Large-scale taxonomy induction using entity and word embeddings

Ristoski, Petar ; Faralli, Stefano ; Ponzetto, Simone Paolo ; Paulheim, Heiko

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Document Type: Conference or workshop publication
Year of publication: 2017
Book title: WI 2017 : proceedings of the International Conference on Web Intelligence, Leipzig, Germany, August 23-26, 2017
Page range: 81-87
Conference title: International Conference on Web Intelligence 2017
Location of the conference venue: Leipzig, Germany
Date of the conference: August 23-26, 2017
Publisher: Sheth, Amit
Place of publication: New York, NY
Publishing house: ACM
ISBN: 978-1-4503-4951-2
Publication language: English
Institution: School of Business Informatics and Mathematics > Wirtschaftsinformatik III (Ponzetto 2016-)
School of Business Informatics and Mathematics > Web Data Mining (Juniorprofessur) (Paulheim 2013-2017)
Subject: 004 Computer science, internet
Individual keywords (German): entity embeddings , ontology induction , text embeddings
Abstract: Taxonomies are an important ingredient of knowledge organization, and serve as a backbone for more sophisticated knowledge representations in intelligent systems, such as formal ontologies. However, building taxonomies manually is a costly endeavor, and hence, automatic methods for taxonomy induction are a good alternative to build large-scale taxonomies. In this paper, we propose TIEmb, an approach for automatic unsupervised class subsumption axiom extraction from knowledge bases using entity and text embeddings. We apply the approach on the WebIsA database, a database of subsumption relations extracted from the large portion of the World Wide Web, to extract class hierarchies in the Person and Place domain.

Dieser Eintrag ist Teil der Universitätsbibliographie.

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