Important issues in knowledge representation

Abstract

This paper discusses a number of important issues that drive knowledge representation research. It begins by considering the relationship between knowledge and the world and the use of knowledge by reasoning agents (both biological and mechanical) and concludes that a knowledge representation system must support activities of perception, learning, and planning to act. An argument is made that the mechanisms of traditional formal logic, while important to our understanding of mechanical reasoning, are not by themselves sufficient to solve all of the associated problems. In particular, notational aspects of a knowledge representation system are important--both for computational and conceptual reasons. Two such aspects are distinguished--expressive adequacy and notational efficacy. The paper also discusses the structure of conceptual representations and argues that taxonomic classification structures can advance both expressive adequacy and notational efficacy. It predicts that such techniques will eventually be applicable throughout computer science and that their application can produce a new style of programming--more oriented toward specifying the desired behavior in conceptual terms. Such "taxonomic programming" can have advantages for flexibility, extensibility, and maintainability, as well as for documentation and user education.