Information extraction from co-occurring similar entities

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DOI:	https://doi.org/10.1145/3442381.3449836
URL:	https://arxiv.org/abs/2102.05444
Weitere URL:	https://www.researchgate.net/publication/349195425...
URN:	urn:nbn:de:bsz:180-madoc-597645
Dokumenttyp:	Konferenzveröffentlichung
Erscheinungsjahr:	2021
Buchtitel:	The Web Conference 2021 : Proceedings of the World Wide Web Conference WWW 2021
Seitenbereich:	3999-4009
Veranstaltungstitel:	WWW '21
Veranstaltungsort:	Ljubljana Slovenia
Veranstaltungsdatum:	19.04.2021
Herausgeber:	Leskovec, Jure ; Grobelnik, Marko ; Najork, Marc ; Zia, Leila ; Tang, Jie
Ort der Veröffentlichung:	New York, NY [u.a.]
Verlag:	Association for Computing Machinery
ISBN:	978-1-4503-8312-7
Sprache der Veröffentlichung:	Englisch
Einrichtung:	Fakultät für Wirtschaftsinformatik und Wirtschaftsmathematik > Data Science (Paulheim 2018-)
Bereits vorhandene Lizenz:	Creative Commons Namensnennung 4.0 International (CC BY 4.0)
Fachgebiet:	004 Informatik
Abstract:	Knowledge about entities and their interrelations is a crucial factor of success for tasks like question answering or text summarization. Publicly available knowledge graphs like Wikidata or DBpedia are, however, far from being complete. In this paper, we explore how information extracted from similar entities that co-occur in structures like tables or lists can help to increase the coverage of such knowledge graphs. In contrast to existing approaches, we do not focus on relationships within a listing (e.g., between two entities in a table row) but on the relationship between a listing’s subject entities and the context of the listing. To that end, we propose a descriptive rule mining approach that uses distant supervision to derive rules for these relationships based on a listing’s context. Extracted from a suitable data corpus, the rules can be used to extend a knowledge graph with novel entities and assertions. In our experiments we demonstrate that the approach is able to extract up to 3M novel entities and 30M additional assertions from listings in Wikipedia. We find that the extracted information is of high quality and thus suitable to extend Wikipedia-based knowledge graphs like DBpedia, YAGO, and CaLiGraph. For the case of DBpedia, this would result in an increase of covered entities by roughly 50%.