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Towards a belief-revision-based adaptive and context-sensitive information retrieval system

Lau, Raymond Y.K., Bruza, Peter D., & Song, Dawei (2008) Towards a belief-revision-based adaptive and context-sensitive information retrieval system. ACM Transactions on Information Systems, 26(2).

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Abstract

In an adaptive information retrieval (IR) setting, the information seekers' beliefs about which terms are relevant or nonrelevant will naturally fluctuate. This article investigates how the theory of belief revision can be used to model adaptive IR. More specifically, belief revision logic provides a rich representation scheme to formalize retrieval contexts so as to disambiguate vague user queries. In addition, belief revision theory underpins the development of an effective mechanism to revise user profiles in accordance with information seekers' changing information needs. It is argued that information retrieval contexts can be extracted by means of the information-flow text mining method so as to realize a highly autonomous adaptive IR system. The extra bonus of a belief-based IR model is that its retrieval behavior is more predictable and explanatory. Our initial experiments show that the belief-based adaptive IR system is as effective as a classical adaptive IR system. To our best knowledge, this is the first successful implementation and evaluation of a logic-based adaptive IR model which can efficiently process large IR collections.

Impact and interest:

29 citations in Scopus
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1 citations in Web of Science®

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ID Code: 13709
Item Type: Journal Article
Additional Information: For more information, please refer to the journal's website (see hypertext link) or contact the author.
Keywords: Belief revision, adaptive information retrieval, information flow, retrieval context, text mining
ISSN: 1046-8188
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > LIBRARY AND INFORMATION STUDIES (080700) > Information Retrieval and Web Search (080704)
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Copyright 2008 Association for Computing Machinery (ACM) Press
Deposited On: 10 Jun 2008
Last Modified: 29 Feb 2012 23:49

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