The Workshop on Term Subsumption Languages in Knowledge Representation was held 18-20 October 1989 at the Inn at Thorn Hill, located in the White Mountain region of New Hampshire. The workshop was organized by Peter F. Patel-Schneider of AT&T Bell Laboratories, Murray Hill, New Jersey; Marc Vilain of MITRE, Bedford, Massachusetts; Ramesh S. Patil of the Massachusetts Institute of Technology (MIT); and Bill Mark of the Lockheed AI Center, Menlo Park, California. Support was provided by the American Association for Artificial Intelligence and AT&T Bell Laboratories. This workshop was the latest in a series in this area. Previous workshops have had a slightly narrower focus, being explicitly concerned with KL-One, the first knowledge representation system based on a term subsumption language (TSL), or its successor, NIKL.
There is a tradeoff between expressive power and computational tractability in knowledge representation formalisms [Levesque and Brachman, 19851. If the formalism is expressively powerful, such as standard first-order logic, then reasoning in the formalism is time-consuming, perhaps even undecidable. This may make the formalism unsuitable as the basis of a knowledge representation system. Formalisms that are computationally tractable, such as standard databases, are much less expressive. Even many expressively limited formalisms are computationally intractable, as is standard propositional logic, which has NPcomplete reasoning. This tradeoff is present in frame-based description languages [Brachman and Levesque, 1984). These languages formalize the notion of frames, a notion present in many current knowledge representation systems, as structured types, often called concepts.
A major difficulty in developing and maintaining very large knowledge bases originates from the variety of forms in which knowledge is made available to the KB builder. The objective of this research is to bring together two complementary knowledge representation schemes: term subsumption languages, which represent and reason about defining characteristics of concepts, and proximate reasoning models, which deal with uncertain knowledge and data in expert systems. Previous works in this area have primarily focused on probabilistic inheritance. In this paper, we address two other important issues regarding the integration of term subsumption-based systems and approximate reasoning models. First, we outline a general architecture that specifies the interactions between the deductive reasoner of a term subsumption system and an approximate reasoner. Second, we generalize the semantics of terminological language so that terminological knowledge can be used to make plausible inferences. The architecture, combined with the generalized semantics, forms the foundation of a synergistic tight integration of term subsumption systems and approximate reasoning models.