Conceptualized Query for Information Retrieval

Many search engines are term-based information retrieval models. The disadvantage of this type of model is that it does not consider word sense. If we can represent the meanings of the terms that a user inputs, the IR system can retrieve the information the user really wants; not simply match the terms. To represent word sense, we proposed conceptual fuzzy sets (CFSs). A CFS is a framework that represents word concepts and that changes dynamically with fuzzy sets. In this paper, we experiment with concept retrieval for documents using conceptualized queries using CFSs. In our experiment, we evaluated our system on a large-scale corpus consisting of 1 million newswire text data. The experimental results showed that the performance of the IR system was improved. It also indicated that generating conceptualized queries is effective in an IR system.