Fuzzy Conceptual Graphs: A Language for Computational Intelligence Approaching Human Expression and Reasoning
Conceptual graphs and fuzzy logic are two logical formalisms that emphasize the target of natural language, where conceptual graphs provide a structure of formulas close to that of natural language sentences while fuzzy logic provides a methodology for computing with words. This paper proposes fuzzy conceptual graphs as a knowledge representation language that combines the advantages of both the two formalisms for computational intelligence approaching human expression and reasoning. Firstly, simple fuzzy conceptual graphs are defined as bipartite graphs of concepts alternate with conceptual relations. Fuzzy types are introduced to represent uncertainty and/or partial truth about concept or relation types. For representing complex information, simple fuzzy conceptual graphs are extended to nested fuzzy conceptual graphs. Then, as a basic operation for inference, the projection operation that matches a (nested) fuzzy conceptual graph to another one and measures the relative necessity degree of the former given the latter is defined.