Intelligent information retrieval

Abstract

In this paper an intelligent agent-based model for information retrieval is presented. The growing amount of online information and its dynamic nature forces us to reconsider existing passive approaches for information retrieval. Because of this ever-growing size of information sources the burden of retrieving information cannot be simply left on users. Our approach uses agent-based paradigm in order to handle this problem. Further in order to avoid users being overloaded with bulk of irrelevant information along with relevant ones and to improve ranking of the returned documents, we attempt to include semantics in making relevance judgment through conceptual graphs. We have first applied vector space model and then used conceptual graph to obtain final ranking. The results achieved show improved ranking of the returned documents.