%0 Conference Proceedings %A Arnaout, Hiba %A Nguyen, Tuan-Phong %A Razniewski, Simon %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T UnCommonSense in Action! Informative Negations for Commonsense Knowledge Bases : %G eng %U http://hdl.handle.net/21.11116/0000-000C-18BC-6 %R 10.1145/3539597.3573027 %D 2023 %B 16th ACM International Conference on Web Search and Data Mining %Z date of event: 2023-02-27 - 2023-03-03 %C Singapore %B WSDM '23 %E Chua, Tat-Seng; Lauw, Hady; Si, Luo; Terzi, Evimaria; Tsaparas, Panayiotis %P 1120 - 1123 %I ACM %@ 978-1-4503-9407-9

%0 Conference Proceedings %A Boualili, Lila %A Yates, Andrew %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T A Study of Term-Topic Embeddings for Ranking : %G eng %U http://hdl.handle.net/21.11116/0000-000C-DC34-2 %R 10.1007/978-3-031-28238-6_25 %D 2023 %B 45th European Conference on IR Research %Z date of event: 2023-04-02 - 2023-04-06 %C Dublin, Ireland %B Advances in Information Retrieval %E Kamps, Jaap; Goeuriot, Lorraine; Crestani, Fabio; Maistro, Maria; Joho, Hideao; Davis, Brian; Gurrin, Cathal; Kruschwitz, Udo; Caputo, Annalina %P 359 - 366 %I Springer %@ 978-3-031-28237-9 %B Lecture Notes in Computer Science %N 13981

%0 Conference Proceedings %A Christmann, Philipp %A Saha Roy, Rishiraj %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T CLOCQ: A Toolkit for Fast and Easy Access to Knowledge Bases : %G eng %U http://hdl.handle.net/21.11116/0000-000C-BF14-7 %R 10.18420/BTW2023-28 %D 2023 %B 20th Conference on Database Systems for Business, Technology and Web %Z date of event: 2023-03-06 - 2023-03-10 %C Dresden, Germany %B BTW 2023 %E König-Ries, Birgitta; Scherzinger, Stefanie; Lehner, Wolfgang; Vossen, Gottfried %P 579 - 591 %I GI %@ 978-3-88579-725-8 %B Lecture Notes in Informatics %N P-331

%0 Conference Proceedings %A Ghazimatin, Azin %+ Databases and Information Systems, MPI for Informatics, Max Planck Society %T Enhancing Explainability and Scrutability of Recommender Systems : %G eng %U http://hdl.handle.net/21.11116/0000-000C-DC4D-7 %R 10.18420/BTW2023-32 %D 2023 %B 20th Conference on Database Systems for Business, Technology and Web %Z date of event: 2023-03-06 - 2023-03-10 %C Dresden, Germany %B BTW 2023 %E König-Ries, Birgitta; Scherzinger, Stefanie; Lehner, Wolfgang; Vossen, Gottfried %P 633 - 640 %I GI %@ 978-3-88579-725-8 %B Lecture Notes in Informatics %N P-331

%0 Journal Article %A Ghosh, Shrestha %A Razniewski, Simon %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Answering Count Questions with Structured Answers from Text : %G eng %U http://hdl.handle.net/21.11116/0000-000C-47CB-0 %R 10.1016/j.websem.2022.100769 %7 2022 %D 2023 %J Journal of Web Semantics %V 76 %Z sequence number: 100769 %I Elsevier %C Amsterdam

%0 Conference Proceedings %A Ghosh, Shrestha %A Razniewski, Simon %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T CoQEx: Entity Counts Explained : %G eng %U http://hdl.handle.net/21.11116/0000-000B-F41F-0 %R 10.1145/3539597.3573021 %D 2023 %B 16th ACM International Conference on Web Search and Data Mining %Z date of event: 2023-02-27 - 2023-03-03 %C Singapore %B WSDM '23 %E Chua, Tat-Seng; Lauw, Hady; Si, Luo; Terzi, Evimaria; Tsaparas, Panayiotis %P 1168 - 1171 %I ACM %@ 978-1-4503-9407-9

%0 Conference Proceedings %A Ghosh, Shrestha %A Razniewski, Simon %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Class Cardinality Comparison as a Fermi Problem : %U http://hdl.handle.net/21.11116/0000-000C-BF05-8 %U https://arxiv.org/abs/2303.04532 %D 2023 %B ACM Web Conference %Z date of event: 2023-04-30 - 2023-05-04 %C Austin, TX, USA %X Questions on class cardinality comparisons are quite tricky to answer and<br>come with its own challenges. They require some kind of reasoning since web<br>documents and knowledge bases, indispensable sources of information, rarely<br>store direct answers to questions, such as, ``Are there more astronauts or<br>Physics Nobel Laureates?'' We tackle questions on class cardinality comparison<br>by tapping into three sources for absolute cardinalities as well as the<br>cardinalities of orthogonal subgroups of the classes. We propose novel<br>techniques for aggregating signals with partial coverage for more reliable<br>estimates and evaluate them on a dataset of 4005 class pairs, achieving an<br>accuracy of 83.7%.<br> %K Computer Science, Information Retrieval, cs.IR,Computer Science, Artificial Intelligence, cs.AI %B WWW '23

%0 Conference Proceedings %A Nguyen, Tuan-Phong %A Razniewski, Simon %A Varde, Aparna %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 Extracting Cultural Commonsense Knowledge at Scale : %G eng %U http://hdl.handle.net/21.11116/0000-000C-9FF7-B %R 10.1145/3543507.3583535 %D 2023 %B ACM Web Conference %Z date of event: 2023-04-30 - 2023-05-04 %C Austin, TX, USA %B WWW '23 %I ACM %@ 978-1-4503-9416-1

%0 Conference Proceedings %A Torbati, Ghazaleh Haratinezhad %A Weikum, Gerhard %A Yates, Andrew %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Search-based Recommendation : The Case for Difficult Predictions : %G eng %U http://hdl.handle.net/21.11116/0000-000C-DC45-F %D 2023 %B ACM Web Conference %Z date of event: 2023-04-30 - 2023-05-04 %C Austin, TX, USA %X Questions on class cardinality comparisons are quite tricky to answer and<br>come with its own challenges. They require some kind of reasoning since web<br>documents and knowledge bases, indispensable sources of information, rarely<br>store direct answers to questions, such as, ``Are there more astronauts or<br>Physics Nobel Laureates?'' We tackle questions on class cardinality comparison<br>by tapping into three sources for absolute cardinalities as well as the<br>cardinalities of orthogonal subgroups of the classes. We propose novel<br>techniques for aggregating signals with partial coverage for more reliable<br>estimates and evaluate them on a dataset of 4005 class pairs, achieving an<br>accuracy of 83.7%.<br> %K Computer Science, Information Retrieval, cs.IR,Computer Science, Artificial Intelligence, cs.AI %B WWW '23

%0 Conference Proceedings %A Veseli, Blerta %A Singhania, Sneha %A Razniewski, Simon %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Evaluating Language Models for Knowledge Base Completion : %G eng %U http://hdl.handle.net/21.11116/0000-000C-DC39-D %D 2023 %B The European Semantic Web Conference %Z date of event: 2023-05-28 - 2023-06-01 %C Hersonissos, Greece %B The Semantic Web %I Springer %B Lecture Notes in Computer Science

%0 Report %A Veseli, Blerta %A Singhania, Sneha %A Razniewski, Simon %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Evaluating Language Models for Knowledge Base Completion : %G eng %U http://hdl.handle.net/21.11116/0000-000C-D3CD-F %U https://arxiv.org/abs/2303.11082 %D 2023 %X Structured knowledge bases (KBs) are a foundation of many intelligent<br>applications, yet are notoriously incomplete. Language models (LMs) have<br>recently been proposed for unsupervised knowledge base completion (KBC), yet,<br>despite encouraging initial results, questions regarding their suitability<br>remain open. Existing evaluations often fall short because they only evaluate<br>on popular subjects, or sample already existing facts from KBs. In this work,<br>we introduce a novel, more challenging benchmark dataset, and a methodology<br>tailored for a realistic assessment of the KBC potential of LMs. For automated<br>assessment, we curate a dataset called WD-KNOWN, which provides an unbiased<br>random sample of Wikidata, containing over 3.9 million facts. In a second step,<br>we perform a human evaluation on predictions that are not yet in the KB, as<br>only this provides real insights into the added value over existing KBs. Our<br>key finding is that biases in dataset conception of previous benchmarks lead to<br>a systematic overestimate of LM performance for KBC. However, our results also<br>reveal strong areas of LMs. We could, for example, perform a significant<br>completion of Wikidata on the relations nativeLanguage, by a factor of ~21<br>(from 260k to 5.8M) at 82% precision, usedLanguage, by a factor of ~2.1 (from<br>2.1M to 6.6M) at 82% precision, and citizenOf by a factor of ~0.3 (from 4.2M to<br>5.3M) at 90% precision. Moreover, we find that LMs possess surprisingly strong<br>generalization capabilities: even on relations where most facts were not<br>directly observed in LM training, prediction quality can be high.<br> %K Computer Science, Computation and Language, cs.CL,Computer Science, Artificial Intelligence, cs.AI %U https://github.com/bveseli/LMsForKBC