Belief revision for adaptive information retrieval

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 beliefbased 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 beliefbased adaptive IR system which is underpinned by an efficient belief revision mechanism. Our ini...

• Type: Conference
• Year: 2004
• Where: SIGIR
• Authors: Raymond Y. K. Lau, Peter Bruza, Dawei Song
• Keywords: Belief Revision, Belief Revision Logic, IR Model, SIGIR 2004