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A comparison of languages which operationalize and formalize KADS models of expertise

Published online by Cambridge University Press:  07 July 2009

Dieter Fensel  and
Frank van Harmelen
Dieter Fensel
Affiliation:
Institut für Angewandte Informatik und Formale Beschreibungsverfahern (AIFB), University of Karlsruhe, 76128 Karlsruhe, Germany (e-mail: [email protected])
Frank van Harmelen
Affiliation:
Department of Social Science Informatics (SWI), University of Amsterdam, Roeterstraat 15, NL-1018 Amsterdam, The Netherlands (e-mail: [email protected])
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Abstract

In the field of knowledge engineering, dissatisfaction with the rapid-prototyping approach has led to a number of more principled methodologies for the contruction of knowledge-based systems. Instead of immediately implementing the gathered and interpreted knowledge in a given implementation formalism according to the rapid-prototyping approach, many such methodologies centre around the notion of a conceptual model: an abstract, implementation independent description of the relevant problem solving expertise. A conceptual model should describe the task which is solved by the system and the knowledge which is required by it. Although such conceptual models more precisely, and operationally as a means for model evaluation. In this paper, we study a number of such formal and operational languages for specifying conceptual models. To enable a meaningful comparison of such languages, we focus on languages which are all aimed at the same underlying conceptual model, namely that from the KADS method for building KBS. We describe eight formal languages for KADS models of expertise, and compare these languages with respect to their modelling primitives, their semantics, their implementations and their applications, Future research issues in the area of formal and operational specification languages for KBS are identified as the result of studying these languages. The paper also contains an extensive bibliography of research in this area.

Type
Research Article
Information
The Knowledge Engineering Review , Volume 9 , Issue 2 , June 1994 , pp. 105 - 146
Copyright
Copyright © Cambridge University Press 1994

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