Rough set generalization models based information retrieval

Abstract

World Wide Web has become the largest pool of knowledge in terms of volume, but web users still continue to have difficulty in finding the relevant information. Two issues influence the output of information retrieval (IR) systems: first, information overload and second, the vagueness and imprecision in document representations as well as user information need. Rough set theory (RST) proved successful in tackling ambiguous, uncertain, imprecise, or incomplete information. RST is being used in a range of artificial intelligence applications as a result of this. RST has several uses, one of which is IR. This survey focused on Rough Sets and their generalization models in IR. Some previous studies attempted to comprehend rough set-based IR models, but their scope was limited. This research examines various rough set-based IR models, their basic methodologies, features, strengths, and limitations systematically. For critical analysis, a comparison of the existing frameworks is also presented.