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Belief Revision for Adaptive Information Retrieval

Lau, Raymond; Bruza, Peter and Song, Dawei (2004). Belief Revision for Adaptive Information Retrieval. In: 27th Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2004), 25-29 Jul 2004, Sheffield, UK,.

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Applying Belief Revision logic to model adaptive information retrieval is appealing since it provides a rigorous theoretical foundation to model partiality and uncertainty inherent in any information retrieval (IR) processes. In particular, a retrieval context can be formalised as a belief set and the formalised context is used to disambiguate vague user queries. Belief revision logic also provides a robust computational mechanism to revise an IR system's beliefs about the users' changing information needs. In addition, information flow is proposed as a text mining method to automatically acquire the initial IR contexts. The advantage of a belief-based IR system is that its IR behaviour is more predictable and explanatory. However, computational efficiency is often a concern when the belief revision formalisms are applied to large real-life applications. This paper describes our belief-based adaptive IR system which is underpinned by an efficient belief revision mechanism. Our initial experiments show that the belief-based symbolic IR model is more effective than a classical quantitative IR model. To our best knowledge, this is the rst successful empirical evaluation of a logic-based IR model based on large IR benchmark collections.

Item Type: Conference or Workshop Item
Extra Information: Published in Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, ISBN 1-58113-881-4 .
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Item ID: 6730
Depositing User: Users 4559 not found.
Date Deposited: 08 Feb 2007
Last Modified: 07 Dec 2018 09:01
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