Fuzzy Conceptual Graph Programming

Overview

As logic programming made logic applicable to computer science as a programming language, CG programming was studied and developed for CG-based automated reasoning systems. Fargues et al. (1986) was among the first works on the particular subset of conceptual graphs that corresponded to definite clauses in predicate logic (Lloyd 1987) and on implementation of a Prolog-like resolution method for it. The authors considered CG clauses of the form G ←G₁, G₂, ..., G _n , where G₁, G₂, ..., G _n and G were connected CGs, and defined a CG program (CGP) as a finite set of CG clauses.

Recognizing that CG projection was not adequate for matching a goal with the head of a rule in a CGP resolution proof procedure, the authors introduced a matching operation based on the notion of compatible concepts. Like unification in predicate logic, that operation could match an individual concept in a goal and a generic concept in the head of a rule. It was then used to define an SLD-resolution style proof procedure for CG programs. Rao and Foo (1987) refined and extended that work with the introduction of modalities to CG programs.