The importance of finding relevant information for business and decision making is imperative for both individuals as well as enterprises. In this paper, we present an approach for the development of a fuzzy information retrieval (IR) system. The approach provides a new mechanism for constructing and integrating three relevancy profiles comprising of: a task profile, user profile and document profile, into a unified index, through the use of relevance feedback and fuzzy rule based summarisation. Experiments were performed from which relevance feedback and user queries were captured from 35 users on 20 predefined simulated enterprise search tasks. The captured data set was used to develop the three types of profiles and train the fuzzy system. The system shows 86% performance accuracy in correctly classifying document relevance. The overall performance of the system was evaluated based on standard precision and recall which shows significant improvements in retrieving relevant documents based on user queries.
|Title of host publication||2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)|
|Publication status||Published - 2015|
|Event||IEEE International Conference on Fuzzy Systems - Istanbul, Turkey|
Duration: 2 Aug 2015 → 5 Aug 2015
|Conference||IEEE International Conference on Fuzzy Systems|
|Period||2/08/15 → 5/08/15|