TL;DR: Classical as mentioned in this paper is a recently developed knowledge representation system that concentrates on the definition of structured concepts, their organization into taxonomies, the creation and manipulation of individual instances of such concepts, and the key inferences of subsumption and classification.
Abstract: CLASSIC is a recently developed knowledge representation system that follows the paradigm originally set out in the KL-ONE system: It concentrates on the definition of structured concepts, their organization into taxonomies, the creation and manipulation of individual instances of such concepts, and the key inferences of subsumption and classification. Rather than simply presenting a description of CLASSIC, we complement a brief system overview with a discussion of how to live within the confines of a limited object-oriented deductive system. By analyzing the representational strengths and weaknesses of CLASSIC, we consider the circumstances under which it is most appropriate to use (or not use) it. We elaborate a knowledge engineering methodology for building KL-ONE-style knowledge bases, with emphasis on the modeling choices that arise in the process of describing a domain. We also address some of the key difficult issues encountered by new users, including primitive vs. defined concepts, and differences between roles and concepts, as well as representational “tricks-of-the-trade,” which we believe to be generally useful. Much of the discussion should be relevant to many of the current systems based on KL-ONE.
TL;DR: The KL-ONE family is introduced, an overview of current research is given, some of the systems that have been developed are described, and some future research directions are outlined.
Abstract: The knowledge representation system KL-ONE has been one of the most influential and imitated knowledge representation systems in the Artificial Intelligence community. Begun at Bolt Beranek and Newman in 1978, KL-ONE pioneered the development of taxonomic representations that can automatically classify and assimilate new concepts based on a criterion of terminological subsumption. This theme generated considerab interest in both the formal community and a large community of potential users. The KL-ONE community has since expanded to include many systems at many institutions and in many different countries. This paper introduces the KL-ONE family and discusses some of the main themes explored by KL-ONE and its successors. We give an overview of current research, describe some of the systems that have been developed, and outline some future research directions.
TL;DR: In this paper, the ontological foundations of the role/concept relationship are explored, and its implications on the practice of knowledge engineering are analyzed, and a formal semantics which binds these concepts to their corresponding relations is proposed.
Abstract: There is a subtle risk of ambiguity in the choice between concepts and roles forced by current KL-ONE-like languages, since many roles may be concepts as well. In this paper we explore the ontological foundations of the role/concept relationship, and analyze its implications on the practice of knowledge engineering. We criticize the current interpretation of KL-ONE roles as arbitrary relations, which vanishes their original meaning and makes them identical to slots. We suggest to call attributes those concepts which actually act as conceptual components, and propose a formal semantics which binds these concepts to their corresponding relations.
TL;DR: For this language, the effect of the three types of semantics introduced by (Nebel 1987,1989,1989a) can be completely described with the help of finite automata and provide an intuitive understanding of terminologies with cyclic definitions and insight into the essential features of the respective semantics.
Abstract: Cyclic definitions are often prohibited in terminological knowledge representation languages because, from a theoretical point of view, their semantics is not clear and, from a practical point of view, existing inference algorithms may go astray in the presence of cycles. In this paper, we shall consider terminological cycles in a very small KL-ONE-based language. For this language, the effect of the three types of semantics introduced by (Nebel 1987,1989,1989a) can be completely described with the help of finite automata. These descriptions provide a rather intuitive understanding of terminologies with cyclic definitions and give insight into the essential features of the respective semantics. In addition, one obtains algorithms and complexity results for subsumption determination. As it stands, the greatest fixed-point semantics comes off best. The characterization of this semantics is easy and has an obvious intuitive interpretation. Furthermore, important constructs - such as value-restriction with respect to the transitive or reflexive-transitive closure of a role - can easily be expressed.
TL;DR: Algorithms for hybrid inferences such as determining subsumption between concepts and checking the consistency of such a knowledge base are investigated.
Abstract: We investigate algorithms for hybrid inferences in KL-ONE-based knowledge representation systems. We employ two kinds of formalisms: the terminological and the assertional formalism. The terminological formalism consists of a concept description language to define concepts and relations between concepts for describing a terminology. On the other hand, the assertional formalism allows to introduce objects, which are instances of concepts and relations of a terminology. We present algorithms for hybrid inferences such as
determining subsumption between concepts
checking the consistency of such a knowledge base
computing the most specialized concepts an object is instance of
computing all objects that are instances of a certain concept.