demonstrate the effectiveness and viability of our proposed approaches towards achieving the larger goal.
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@phdthesis{Mishraphd2018, TITLE = {Leveraging Semantic Annotations for Event-focused Search \& Summarization}, AUTHOR = {Mishra, Arunav}, LANGUAGE = {eng}, URL = {urn:nbn:de:bsz:291-scidok-ds-271081}, DOI = {10.22028/D291-27108}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Today in this Big Data era, overwhelming amounts of textual information across different sources with a high degree of redundancy has made it hard for a consumer to retrospect on past events. A plausible solution is to link semantically similar information contained across the different sources to enforce a structure thereby providing multiple access paths to relevant information. Keeping this larger goal in view, this work uses Wikipedia and online news articles as two prominent yet disparate information sources to address the following three problems: \mbox{$\bullet$} We address a linking problem to connect Wikipedia excerpts to news articles by casting it into an IR task. Our novel approach integrates time, geolocations, and entities with text to identify relevant documents that can be linked to a given excerpt. \mbox{$\bullet$} We address an unsupervised extractive multi-document summarization task to generate a fixed-length event digest that facilitates efficient consumption of information contained within a large set of documents. Our novel approach proposes an ILP for global inference across text, time, geolocations, and entities associated with the event. \mbox{$\bullet$} To estimate temporal focus of short event descriptions, we present a semi-supervised approach that leverages redundancy within a longitudinal news collection to estimate accurate probabilistic time models. Extensive experimental evaluations demonstrate the effectiveness and viability of our proposed approaches towards achieving the larger goal.}, }
Endnote
%0 Thesis %A Mishra, Arunav %Y Berberich, Klaus %A referee: Weikum, Gerhard %A referee: Hauff, Claudia %+ Databases and Information Systems, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Leveraging Semantic Annotations for Event-focused Search & Summarization : %G eng %U http://hdl.handle.net/21.11116/0000-0001-1844-8 %U urn:nbn:de:bsz:291-scidok-ds-271081 %R 10.22028/D291-27108 %I Universit&#228;t des Saarlandes %C Saarbr&#252;cken %D 2018 %8 08.02.2018 %P 252 p. %V phd %9 phd %X Today in this Big Data era, overwhelming amounts of textual information across different sources with a high degree of redundancy has made it hard for a consumer to retrospect on past events. A plausible solution is to link semantically similar information contained across the different sources to enforce a structure thereby providing multiple access paths to relevant information. Keeping this larger goal in view, this work uses Wikipedia and online news articles as two prominent yet disparate information sources to address the following three problems: &#8226; We address a linking problem to connect Wikipedia excerpts to news articles by casting it into an IR task. Our novel approach integrates time, geolocations, and entities with text to identify relevant documents that can be linked to a given excerpt. &#8226; We address an unsupervised extractive multi-document summarization task to generate a fixed-length event digest that facilitates efficient consumption of information contained within a large set of documents. Our novel approach proposes an ILP for global inference across text, time, geolocations, and entities associated with the event. &#8226; To estimate temporal focus of short event descriptions, we present a semi-supervised approach that leverages redundancy within a longitudinal news collection to estimate accurate probabilistic time models. Extensive experimental evaluations demonstrate the effectiveness and viability of our proposed approaches towards achieving the larger goal. %U https://publikationen.sulb.uni-saarland.de/handle/20.500.11880/26995
[59]
S. Nag Chowdhury, N. Tandon, H. Ferhatosmanoglu, and G. Weikum, “VISIR: Visual and Semantic Image Label Refinement,” in WSDM’18, 11th ACM International Conference on Web Search and Data Mining, Marina Del Rey, CA, USA, 2018.
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@inproceedings{NagChowdhury_WSDM2018, TITLE = {{VISIR}: {V}isual and Semantic Image Label Refinement}, AUTHOR = {Nag Chowdhury, Sreyasi and Tandon, Niket and Ferhatosmanoglu, Hakan and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-5581-0}, DOI = {10.1145/3159652.3159693}, PUBLISHER = {ACM}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {WSDM'18, 11th ACM International Conference on Web Search and Data Mining}, PAGES = {117--125}, ADDRESS = {Marina Del Rey, CA, USA}, }
Endnote
%0 Conference Proceedings %A Nag Chowdhury, Sreyasi %A Tandon, Niket %A Ferhatosmanoglu, Hakan %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T VISIR: Visual and Semantic Image Label Refinement : %G eng %U http://hdl.handle.net/21.11116/0000-0001-3CA2-5 %R 10.1145/3159652.3159693 %D 2018 %B 11th ACM International Conference on Web Search and Data Mining %Z date of event: 2018-02-05 - 2018-02-09 %C Marina Del Rey, CA, USA %B WSDM'18 %P 117 - 125 %I ACM %@ 978-1-4503-5581-0
[60]
S. Paramonov, D. Stepanova, and P. Miettinen, “Hybrid ASP-based Approach to Pattern Mining,” 2018. [Online]. Available: http://arxiv.org/abs/1808.07302. (arXiv: 1808.07302)
Abstract
Detecting small sets of relevant patterns from a given dataset is a central challenge in data mining. The relevance of a pattern is based on user-provided criteria; typically, all patterns that satisfy certain criteria are considered relevant. Rule-based languages like Answer Set Programming (ASP) seem well-suited for specifying such criteria in a form of constraints. Although progress has been made, on the one hand, on solving individual mining problems and, on the other hand, developing generic mining systems, the existing methods either focus on scalability or on generality. In this paper we make steps towards combining local (frequency, size, cost) and global (various condensed representations like maximal, closed, skyline) constraints in a generic and efficient way. We present a hybrid approach for itemset, sequence and graph mining which exploits dedicated highly optimized mining systems to detect frequent patterns and then filters the results using declarative ASP. To further demonstrate the generic nature of our hybrid framework we apply it to a problem of approximately tiling a database. Experiments on real-world datasets show the effectiveness of the proposed method and computational gains for itemset, sequence and graph mining, as well as approximate tiling. Under consideration in Theory and Practice of Logic Programming (TPLP).
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@online{Paramonov_arXiv1808.07302, TITLE = {Hybrid {ASP}-based Approach to Pattern Mining}, AUTHOR = {Paramonov, Sergey and Stepanova, Daria and Miettinen, Pauli}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1808.07302}, EPRINT = {1808.07302}, EPRINTTYPE = {arXiv}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Detecting small sets of relevant patterns from a given dataset is a central challenge in data mining. The relevance of a pattern is based on user-provided criteria; typically, all patterns that satisfy certain criteria are considered relevant. Rule-based languages like Answer Set Programming (ASP) seem well-suited for specifying such criteria in a form of constraints. Although progress has been made, on the one hand, on solving individual mining problems and, on the other hand, developing generic mining systems, the existing methods either focus on scalability or on generality. In this paper we make steps towards combining local (frequency, size, cost) and global (various condensed representations like maximal, closed, skyline) constraints in a generic and efficient way. We present a hybrid approach for itemset, sequence and graph mining which exploits dedicated highly optimized mining systems to detect frequent patterns and then filters the results using declarative ASP. To further demonstrate the generic nature of our hybrid framework we apply it to a problem of approximately tiling a database. Experiments on real-world datasets show the effectiveness of the proposed method and computational gains for itemset, sequence and graph mining, as well as approximate tiling. Under consideration in Theory and Practice of Logic Programming (TPLP).}, }
Endnote
%0 Report %A Paramonov, Sergey %A Stepanova, Daria %A Miettinen, Pauli %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Hybrid ASP-based Approach to Pattern Mining : %G eng %U http://hdl.handle.net/21.11116/0000-0002-5E60-9 %U http://arxiv.org/abs/1808.07302 %D 2018 %X Detecting small sets of relevant patterns from a given dataset is a central challenge in data mining. The relevance of a pattern is based on user-provided criteria; typically, all patterns that satisfy certain criteria are considered relevant. Rule-based languages like Answer Set Programming (ASP) seem well-suited for specifying such criteria in a form of constraints. Although progress has been made, on the one hand, on solving individual mining problems and, on the other hand, developing generic mining systems, the existing methods either focus on scalability or on generality. In this paper we make steps towards combining local (frequency, size, cost) and global (various condensed representations like maximal, closed, skyline) constraints in a generic and efficient way. We present a hybrid approach for itemset, sequence and graph mining which exploits dedicated highly optimized mining systems to detect frequent patterns and then filters the results using declarative ASP. To further demonstrate the generic nature of our hybrid framework we apply it to a problem of approximately tiling a database. Experiments on real-world datasets show the effectiveness of the proposed method and computational gains for itemset, sequence and graph mining, as well as approximate tiling. Under consideration in Theory and Practice of Logic Programming (TPLP). %K Computer Science, Artificial Intelligence, cs.AI
[61]
T. Pellissier Tanon, D. Stepanova, S. Razniewski, P. Mirza, and G. Weikum, “Completeness-aware Rule Learning from Knowledge Graphs,” in Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI 2018), Stockholm, Sweden, 2018.
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@inproceedings{PellissierIJCAI2018, TITLE = {Completeness-aware Rule Learning from Knowledge Graphs}, AUTHOR = {Pellissier Tanon, Thomas and Stepanova, Daria and Razniewski, Simon and Mirza, Paramita and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-0-9992411-2-7}, DOI = {10.24963/ijcai.2018/749}, PUBLISHER = {IJCAI}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI 2018)}, EDITOR = {Lang, J{\'e}r{\^o}me}, PAGES = {5339--5343}, ADDRESS = {Stockholm, Sweden}, }
Endnote
%0 Conference Proceedings %A Pellissier Tanon, Thomas %A Stepanova, Daria %A Razniewski, Simon %A Mirza, Paramita %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Completeness-aware Rule Learning from Knowledge Graphs : %G eng %U http://hdl.handle.net/21.11116/0000-0001-9070-D %R 10.24963/ijcai.2018/749 %D 2018 %B 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence %Z date of event: 2018-07-13 - 2018-07-19 %C Stockholm, Sweden %B Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence %E Lang, J&#233;r&#244;me %P 5339 - 5343 %I IJCAI %@ 978-0-9992411-2-7 %U https://doi.org/10.24963/ijcai.2018/749
[62]
M. Ponza, L. Del Corro, and G. Weikum, “Facts That Matter,” in The Conference on Empirical Methods in Natural Language Processing (EMNLP 2018), Brussels, Belgium, 2018.
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@inproceedings{D18-1129, TITLE = {Facts That Matter}, AUTHOR = {Ponza, Marco and Del Corro, Luciano and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-948087-84-1}, URL = {https://aclanthology.coli.uni-saarland.de/papers/D18-1129/d18-1129}, PUBLISHER = {ACL}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {The Conference on Empirical Methods in Natural Language Processing (EMNLP 2018)}, EDITOR = {Riloff, Ellen and Chiang, David and Hockenmaier, Julia and Jun'ichi, Tsujii}, PAGES = {1043--1048}, ADDRESS = {Brussels, Belgium}, }
Endnote
%0 Conference Proceedings %A Ponza, Marco %A Del Corro, Luciano %A Weikum, Gerhard %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Facts That Matter : %G eng %U http://hdl.handle.net/21.11116/0000-0002-A2C1-C %U https://aclanthology.coli.uni-saarland.de/papers/D18-1129/d18-1129 %D 2018 %B Conference on Empirical Methods in Natural Language Processing %Z date of event: 2018-10-31 - 2018-11-04 %C Brussels, Belgium %B The Conference on Empirical Methods in Natural Language Processing %E Riloff, Ellen; Chiang, David; Hockenmaier, Julia; Jun'ichi, Tsujii %P 1043 - 1048 %I ACL %@ 978-1-948087-84-1
[63]
K. Popat, S. Mukherjee, J. Strötgen, and G. Weikum, “CredEye: A Credibility Lens for Analyzing and Explaining Misinformation,” in Companion of the Word Wide Web Conference (WWW 2018), Lyon, France, 2018.
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@inproceedings{PopatWWW2017, TITLE = {{CredEye}: {A} Credibility Lens for Analyzing and Explaining Misinformation}, AUTHOR = {Popat, Kashyap and Mukherjee, Subhabrata and Str{\"o}tgen, Jannik and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-5640-4}, DOI = {10.1145/3184558.3186967}, PUBLISHER = {ACM}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {Companion of the Word Wide Web Conference (WWW 2018)}, EDITOR = {Champin, Pierre-Antoine and Gandon, Fabien and M{\'e}dini, Lionel}, PAGES = {155--158}, ADDRESS = {Lyon, France}, }
Endnote
%0 Conference Proceedings %A Popat, Kashyap %A Mukherjee, Subhabrata %A Str&#246;tgen, Jannik %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T CredEye: A Credibility Lens for Analyzing and Explaining Misinformation : %G eng %U http://hdl.handle.net/21.11116/0000-0000-B546-5 %R 10.1145/3184558.3186967 %D 2018 %B The Web Conference %Z date of event: 2018-04-23 - 2018-04-27 %C Lyon, France %B Companion of the Word Wide Web Conference %E Champin, Pierre-Antoine; Gandon , Fabien; M&#233;dini, Lionel %P 155 - 158 %I ACM %@ 978-1-4503-5640-4
[64]
K. Popat, S. Mukherjee, A. Yates, and G. Weikum, “DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning,” 2018. [Online]. Available: http://arxiv.org/abs/1809.06416. (arXiv: 1809.06416)
Abstract
Misinformation such as fake news is one of the big challenges of our society. Research on automated fact-checking has proposed methods based on supervised learning, but these approaches do not consider external evidence apart from labeled training instances. Recent approaches counter this deficit by considering external sources related to a claim. However, these methods require substantial feature modeling and rich lexicons. This paper overcomes these limitations of prior work with an end-to-end model for evidence-aware credibility assessment of arbitrary textual claims, without any human intervention. It presents a neural network model that judiciously aggregates signals from external evidence articles, the language of these articles and the trustworthiness of their sources. It also derives informative features for generating user-comprehensible explanations that makes the neural network predictions transparent to the end-user. Experiments with four datasets and ablation studies show the strength of our method.
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@online{Popat_arXiv1809.06416, TITLE = {{DeClarE}: {D}ebunking Fake News and False Claims using Evidence-Aware Deep Learning}, AUTHOR = {Popat, Kashyap and Mukherjee, Subhabrata and Yates, Andrew and Weikum, Gerhard}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1809.06416}, EPRINT = {1809.06416}, EPRINTTYPE = {arXiv}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Misinformation such as fake news is one of the big challenges of our society. Research on automated fact-checking has proposed methods based on supervised learning, but these approaches do not consider external evidence apart from labeled training instances. Recent approaches counter this deficit by considering external sources related to a claim. However, these methods require substantial feature modeling and rich lexicons. This paper overcomes these limitations of prior work with an end-to-end model for evidence-aware credibility assessment of arbitrary textual claims, without any human intervention. It presents a neural network model that judiciously aggregates signals from external evidence articles, the language of these articles and the trustworthiness of their sources. It also derives informative features for generating user-comprehensible explanations that makes the neural network predictions transparent to the end-user. Experiments with four datasets and ablation studies show the strength of our method.}, }
Endnote
%0 Report %A Popat, Kashyap %A Mukherjee, Subhabrata %A Yates, Andrew %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning : %G eng %U http://hdl.handle.net/21.11116/0000-0002-5EE1-7 %U http://arxiv.org/abs/1809.06416 %D 2018 %X Misinformation such as fake news is one of the big challenges of our society. Research on automated fact-checking has proposed methods based on supervised learning, but these approaches do not consider external evidence apart from labeled training instances. Recent approaches counter this deficit by considering external sources related to a claim. However, these methods require substantial feature modeling and rich lexicons. This paper overcomes these limitations of prior work with an end-to-end model for evidence-aware credibility assessment of arbitrary textual claims, without any human intervention. It presents a neural network model that judiciously aggregates signals from external evidence articles, the language of these articles and the trustworthiness of their sources. It also derives informative features for generating user-comprehensible explanations that makes the neural network predictions transparent to the end-user. Experiments with four datasets and ablation studies show the strength of our method. %K Computer Science, Computation and Language, cs.CL,Computer Science, Learning, cs.LG
[65]
Y. Ran, B. He, K. Hui, J. Xu, and L. Sun, “Neural Relevance Model Using Similarities with Elite Documents for Effective Clinical Decision Support,” International Journal of Data Mining and Bioinformatics, vol. 20, no. 2, 2018.
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@article{Ran_2018, TITLE = {Neural Relevance Model Using Similarities with Elite Documents for Effective Clinical Decision Support}, AUTHOR = {Ran, Yanhua and He, Ben and Hui, Kai and Xu, Jungang and Sun, Le}, LANGUAGE = {eng}, ISSN = {1748-5673}, DOI = {10.1504/IJDMB.2018.10015098}, PUBLISHER = {Inderscience Publ.}, ADDRESS = {Gen{\`e}ve}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, JOURNAL = {International Journal of Data Mining and Bioinformatics}, VOLUME = {20}, NUMBER = {2}, PAGES = {91--108}, }
Endnote
%0 Journal Article %A Ran, Yanhua %A He, Ben %A Hui, Kai %A Xu, Jungang %A Sun, Le %+ External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T Neural Relevance Model Using Similarities with Elite Documents for Effective Clinical Decision Support : %G eng %U http://hdl.handle.net/21.11116/0000-0002-5743-1 %R 10.1504/IJDMB.2018.10015098 %7 2018 %D 2018 %J International Journal of Data Mining and Bioinformatics %V 20 %N 2 %& 91 %P 91 - 108 %I Inderscience Publ. %C Gen&#232;ve %@ false
[66]
S. Razniewski and G. Weikum, “Knowledge Base Recall: Detecting and Resolving the Unknown Unknowns,” ACM SIGWEB Newsletter, no. Spring, 2018.
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@article{Razniewski2018, TITLE = {Knowledge Base Recall: Detecting and Resolving the Unknown Unknowns}, AUTHOR = {Razniewski, Simon and Weikum, Gerhard}, LANGUAGE = {eng}, DOI = {10.1145/3210578.3210581}, PUBLISHER = {ACM}, ADDRESS = {New York, NY}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, JOURNAL = {ACM SIGWEB Newsletter}, NUMBER = {Spring}, EID = {3}, }
Endnote
%0 Journal Article %A Razniewski, Simon %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Knowledge Base Recall: Detecting and Resolving the Unknown Unknowns : %G eng %U http://hdl.handle.net/21.11116/0000-0001-E175-D %R 10.1145/3210578.3210581 %7 2018 %D 2018 %J ACM SIGWEB Newsletter %N Spring %Z sequence number: 3 %I ACM %C New York, NY
[67]
M. Ringsquandl, E. Kharlamov, D. Stepanova, M. Hildebrandt, S. Lamparter, R. Lepratti, I. Horrocks, and P. Kroeger, “Filling Gaps in Industrial Knowledge Graphs via Event-Enhanced Embedding,” in Proceedings of the ISWC 2018 Posters & Demonstrations, Industry and Blue Sky Ideas Tracks co-located with 17th International Semantic Web Conference (ISWC 2018) (ISWC-P&D-Industry-BlueSky 2018), Monterey, CA, USA, 2018.
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@inproceedings{Ringsquandl_ISWC2018_Poster, TITLE = {Filling Gaps in Industrial Knowledge Graphs via Event-Enhanced Embedding}, AUTHOR = {Ringsquandl, Martin and Kharlamov, Evgeny and Stepanova, Daria and Hildebrandt, Marcel and Lamparter, Steffen and Lepratti, Raffaello and Horrocks, Ian and Kroeger, Peer}, LANGUAGE = {eng}, ISSN = {1613-0073}, URL = {http://ceur-ws.org/Vol-2180/paper-52.pdf; urn:nbn:de:0074-2180-3}, PUBLISHER = {CEUR-WS.org}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of the ISWC 2018 Posters \& Demonstrations, Industry and Blue Sky Ideas Tracks co-located with 17th International Semantic Web Conference (ISWC 2018) (ISWC-P\&D-Industry-BlueSky 2018)}, EDITOR = {van Erp, Marieke and Atre, Medha and Lopez, Vanessa and Srinivas, Kavitha and Fortuna, Carolina}, EID = {52}, SERIES = {CEUR Workshop Proceedings}, VOLUME = {2180}, ADDRESS = {Monterey, CA, USA}, }
Endnote
%0 Conference Proceedings %A Ringsquandl, Martin %A Kharlamov, Evgeny %A Stepanova, Daria %A Hildebrandt, Marcel %A Lamparter, Steffen %A Lepratti, Raffaello %A Horrocks, Ian %A Kroeger, Peer %+ External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations External Organizations External Organizations %T Filling Gaps in Industrial Knowledge Graphs via Event-Enhanced Embedding : %G eng %U http://hdl.handle.net/21.11116/0000-0002-5E67-2 %U http://ceur-ws.org/Vol-2180/paper-52.pdf %D 2018 %B 17th International Semantic Web Conference %Z date of event: 2018-10-08 - 2018-10-12 %C Monterey, CA, USA %B Proceedings of the ISWC 2018 Posters & Demonstrations, Industry and Blue Sky Ideas Tracks co-located with 17th International Semantic Web Conference (ISWC 2018) %E van Erp, Marieke; Atre, Medha; Lopez, Vanessa; Srinivas, Kavitha; Fortuna, Carolina %Z sequence number: 52 %I CEUR-WS.org %B CEUR Workshop Proceedings %N 2180 %@ false
[68]
M. Ringsquandl, E. Kharlamov, D. Stepanova, M. Hildebrandt, S. Lamparter, R. Lepratti, I. Horrocks, and P. Kröger, “Event-Enhanced Learning for KG Completion,” in The Semantic Web (ESWC 2018), Heraklion, Crete, Greece, 2018.
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@inproceedings{Ringsquandl_ESWC2018, TITLE = {Event-Enhanced Learning for {KG} Completion}, AUTHOR = {Ringsquandl, Martin and Kharlamov, Evgeny and Stepanova, Daria and Hildebrandt, Marcel and Lamparter, Steffen and Lepratti, Raffaello and Horrocks, Ian and Kr{\"o}ger, Peer}, LANGUAGE = {eng}, ISBN = {978-3-319-93416-7}, DOI = {10.1007/978-3-319-93417-4_35}, PUBLISHER = {Springer}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {The Semantic Web (ESWC 2018)}, EDITOR = {Gangem, Aldo and Navigli, Roberto and Vidal, Maria-Esther and Hitzler, Pascal and Troncy, Rapha{\"e}l and Hollink, Laura and Tordai, Anna and Alam, Mehwish}, PAGES = {541--559}, SERIES = {Lecture Notes in Computer Science}, VOLUME = {10843}, ADDRESS = {Heraklion, Crete, Greece}, }
Endnote
%0 Conference Proceedings %A Ringsquandl, Martin %A Kharlamov, Evgeny %A Stepanova, Daria %A Hildebrandt, Marcel %A Lamparter, Steffen %A Lepratti, Raffaello %A Horrocks, Ian %A Kr&#246;ger, Peer %+ External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations External Organizations External Organizations %T Event-Enhanced Learning for KG Completion : %G eng %U http://hdl.handle.net/21.11116/0000-0002-5E82-2 %R 10.1007/978-3-319-93417-4_35 %D 2018 %B 15th Extended Semantic Web Conferenc %Z date of event: 2018-06-03 - 2018-06-07 %C Heraklion, Crete, Greece %B The Semantic Web %E Gangem, Aldo; Navigli, Roberto; Vidal, Maria-Esther; Hitzler, Pascal; Troncy, Rapha&#235;l; Hollink, Laura; Tordai, Anna; Alam, Mehwish %P 541 - 559 %I Springer %@ 978-3-319-93416-7 %B Lecture Notes in Computer Science %N 10843
[69]
D. Seyler, T. Dembelova, L. Del Corro, J. Hoffart, and G. Weikum, “A Study of the Importance of External Knowledge in the Named Entity Recognition Task,” in The 56th Annual Meeting of the Association for Computational Linguistics (ACL 2018), Melbourne, Australia, 2018.
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@inproceedings{AgrawalACL2018b, TITLE = {A Study of the Importance of External Knowledge in the Named Entity Recognition Task}, AUTHOR = {Seyler, Dominic and Dembelova, Tatiana and Del Corro, Luciano and Hoffart, Johannes and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-948087-34-6}, PUBLISHER = {ACL}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {The 56th Annual Meeting of the Association for Computational Linguistics (ACL 2018)}, PAGES = {241--246}, EID = {602}, ADDRESS = {Melbourne, Australia}, }
Endnote
%0 Conference Proceedings %A Seyler, Dominic %A Dembelova, Tatiana %A Del Corro, Luciano %A Hoffart, Johannes %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T A Study of the Importance of External Knowledge in the Named Entity Recognition Task : %G eng %U http://hdl.handle.net/21.11116/0000-0002-0C65-0 %D 2018 %B The 56th Annual Meeting of the Association for Computational Linguistics %Z date of event: 2018-07-15 - 2018-07-20 %C Melbourne, Australia %B The 56th Annual Meeting of the Association for Computational Linguistics %P 241 - 246 %Z sequence number: 602 %I ACL %@ 978-1-948087-34-6 %U http://aclweb.org/anthology/P18-2039
[70]
M. Singh, A. Mishra, Y. Oualil, K. Berberich, and D. Klakow, “Long-Span Language Models for Query-Focused Unsupervised Extractive Text Summarization,” in Advances in Information Retrieval (ECIR 2018), Grenoble, France, 2018.
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@inproceedings{SinghECIR2ss18, TITLE = {Long-Span Language Models for Query-Focused Unsupervised Extractive Text Summarization}, AUTHOR = {Singh, Mittul and Mishra, Arunav and Oualil, Youssef and Berberich, Klaus and Klakow, Dietrich}, LANGUAGE = {eng}, ISBN = {978-3-319-76940-0}, DOI = {10.1007/978-3-319-76941-7_59}, PUBLISHER = {Springer}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {Advances in Information Retrieval (ECIR 2018)}, EDITOR = {Pasi, Gabriella and Piwowarski, Benjamin and Azzopardi, Leif and Hanbury, Allan}, PAGES = {657--664}, SERIES = {Lecture Notes in Computer Science}, VOLUME = {10772}, ADDRESS = {Grenoble, France}, }
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%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
[71]
A. Spitz, J. Strötgen, and M. Gertz, “Predicting Document Creation Times in News Citation Networks,” in Companion of the World Wide Web Conference (WWW 2018), Lyon, France, 2018.
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@inproceedings{SpitzWWW2017, TITLE = {Predicting Document Creation Times in News Citation Networks}, AUTHOR = {Spitz, Andreas and Str{\"o}tgen, Jannik and Gertz, Michael}, LANGUAGE = {eng}, ISBN = {978-1-4503-5640-4}, DOI = {10.1145/3184558.3191633}, PUBLISHER = {ACM}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {Companion of the World Wide Web Conference (WWW 2018)}, EDITOR = {Champin, Pierre-Antoine and Gandon, Fabien and M{\'e}dini, Lionel}, PAGES = {1731--1736}, ADDRESS = {Lyon, France}, }
Endnote
%0 Conference Proceedings %A Spitz, Andreas %A Str&#246;tgen, Jannik %A Gertz, Michael %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Predicting Document Creation Times in News Citation Networks : %G eng %U http://hdl.handle.net/21.11116/0000-0000-B544-7 %R 10.1145/3184558.3191633 %D 2018 %B The Web Conference %Z date of event: 2018-04-23 - 2018-04-27 %C Lyon, France %B Companion of the World Wide Web Conference %E Champin, Pierre-Antoine; Gandon, Fabien; M&#233;dini, Lionel %P 1731 - 1736 %I ACM %@ 978-1-4503-5640-4
[72]
D. Stepanova, V. T. Ho, and M. H. Gad-Elrab, “Rule Induction and Reasoning over Knowledge Graphs,” in Reasoning Web, Esch-sur-Alzette, Luxembourg, 2018.
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@inproceedings{StepanovaRW2018, TITLE = {Rule Induction and Reasoning over Knowledge Graphs}, AUTHOR = {Stepanova, Daria and Ho, Vinh Thinh and Gad-Elrab, Mohamed Hassan}, LANGUAGE = {eng}, ISBN = {978-3-030-00337-1}, DOI = {10.1007/978-3-030-00338-8_6}, PUBLISHER = {Springer}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {Reasoning Web}, EDITOR = {D'Amato, Claudia and Theobald, Martin}, PAGES = {142--172}, SERIES = {Lecture Notes in Computer Science}, VOLUME = {11078}, ADDRESS = {Esch-sur-Alzette, Luxembourg}, }
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%0 Conference Proceedings %A Stepanova, Daria %A Ho, Vinh Thinh %A Gad-Elrab, Mohamed Hassan %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Rule Induction and Reasoning over Knowledge Graphs : %G eng %U http://hdl.handle.net/21.11116/0000-0001-9066-9 %R 10.1007/978-3-030-00338-8_6 %D 2018 %B 14th Reasoning Web Summer School %Z date of event: 2018-09-22 - 2018-09-26 %C Esch-sur-Alzette, Luxembourg %B Reasoning Web %E D'Amato, Claudia; Theobald, Martin %P 142 - 172 %I Springer %@ 978-3-030-00337-1 %B Lecture Notes in Computer Science %N 11078
[73]
J. Strötgen, R. Andrade, and D. Gupta, “Putting Dates on the Map: Harvesting and Analyzing Street Names with Date Mentions and their Explanations,” in JCDL’18, Joint Conference on Digital Libraries, Fort Worth, TX, USA, 2018.
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@inproceedings{StroetgenJCDL2018, TITLE = {Putting Dates on the Map: {H}arvesting and Analyzing Street Names with Date Mentions and their Explanations}, AUTHOR = {Str{\"o}tgen, Jannik and Andrade, Rosita and Gupta, Dhruv}, LANGUAGE = {eng}, ISBN = {978-1-4503-5178-2}, DOI = {10.1145/3197026.3197035}, PUBLISHER = {ACM}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {JCDL'18, Joint Conference on Digital Libraries}, PAGES = {79--88}, ADDRESS = {Fort Worth, TX, USA}, }
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%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
[74]
J. Strötgen, A.-L. Minard, L. Lange, M. Speranza, and B. Magnini, “KRAUTS: A German Temporally Annotated News Corpus,” in Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan, 2018.
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@inproceedings{StroetgenELREC2018, TITLE = {{KRAUTS}: {A German} Temporally Annotated News Corpus}, AUTHOR = {Str{\"o}tgen, Jannik and Minard, Anne-Lyse and Lange, Lukas and Speranza, Manuela and Magnini, Bernardo}, LANGUAGE = {eng}, ISBN = {979-10-95546-00-9}, URL = {http://lrec2018.lrec-conf.org/en/}, PUBLISHER = {ELRA}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, EDITOR = {Calzolari, Nicoletta and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Hasida, Koiti}, PAGES = {536--540}, ADDRESS = {Miyazaki, Japan}, }
Endnote
%0 Conference Proceedings %A Str&#246;tgen, Jannik %A Minard, Anne-Lyse %A Lange, Lukas %A Speranza, Manuela %A Magnini, Bernardo %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T KRAUTS: A German Temporally Annotated News Corpus : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002E-8B8C-E %U http://lrec2018.lrec-conf.org/en/ %D 2018 %B 11th Language Resources and Evaluation Conference %Z date of event: 2018-05-07 - 2018-05-12 %C Miyazaki, Japan %B Eleventh International Conference on Language Resources and Evaluation %E Calzolari, Nicoletta; Choukri, Khalid; Cieri, Christopher; Declerck, Thierry; Goggi, Sara; Hasida, Koiti %P 536 - 540 %I ELRA %@ 979-10-95546-00-9
[75]
H. Wu, Y. Ning, P. Chakraborty, J. Vreeken, N. Tatti, and N. Ramakrishnan, “Generating Realistic Synthetic Population Datasets,” ACM Transactions on Knowledge Discovery from Data, vol. 12, no. 4, 2018.
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@article{Wu_2018, TITLE = {Generating Realistic Synthetic Population Datasets}, AUTHOR = {Wu, Hao and Ning, Yue and Chakraborty, Prithwish and Vreeken, Jilles and Tatti, Nikolaj and Ramakrishnan, Naren}, LANGUAGE = {eng}, DOI = {10.1145/3182383}, PUBLISHER = {ACM}, ADDRESS = {New York, NY}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, JOURNAL = {ACM Transactions on Knowledge Discovery from Data}, VOLUME = {12}, NUMBER = {4}, PAGES = {1--22}, EID = {45}, }
Endnote
%0 Journal Article %A Wu, Hao %A Ning, Yue %A Chakraborty, Prithwish %A Vreeken, Jilles %A Tatti, Nikolaj %A Ramakrishnan, Naren %+ External Organizations External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T Generating Realistic Synthetic Population Datasets : %G eng %U http://hdl.handle.net/21.11116/0000-0002-16ED-B %R 10.1145/3182383 %7 2018 %D 2018 %J ACM Transactions on Knowledge Discovery from Data %O TKDD %V 12 %N 4 %& 1 %P 1 - 22 %Z sequence number: 45 %I ACM %C New York, NY
[76]
Y. Zhao, X. Shen, H. Senuma, and A. Aizawa, “A Comprehensive Study: Sentence Compression with Linguistic Knowledge-enhanced Gated Neural Network,” Data & Knowledge Engineering, vol. 117, 2018.
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@article{Zhao_2018, TITLE = {A Comprehensive Study: Sentence Compression with Linguistic Knowledge-enhanced Gated Neural Network}, AUTHOR = {Zhao, Yang and Shen, Xiaoyu and Senuma, Hajime and Aizawa, Akiko}, LANGUAGE = {eng}, ISSN = {0169-023X}, DOI = {10.1016/j.datak.2018.05.007}, PUBLISHER = {Elsevier}, ADDRESS = {Amsterdam}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, JOURNAL = {Data \& Knowledge Engineering}, VOLUME = {117}, PAGES = {307--318}, }
Endnote
%0 Journal Article %A Zhao, Yang %A Shen, Xiaoyu %A Senuma, Hajime %A Aizawa, Akiko %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T A Comprehensive Study: Sentence Compression with Linguistic Knowledge-enhanced Gated Neural Network : %G eng %U http://hdl.handle.net/21.11116/0000-0002-72D7-B %R 10.1016/j.datak.2018.05.007 %7 2018 %D 2018 %J Data & Knowledge Engineering %V 117 %& 307 %P 307 - 318 %I Elsevier %C Amsterdam %@ false