Guest Editors' Introduction: Approaches to Knowledge Representation

In contrast to conventional database systems, AI systems require a knowledge base with diverse kinds of knowledge. These include, but are not limited to, knowledge about objects, knowledge about processes, and hard-to-represent commonsense knowledge about goals, motivation, causality, time, actions, etc. Attempts to represent this breadth of knowledge raise many questions: (1) How do we structure the explicit knowledge in a knowledge base? (2) How do we encode rules for manipulating a knowledge base's explicit knowledge to infer knowledge contained implicitly within the knowledge base? (3) When do we undertake and how do we control such inferences? (4) How do we formally specify the semantics of a knowledge base? (5) How do we deal with incomplete knowledge? (6) How do we extract the knowledge of an expert to initially "stock" the knowledge base? (7) How do we automatically acquire new knowledge as time goes on so that the knowledge base can be kept current? This special issue introduces this important area of artificial intelligence to a wider audience. The core of the 15 articles, contributed by a broad spectrum of researchers on various aspects of knowledge representation, show the importance, diversity, and vigor of knowledge representation as a research activity. This introduction provides some background and context to these articles by mapping out the basic approaches to knowledge representation that have developed over the years.