Uncovering the semantics of Wikipedia categories

Heist, Nicolas ; Paulheim, Heiko

DOI: https://doi.org/10.1007/978-3-030-30793-6_13
URL: https://link.springer.com/chapter/10.1007/978-3-03...
Additional URL: https://arxiv.org/abs/1906.12089
Document Type: Conference or workshop publication
Year of publication: 2019
Book title: The Semantic Web - ISWC 2019 : 18th International Semantic Web Conference, Auckland, New Zealand, October 26–30, 2019, Proceedings, Part I
The title of a journal, publication series: Information Systems and Applications, incl. Internet/Web, and HCI
Volume: 11778
Page range: 219-236
Conference title: ISWC 2019
Location of the conference venue: Auckland, New Zealand
Date of the conference: October 26-30, 2019
Publisher: Ghidini, Chiara
Place of publication: Cham
Publishing house: Springer
ISBN: 978-3-030-30792-9 , 978-3-030-30793-6
Publication language: English
Institution: School of Business Informatics and Mathematics > Data Science (Paulheim 2018-)
Subject: 004 Computer science, internet
Abstract: The Wikipedia category graph serves as the taxonomic backbone for large-scale knowledge graphs like YAGO or Probase, and has been used extensively for tasks like entity disambiguation or semantic similarity estimation. Wikipedia's categories are a rich source of taxonomic as well as non-taxonomic information. The category 'German science fiction writers', for example, encodes the type of its resources (Writer), as well as their nationality (German) and genre (Science Fiction). Several approaches in the literature make use of fractions of this encoded information without exploiting its full potential. In this paper, we introduce an approach for the discovery of category axioms that uses information from the category network, category instances, and their lexicalisations. With DBpedia as background knowledge, we discover 703k axioms covering 502k of Wikipedia's categories and populate the DBpedia knowledge graph with additional 4.4M relation assertions and 3.3M type assertions at more than 87% and 90% precision, respectively.

Dieser Eintrag ist Teil der Universitätsbibliographie.

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