Large-scale Taxonomy Induction Using Entity and Word Embeddings
- URL: http://arxiv.org/abs/2105.01305v1
- Date: Tue, 4 May 2021 05:53:12 GMT
- Title: Large-scale Taxonomy Induction Using Entity and Word Embeddings
- Authors: Petar Ristoski, Stefano Faralli, Simone Paolo Ponzetto and Heiko
Paulheim
- Abstract summary: We propose TIEmb, an approach for automatic subsumption extraction from knowledge using entity and text embeddings.
We apply the approach on the WebIsA database, a database of classes subsumption relations extracted from the large portion of Wide Web, to extract hierarchies in the Person and Place domain.
- Score: 13.30719395448771
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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