Large-scale Taxonomy Induction Using Entity and Word Embeddings

Autor:	Ristoski, Petar, Faralli, Stefano, Ponzetto, Simone Paolo, Paulheim, Heiko
Rok vydání:	2021
Předmět:	Computer Science - Artificial Intelligence Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	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. Comment: Published at IEEE/WIC/ACM International Conference on Web Intelligence 2017 (WI'17)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2105.01305 Zobrazit plný text záznamu View this record from Arxiv