General purpose inference engine for canonical graph models

The design and implementation of a General Purpose Inference Engine for canonical graph models that is both flexible and efficient is addressed. Conventional inference techniques (e.g. forward chaining, backward chaining and mixed strategies) are described, and new modes of flexibility through the provision of inexact matching between data and assertions/rules are explained. In GPIE, scanning/searching of the rules in the rule base is restricted to a minimum during execution, but at the expense of compilation of the rule set prior to execution. The generality of the rule set is transparent to the inference engine, thereby permitting reasoning at various levels. This research demonstrates that a graph-based inference engine offering flexible control structures and inxact matching can complement intermediate notations, such as conceptual graphs, offering the expressive power of a rich knowledge representation formalism. The availability of an extendible graph processor for building appropriate canonical graph models presents the exciting prospect of a general purpose reasoning engine.