Query Reformulation in Collaborative Information Retrieval

A. Hust, S. Klink, M. Junker,and A. Dengel (Germany)


Collaborative Information Retrieval, Query Expansion, Text Mining, Machine Learning


Information retrieval (IR) systems utilize user feedback for generating optimal queries with respect to a particular in formation need. However the methods that have been de veloped in IR for generating these queries do not memo rize information gathered from previous search processes, and hence can not use such information in new search pro cesses. Thus each new search process does not know any thing about previous search processes and can not profit from the results of the previous processes. We call sys tems which can consider results from previous search pro cesses Collaborative Information Retrieval (CIR) systems. Improving retrieval quality in a CIR system should be pos sible, since the system can learn from many queries issued from various users. In this paper we present a new method for use in CIR. We are proposing to use previously learned queries and their relevant documents for improving overall retrieval quality. Based on the similarity of a new query to previously learned queries we are expanding 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 ex pansion in contrast to local feedback information which has been used in previous work in query expansion methods.

Important Links:

Go Back