Query expansion for web information retrieval

Abstract

Information retrieval (IR) systems utilize user feedback for generating optimal queries with respect to a particular information need. However the methods that have been developed in IR for generating these queries do not memorize information gathered from previous search processes, and hence can not use such information in new search processes. Thus a new search process can not profit from the results of the previous processes. Web Information Retrieval (WIR) systems should be able to maintain results from previous search processes, thus learning from previous queries and improving overall retrieval quality. In our approach we are using the similarity of a new query to previously learned queries. We then expand the new query by extracting terms from documents which have been judged as relevant to these previously learned queries. Thus our method uses global feedback information for query expansion in contrast to local feedback information which has been widely used in previous work in query expansion methods.