Conceptual graphs as a universal knowledge representation

Abstract

Conceptual graphs are a knowledge representation language designed as a synthesis of several different traditions. First are the semantic networks, which have been used in machine translation and computational linguistics for over thirty years. Second are the logic-based techniques of unification, lambda calculus, and Peirce's existential graphs. Third is the linguistic research based on Tesnière's dependency graphs and various forms of case grammar and thematic relations. Fourth are the dataflow diagrams and Petri nets, which provide a computational mechanism for relating conceptual graphs to external procedures and databases. The result is a highly expressive system of logic with a direct mapping to and from natural languages. The lambda calculus supports the definitions for a taxonomic system and provides a general mechanism for restructuring knowledge bases. With the definitional mechanisms, conceptual graphs can be used an intermediate stage between natural languages and the rules and frames of expert systems—an important feature for knowledge acquisition and for help and explanations. During the past five years, conceptual graphs have been applied to almost every aspect of AI, ranging from expert systems and natural language to computer vision and neural networks. This paper surveys conceptual graphs, their development from each of these traditions, and the applications based on them.