A Model of Information Retrieval System with Unbalanced Fuzzy Linguistic Information

Most information retrieval systems based on linguistic approaches use symmetrically and uniformly distributed linguistic term sets to express the weights of queries and the relevance degrees of documents. However, to improve the system-user interaction it seems more adequate to express these linguistic weights and degrees by means of unbalanced linguistic scales, i.e., linguistic term sets with different discrimination levels on both sides of mid linguistic term. In this contribution we present an information retrieval system which accepts weighted queries whose weights are expressed using unbalanced linguistic term sets. Then, system provides the retrieved documents classified in linguistic relevance classes assessed on unbalanced linguistic term sets. To do so, we propose a methodology to manage unbalanced linguistic information and we use the linguistic 2-tuple model as representation base of the unbalanced linguistic information. Additionally, the linguistic 2-tuple model allows us to increase the number of relevance classes in the output and also to improve the performance of information retrieval system.