Using fuzzy centrality and intensity concepts to construct an information retrieval model

Traditional information retrieval techniques are quantitative approaches. That is, they only concern with finding information that completely or partially matches users' queries. However, the retrieval task will be unsatisfactoty if definite forms 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 while users cannot clearly describe part or whole features of their query specifications. User query which is viewed as a semantic entry, can belong to 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 combining the centrality distance and intensity distance between the query and targets in database. The total distance will take into account the confidence values of all the considered features. Since our model is qualitative approach, the system can capture what the users are hardly to express. The system can "see" what users' interests are, even if users cannot or don't know how to explicitly express what they have remembered in forms of queries.