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 PaperResearchpeer-review

84 Citations (Scopus)


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 publicationNAACL HLT 2016 - The 2016 Conference of the North American Chapter of the Association for Computational Linguistics
Subtitle of host publicationHuman Language Technologies, Proceedings of the Conference
EditorsAni Nenkova, Owen Rambow
Place of PublicationStroudsburg PA USA
PublisherAssociation for Computational Linguistics (ACL)
Number of pages7
ISBN (Electronic)9781941643914
Publication statusPublished - 2016
Externally publishedYes
EventNorth American Association for Computational Linguistics 2016: Human Language Technologies - Sheraton San Diego Hotel & Marina, San Diego, United States of America
Duration: 12 Jun 201617 Jun 2016
Conference number: 15th


ConferenceNorth American Association for Computational Linguistics 2016
Abbreviated titleNAACL HLT 2016
CountryUnited States of America
CitySan Diego
Internet address

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