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.
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.
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|Item Type:||Conference Paper|
|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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