A Novel Fuzzy Logic Model for Pseudo-Relevance Feedback-Based Query Expansion

Singh, J; Prasad, M; Prasad, OK; Meng Joo, E; Saxena, AK; Lin, CT

A Novel Fuzzy Logic Model for Pseudo-Relevance Feedback-Based Query Expansion

Singh, J Prasad, M

Prasad, OK Meng Joo, E Saxena, AK Lin, CT

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Publication Type:: Journal Article
Citation:: International Journal of Fuzzy Systems, 2016, 18 (6), pp. 980 - 989
Issue Date:: 2016-12-01

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Field	Value	Language
dc.contributor.author	Singh, J	en_US
dc.contributor.author	Prasad, M https://orcid.org/0000-0001-9719-2253	en_US
dc.contributor.author	Prasad, OK	en_US
dc.contributor.author	Meng Joo, E	en_US
dc.contributor.author	Saxena, AK	en_US
dc.contributor.author	Lin, CT https://orcid.org/0000-0001-8371-8197	en_US
dc.date.issued	2016-12-01	en_US
dc.identifier.citation	International Journal of Fuzzy Systems, 2016, 18 (6), pp. 980 - 989	en_US
dc.identifier.issn	1562-2479	en_US
dc.identifier.uri	http://hdl.handle.net/10453/113453
dc.description.abstract	© 2016, Taiwan Fuzzy Systems Association and Springer-Verlag Berlin Heidelberg. In this paper, a novel fuzzy logic-based expansion approach considering the relevance score produced by different rank aggregation approaches is proposed. It is well known that different rank aggregation approaches yield different relevance scores for each term. The proposed fuzzy logic approach combines different weights of each term by using fuzzy rules to infer the weights of the additional query terms. Experimental results demonstrate that the proposed approach achieves significant improvement over individual expansion, aggregated and other related state-of-the-arts methods.	en_US
dc.relation.ispartof	International Journal of Fuzzy Systems	en_US
dc.relation.isbasedon	10.1007/s40815-016-0254-1	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	A Novel Fuzzy Logic Model for Pseudo-Relevance Feedback-Based Query Expansion	en_US
dc.type	Journal Article
utslib.citation.volume	6	en_US
utslib.citation.volume	18	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0802 Computation Theory and Mathematics	en_US
utslib.for	0102 Applied Mathematics	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/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.issue	6	en_US
pubs.publication-status	Published	en_US
pubs.volume	18	en_US

Abstract:

© 2016, Taiwan Fuzzy Systems Association and Springer-Verlag Berlin Heidelberg. In this paper, a novel fuzzy logic-based expansion approach considering the relevance score produced by different rank aggregation approaches is proposed. It is well known that different rank aggregation approaches yield different relevance scores for each term. The proposed fuzzy logic approach combines different weights of each term by using fuzzy rules to infer the weights of the additional query terms. Experimental results demonstrate that the proposed approach achieves significant improvement over individual expansion, aggregated and other related state-of-the-arts methods.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/113453