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.
Since the development of this system more than a dozen similar representation systems have been developed by various research groups. These systems vary along a number of dimensions. In this paper, we present the results of an empirical analysis of six such systems. Surprisingly, the systems turned out to be quite diverse leading to problems when transporting knowledge bases from one system to another. Additionally, the runtime performance between different systems and knowledge bases varied more than we expected. Finally, our empirical runtime performance results give an idea of what runtime performance to expect from such representation systems. These findings complement previously reported analytical results about the computational complexity of reasoning in such systems.
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.
NIKL (a New Implementation of KL-ONE) is one of the members of the KL-ONE family of knowledge representation languages. NIKL has been in use for several years and our experiences have led us to define and implement various extensions to the language, its support environment and the implementation. Our experiences are particular to the use of NIKL. However, the requirements that we have discovered are relevant to any intelligent system that must reason about terminology. This article reports on the extensions that we have found necessary based on experiences in several different testbeds. The motivations for the extensions and future plans are also presented.
Even though specificity has been one of the most useful conflict resolution strategies for selecting productions, most existing rule-based systems use heuristic approximation such as the number of clauses to measure a rule's specificity. This paper describes an approach for computing a principled specificity relation between rules whose conditions are constructed using predicates defined in a terminological knowledge base. Based on a formal definition about pattern subsumption relation, we first show that a subsumption test between two conjunctive patterns can be viewed as a search problem. Then we describe an implemented pattern classification algorithm that improves the efficiency of the search process by deducing implicit conditions logically implied by a pattern and by reducing the search space using subsumption relationships between predicates. Our approach enhances the maintainability of rule-based systems and the reusability of definitional knowledge.