STransE: a novel embedding model of entities and relationships in knowledge bases

Dat Quoc Nguyen, Kairit Sirts, Lizhen Qu, Mark Johnson

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

147 Citations (Scopus)
114 Downloads (Pure)

Abstract

Knowledge bases of real-world facts about entities and their relationships are useful resources for a variety of natural language processing tasks. However, because knowledge bases are typically incomplete, it is useful to be able to perform link prediction, i.e., predict whether a relationship not in the knowledge base is likely to be true. This paper combines insights from several previous link prediction models into a new embedding model STransE that represents each entity as a low-dimensional vector, and each relation by two matrices and a translation vector. STransE is a simple combination of the SE and TransE models, but it obtains better link prediction performance on two benchmark datasets than previous embedding models. Thus, STransE can serve as a new baseline for the more complex models in the link prediction task.

Original languageEnglish
Title of host publication2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2016 - Proceedings of the Conference
Place of PublicationRed Hook, New York
PublisherAssociation for Computational Linguistics (ACL)
Pages460-466
Number of pages7
ISBN (Electronic)9781941643914
DOIs
Publication statusPublished - 2016
Event15th Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2016 - San Diego, United States
Duration: 12 Jun 201617 Jun 2016

Other

Other15th Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2016
Country/TerritoryUnited States
CitySan Diego
Period12/06/1617/06/16

Bibliographical note

Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

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