Structured embedding via pairwise relations and long-range interactions in knowledge base

Wu, F; Song, J; Yang, Y; Li, X; Zhang, Z; Zhuang, Y

Structured embedding via pairwise relations and long-range interactions in knowledge base

Wu, F Song, J Yang, Y

Li, X Zhang, Z Zhuang, Y

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the National Conference on Artificial Intelligence, 2015, 2 pp. 1663 - 1670
Issue Date:: 2015-06-01

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Field	Value	Language
dc.contributor.author	Wu, F	en_US
dc.contributor.author	Song, J	en_US
dc.contributor.author	Yang, Y https://orcid.org/0000-0001-5528-0546	en_US
dc.contributor.author	Li, X	en_US
dc.contributor.author	Zhang, Z	en_US
dc.contributor.author	Zhuang, Y	en_US
dc.date.issued	2015-06-01	en_US
dc.identifier.citation	Proceedings of the National Conference on Artificial Intelligence, 2015, 2 pp. 1663 - 1670	en_US
dc.identifier.isbn	9781577357001	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121624
dc.description.abstract	Copyright © 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. We consider the problem of embedding entities and relations of knowledge bases into low-dimensional continuous vector spaces (distributed representations). Unlike most existing approaches, which are primarily efficient for modelling pairwise relations between entities, we attempt to explicitly model both pairwise relations and long-range interactions between entities, by interpreting them as linear operators on the low-dimensional embeddings of the entities. Therefore, in this paper we introduces path ranking to capture the long-range interactions of knowledge graph and at the same time preserve the pairwise relations of knowledge graph; we call it structured embedding via pairwise relation and /ong-range interactions (referred to as SePLi). Comparing with the-state-of-the-art models, SePLi achieves better performances of embeddings.	en_US
dc.relation.ispartof	Proceedings of the National Conference on Artificial Intelligence	en_US
dc.title	Structured embedding via pairwise relations and long-range interactions in knowledge base	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	2	en_US

Abstract:

Copyright © 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. We consider the problem of embedding entities and relations of knowledge bases into low-dimensional continuous vector spaces (distributed representations). Unlike most existing approaches, which are primarily efficient for modelling pairwise relations between entities, we attempt to explicitly model both pairwise relations and long-range interactions between entities, by interpreting them as linear operators on the low-dimensional embeddings of the entities. Therefore, in this paper we introduces path ranking to capture the long-range interactions of knowledge graph and at the same time preserve the pairwise relations of knowledge graph; we call it structured embedding via pairwise relation and /ong-range interactions (referred to as SePLi). Comparing with the-state-of-the-art models, SePLi achieves better performances of embeddings.

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http://hdl.handle.net/10453/121624