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Configuration knowledge representations for Semantic Web applications

Published online by Cambridge University Press:  07 August 2003

ALEXANDER FELFERNIG ,
GERHARD FRIEDRICH ,
DIETMAR JANNACH ,
MARKUS STUMPTNER  and
MARKUS ZANKER
ALEXANDER FELFERNIG
Affiliation:
Institut für Wirtschaftsinformatik und Anwendungssysteme, Produktionsinformatik, Klagenfurt, Austria
GERHARD FRIEDRICH
Affiliation:
Institut für Wirtschaftsinformatik und Anwendungssysteme, Produktionsinformatik, Klagenfurt, Austria
DIETMAR JANNACH
Affiliation:
Institut für Wirtschaftsinformatik und Anwendungssysteme, Produktionsinformatik, Klagenfurt, Austria
MARKUS STUMPTNER
Affiliation:
University of South Australia, Advanced Computing Research Centre, Adelaide, Australia
MARKUS ZANKER
Affiliation:
Institut für Wirtschaftsinformatik und Anwendungssysteme, Produktionsinformatik, Klagenfurt, Austria
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Abstract

Today's economy exhibits a growing trend toward highly specialized solution providers cooperatively offering configurable products and services to their customers. This paradigm shift requires the extension of current standalone configuration technology with capabilities of knowledge sharing and distributed problem solving. In this context a standardized configuration knowledge representation language with formal semantics is needed in order to support knowledge interchange between different configuration environments. Languages such as Ontology Inference Layer (OIL) and DARPA Agent Markup Language (DAML+OIL) are based on such formal semantics (description logic) and are very popular for knowledge representation in the Semantic Web. In this paper we analyze the applicability of those languages with respect to configuration knowledge representation and discuss additional demands on expressivity. For joint configuration problem solving it is necessary to agree on a common problem definition. Therefore, we give a description logic based definition of a configuration problem and show its equivalence with existing consistency-based definitions, thus joining the two major streams in knowledge-based configuration (description logics and predicate logic/constraint based configuration).

Type
Research Article
Information
AI EDAM , Volume 17 , Issue 1 , February 2003 , pp. 31 - 50
Copyright
© 2003 Cambridge University Press

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