Temporal Learning of Semantic Relations between Concepts Using Web Repository

Xu, Z; Xuan, J

Temporal Learning of Semantic Relations between Concepts Using Web Repository

Xu, Z Xuan, J

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 2015 11th International Conference on Semantics, Knowledge and Grids, SKG 2015, 2016, pp. 239 - 243
Issue Date:: 2016-03-08

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Field	Value	Language
dc.contributor.author	Xu, Z	en_US
dc.contributor.author	Xuan, J https://orcid.org/0000-0002-8367-6908	en_US
dc.date.issued	2016-03-08	en_US
dc.identifier.citation	Proceedings - 2015 11th International Conference on Semantics, Knowledge and Grids, SKG 2015, 2016, pp. 239 - 243	en_US
dc.identifier.isbn	9781467398084	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120766
dc.description.abstract	© 2015 IEEE. In this paper, the study on generating temporal learning of relations between concepts is proposed. The purpose of the proposed study is to annotate a relation between concepts with semantic, temporal, concise, and structured information, which can release the cognitive burden of learning relations between concepts for users. The temporal annotations can help users to learn and understand the unfamiliar or new emerged relations between concepts. A general method is proposed to generate temporal learning of a relation between concepts by constructing its relation words, relation sentences, relation graph, and relation factor. Empirical experiments on movie star dataset show that the proposed algorithm is effective and accurate.	en_US
dc.relation.ispartof	Proceedings - 2015 11th International Conference on Semantics, Knowledge and Grids, SKG 2015	en_US
dc.relation.isbasedon	10.1109/SKG.2015.18	en_US
dc.title	Temporal Learning of Semantic Relations between Concepts Using Web Repository	en_US
dc.type	Conference Proceeding
utslib.for	0806 Information Systems	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	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2015 IEEE. In this paper, the study on generating temporal learning of relations between concepts is proposed. The purpose of the proposed study is to annotate a relation between concepts with semantic, temporal, concise, and structured information, which can release the cognitive burden of learning relations between concepts for users. The temporal annotations can help users to learn and understand the unfamiliar or new emerged relations between concepts. A general method is proposed to generate temporal learning of a relation between concepts by constructing its relation words, relation sentences, relation graph, and relation factor. Empirical experiments on movie star dataset show that the proposed algorithm is effective and accurate.

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http://hdl.handle.net/10453/120766