Bayesian rough set based information retrieval

Abstract

Information retrieval systems strive for catering user’s information need in terms of providing most relevant documents with regards to user’s query. Despite of decades of research in information retrieval field, users still struggle to meet their information need. There are two major challenges for information retrieval systems. First, vagueness in specifying user’s information need through query. Second, lack of effective methods to perform partial match between documents and query. In this work, Bayesian Rough Set based intelligent information retrieval model is proposed, which combines rough set theory and Bayesian reasoning. Both, user queries and web pages are presented in the form of rough sets. The approximation regions for query and documents are calculated using Bayesian Rough Set Model. Proposed model exploits rough relations for relevance ranking of web pages. Initial results of the proposed model are presented and demonstrate better performance than some of existing models.