Knowledge retrieval as limited inference

Abstract

Artificial intelligence reasoning systems commonly employ a knowledge base module that stores a set of facts expressed in a representation language and provides facilities to retrieve these facts. A retriever could range from a simple pattern matcher to a complete logical inference system. In practice, most fall in between these extremes, providing some forms of inference but not others. Unfortunately, most of these retrievers are not precisely defined.

We view knowledge retrieval as a limited form of inference operating on the stored facts. This paper is concerned with our method of using first-order predicate calculus to formally specify a limited inference mechanism and to a lesser extent with the techniques for producing an efficient program that meets the specification. Our ideas are illustrated by developing a simplified version of a retriever used in the knowledge base of the Rochester Dialog System. The interesting property of this retriever is that it perlorms typical semantic network inferences such as inheritance but not arbitrary logical inferences such as modus ponens.