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

Lau, Raymond; Bruza, Peter and Song, Dawei (2008). Towards a belief revision based adaptive and context sensitive information retrieval system. ACM Transactions on Information Systems (TOIS), 26(6) 8:1-8:38.

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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.

Item Type: Journal Item
Copyright Holders: 2008 ACM
ISSN: 1558-2868
Keywords: belief revision; retrieval context; information flow; text mining; adaptive information retrieval
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Item ID: 33896
Depositing User: Dawei Song
Date Deposited: 21 Jun 2012 14:19
Last Modified: 02 May 2018 13:41
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