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,.

DOI: https://doi.org/10.1145/1008992.1009017

URL: http://portal.acm.org/citation.cfm?id=1009017&coll...

Abstract

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.

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