A comparative analysis of fuzzy logic based query expansion approaches for document retrieval

Abstract

Query expansion is one of the techniques to find suitable terms for redefining the queries so that the document retrieval performance can be enhanced. This paper presents a comparative analysis of recently developed query expansion approaches using fuzzy logic to retrieve relevant documents from large datasets for a given user query. In this paper, two query expansion approaches are compared and analyzed in different manner for two benchmark datasets: CISI and CACM. Both the approaches are based on fuzzy logic and term selection methods. On the basis performance evaluating parameters such as precision, recall, MAP and precision-recall graph, it is found that the approach proposed in [13] improves document retrieval in comparison to the approach proposed in [32].