Building spatial temporal relation graph of concepts pair using web repository

Xu, Z; Xuan, J; Liu, Y; Choo, KKR; Mei, L; Hu, C

Building spatial temporal relation graph of concepts pair using web repository

Xu, Z Xuan, J

Liu, Y Choo, KKR Mei, L Hu, C

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Publisher:: Springer
Publication Type:: Journal Article
Citation:: Information Systems Frontiers: a journal of research and innovation, 2017, 19, (5), pp. 1029-1038
Issue Date:: 2017-10-01

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Field	Value	Language
dc.contributor.author	Xu, Z
dc.contributor.author	Xuan, J https://orcid.org/0000-0002-8367-6908
dc.contributor.author	Liu, Y
dc.contributor.author	Choo, KKR
dc.contributor.author	Mei, L
dc.contributor.author	Hu, C
dc.date.accessioned	2022-08-11T05:11:45Z
dc.date.available	2022-08-11T05:11:45Z
dc.date.issued	2017-10-01
dc.identifier.citation	Information Systems Frontiers: a journal of research and innovation, 2017, 19, (5), pp. 1029-1038
dc.identifier.issn	1387-3326
dc.identifier.issn	1572-9419
dc.identifier.uri	http://hdl.handle.net/10453/159933
dc.description.abstract	Mining semantic relations between concepts underlies many fundamental tasks including natural language processing, web mining, information retrieval, and web search. In order to describe the semantic relation between concepts, in this paper, the problem of automatically generating spatial temporal relation graph (STRG) of semantic relation between concepts is studied. The spatial temporal relation graph of semantic relation between concepts includes relation words, relation sentences, relation factor, relation graph, faceted feature, temporal feature, and spatial feature. The proposed method can automatically generate the spatial temporal relation graph (STRG) of semantic relation between concepts, which is different from the manually generated annotation repository such as WordNet and Wikipedia. Moreover, the proposed method does not need any prior knowledge such as ontology or the hierarchical knowledge base such as WordNet. Empirical experiments on real dataset show that the proposed algorithm is effective and accurate.
dc.language	English
dc.publisher	Springer
dc.relation.ispartof	Information Systems Frontiers: a journal of research and innovation
dc.relation.isbasedon	10.1007/s10796-016-9676-4
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0806 Information Systems
dc.subject.classification	Information Systems
dc.title	Building spatial temporal relation graph of concepts pair using web repository
dc.type	Journal Article
utslib.citation.volume	19
utslib.for	0806 Information Systems
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 - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-08-11T05:11:44Z
pubs.issue	5
pubs.publication-status	Published
pubs.volume	19
utslib.citation.issue	5

Abstract:

Mining semantic relations between concepts underlies many fundamental tasks including natural language processing, web mining, information retrieval, and web search. In order to describe the semantic relation between concepts, in this paper, the problem of automatically generating spatial temporal relation graph (STRG) of semantic relation between concepts is studied. The spatial temporal relation graph of semantic relation between concepts includes relation words, relation sentences, relation factor, relation graph, faceted feature, temporal feature, and spatial feature. The proposed method can automatically generate the spatial temporal relation graph (STRG) of semantic relation between concepts, which is different from the manually generated annotation repository such as WordNet and Wikipedia. Moreover, the proposed method does not need any prior knowledge such as ontology or the hierarchical knowledge base such as WordNet. Empirical experiments on real dataset show that the proposed algorithm is effective and accurate.

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http://hdl.handle.net/10453/159933