A new fuzzy logic based ranking function for efficient Information Retrieval system. Issue 3 (15th February 2015)

• Record Type: Journal Article
• Authors: Gupta, Yogesh; Saini, Ashish; Saxena, A.K.
• Is Part Of: Expert systems with applications. Volume 42:Issue 3(2015)
• Page Start: 1223
• Page End: 1234
• Publication Date: 2015-02-15
• Subjects: Information Retrieval -- Fuzzy Logic Controller -- Precision -- Recall -- Ranking function; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2014.09.009
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A fuzzy logic based ranking function is proposed to enhance Information Retrieval. It is based on Composite Fuzzy Inference System with 259 fuzzy rules. The performance is tested in terms of precision, recall and F-measure on CACM and CISI . The better results are obtained as compared to Okapi-BM25 and Rubens' approach. A paired t-test is also conducted to perform statistical analysis. Abstract: The relevant documents from large data sets are retrieved with the help of ranking function in Information Retrieval system. In this paper, a new fuzzy logic based ranking function is proposed and implemented to enhance the performance of Information Retrieval system. The proposed ranking function is based on the computation of different terms of term-weighting schema such as term frequency, inverse document frequency and normalization. Fuzzy logic is used at two levels to compute relevance score of a document with respect to the query in present work. All the experiments are performed on CACM and CISI benchmark data sets. The experimental results reveal that the performance of our proposed ranking function is much better than the fuzzy based ranking function developed by Rubens along with other widely used ranking function Okapi-BM25 in terms of precision, recall and F-measure.