A context model for fuzzy concept analysis based upon modal logic

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 et 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.