Belief Revision for Adaptive Information Retrieval

Lau, Raymond Y. K., Bruza, Peter D., & Song, Dawei (2004) Belief Revision for Adaptive Information Retrieval. In 27th Annual ACM Conference of Research and Development in Information Retrieval (SIGIR 2004), 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 IRsystem 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 first successful empirical evaluation of a logic-based IR model based on large IR benchmark collections.

Impact and interest:

12 citations in Scopus
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ID Code: 11132
Item Type: Conference Paper
Refereed: Yes
ISBN: 1581138814
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Copyright 2004 ACM Press
Copyright Statement: Reproduced in accordance with the copyright policy of the publisher.
Deposited On: 06 Dec 2007 00:00
Last Modified: 29 Feb 2012 13:06

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