Query expansion for document retrieval based on fuzzy rules and user relevance feedback techniques

In document retrieval systems, proper query terms significantly affect the performance of document retrieval systems. The performance of the systems can be improved by using query expansion techniques. In this paper, we present a new method for query expansion based on user relevance feedback techniques for mining additional query terms. According to the user's relevance feedback, the proposed query expansion method calculates the degrees of importance of relevant terms of documents in the document database. The relevant terms have higher degrees of importance may become additional query terms. The proposed method uses fuzzy rules to infer the weights of the additional query terms. Then, the weights of the additional query terms and the weights of the original query terms are used to form the new query vector, and we use this new query vector to retrieve documents. The proposed query expansion method increases the precision rates and the recall rates of information retrieval systems for dealing with document retrieval. It gets a higher average recall rate and a higher average precision rate than the method presented in Chang, Y. C., Chen, S. M., & Liau, C. J. (2003). A new query expansion method based on fuzzy rules. Proceedings of the Seventh Joint Conference on AI, Fuzzy System, and Grey System, Taipei, Taiwan, Republic of China.