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Traditional information retrieval techniques are quantitative approaches. That is, if the only concern is to find information that completely or partially matches users' queries. However, the retrieval task is unsatisfactory if the definite forms are not easily or possibly represent the semantic contents of the queries. Thus, we propose a fuzzy information retrieval model that can "understand" users' queries especially when the users cannot clearly describe the part or the whole features of their query specifications. User query which is viewed as a semantic entry, could belong to the multiple semantic categories and by introducing two fuzzy measure degrees, centrality and intensity, our model is capable of dealing with the ambiguity in user query. The matching policy is based on the combining centrality distance and the intensity distance between the query and the targets in database. The total distance is taking into account the confidence values of all the considered features. Since the model is qualitative approach, the system can capture what the users' can hardly express. The system can "see" what users' interest are, even if the user cannot or don't know how to explicitly express what they have remembered in the form of queries.