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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


Raymond Y.K. Lau

Peter D. Bruza

Dawei Song


In an adaptive information retrieval (IR) setting, the information seekers' beliefs about which terms are relevant, or non-relevant, 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.


LAU, R.Y.K., BRUZA, P. and SONG, D. 2008. Towards a belief-revision-based adaptive and context-sensitive information retrieval system. ACM transactions on information systems [online], 26(2), article number 8. Available from:

Journal Article Type Article
Acceptance Date Mar 31, 2008
Online Publication Date Mar 31, 2008
Publication Date Mar 31, 2008
Deposit Date Aug 13, 2009
Publicly Available Date Aug 13, 2009
Journal ACM transactions on information systems
Print ISSN 1046-8188
Electronic ISSN 1558-2868
Publisher Association for Computing Machinery (ACM)
Peer Reviewed Peer Reviewed
Volume 26
Issue 2
Article Number 8
Keywords Theory; Algorithms; Experimentation; Belief revision; Retrieval context; Information flow; Text mining; Adaptive information retrieval
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