A New Similarity Function for Information Retrieval based on Fuzzy Logic

In this paper, a novel approach is presented to construct a similarity function to make information retrieval efficient. This approach is based on different terms of term-weighting schema like term frequency, inverse document frequency and normalization. The proposed similarity function uses fuzzy logic to determine similarity score of a document against a query. All the experiments are done with CACM benchmark data collection. The experimental results reveal that the performance of proposed similarity function is much better than the fuzzy based ranking function developed by Rubens along with other widely used similarity function Okapi-BM25 in terms of precision rate and recall rate.