A rough-fuzzy document grading system for customized text information retrieval |
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| Institution: | 1. Kingston Business School, London and Ehrenberg-Bass Institute, UNISA, Australia;2. UNSW Business School, UNSW, Australia;3. Ehrenberg-Bass Institute, UNISA, Australia;4. Kingston Business School, London, UK;1. Department of Chemistry, College of Science, King Faisal university, P.O. Box 400 Al-Ahsa 31982, Saudi Arabia;2. Chemistry Department, Faculty of Science, Sohag University, Sohag-82534, Egypt;3. Chemistry Department, Faculty of Science, Benha University, 13518 Benha, Egypt |
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| Abstract: | Due to the large repository of documents available on the web, users are usually inundated by a large volume of information, most of which is found to be irrelevant. Since user perspectives vary, a client-side text filtering system that learns the user's perspective can reduce the problem of irrelevant retrieval. In this paper, we have provided the design of a customized text information filtering system which learns user preferences and modifies the initial query to fetch better documents. It uses a rough-fuzzy reasoning scheme. The rough-set based reasoning takes care of natural language nuances, like synonym handling, very elegantly. The fuzzy decider provides qualitative grading to the documents for the user's perusal. We have provided the detailed design of the various modules and some results related to the performance analysis of the system. |
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