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A Context Model for Constructing Membership Functions of Fuzzy Concepts Based on Modal Logic
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Information Sciences: an International Journal
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In this paper we present interesting relationships between the context model, modal logic and fuzzy concept analysis. It has been shown that the context model proposed by Gebhardt and Kruse [Int. J. Approx. Reason. 9 (1993) 283] can be semantically extended and considered as a data model for fuzzy concept analysis within the framework of the meta-theory developed by Resconi el al. in 1990s. Consequently, the context model provides a practical framework for constructing membership functions of fuzzy concepts and gives the basis for a theoretical justification of suitably use of well-known t-norm based connectives such as min-max and product-sum rules in applications. Furthermore, an interpretation of mass assignments of fuzzy concepts within the context model is also established.