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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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References

REFERENCES

Aldanondo, M., Moynard, G., & Hamou, K. (2000). General configurator requirements and modeling elements. Proc. Workshop on Configuration, ECAI 2000, pp. 1–6.
Ardissono, L., Felfernig, A., Friedrich, G., Jannach, D., Zanker, M., & Schäfer, R. (2001). Customer-adaptive and distributed online product configuration in the CAWICOMS Project. Proc. Workshop on Configuration, in Conjunction with the 17th International Conf. Artificial Intelligence (IJCAI-2001), pp. 8–14.
Artale, A., Franconi, E., Guarino, N., & Pazzi, L. (1996). Part–whole relations in object-centered systems: An overview. Data & Knowledge Engineering 20(3), 347–383.Google Scholar
Baader, F. & Sattler, U. (1998). Description logics with concrete domains and aggregation. Proc. 13th European Conf. Artificial Intelligence (ECAI '98), pp. 336–340.
Barker, V., O'Connor, D., Bachant, J., & Soloway, E. (1989). Expert systems for configuration at Digital: XCON and beyond. Communications of the ACM 32(3), 298–318.Google Scholar
Bechhofer, S., Horrocks, I., Goble, C., & Stevens, R. (2001). OilEd: A reasonable ontology editor for the Semantic Web. Proc. Joint German/Austrian Conf. on AI, pp. 396–408.
Burners–Lee, T. (2000). Weaving the Web. New York: Harper Business.
Booch, G. (1994). Object-Oriented Analysis and Design with Applications. Object Technology Series. Reading, MA: Addison–Wesley.
Borgida, A. (1996). On the relative expressive power of description logics and predicate calculus. Artificial Intelligence 82, 353–367.
Buchheit, M., Klein, R., & Nutt, W. (1995). Constructive Problem Solving: A Model Construction Approach Towards Configuration. Technical Report TM-95-01. Saarbrücken, Germany: DFKI.
Cai, J., Fürer, M., & Immerman, N. (1989). An optimal lower bond on the number of variables for graph identification. Proc. 30th IEEE Symposium on FOCS, pp. 612–617.
Chandrasekaran, B., Josephson, J., & Benjamins, R. (1999). What are ontologies, and why do we need them? IEEE Intelligent Systems 14(1), 20–26.Google Scholar
Cranefield, S. (2001). UML and the Semantic Web. Semantic Web Working Symposium.
Cunis, R., Günter, A., Syska, I., Peters, H., & Bode, H. (1989). PLAKON—An approach to domain independent construction. Proc. Int. Conf. Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, pp. 298–318.
Felfernig, A., Friedrich, G., & Jannach, D. (2000a). UML as domain specific language for the construction of knowledge-based configuration systems. International Journal of Software Engineering and Knowledge Engineering 10(4), 449–469.Google Scholar
Felfernig, A., Friedrich, G., Jannach, D., & Stumptner, M. (2000b). Consistency-based diagnosis of configuration knowledge bases. Proc. 14th European Conf. Artificial Intelligence (ECAI 2000), pp. 146–150.
Fensel, D., Ding, Y., Omelayenko, B., Schulten, E., Botquin, G., Brown, M., & Fett, A. (2001a). Product data integration in B2B E-commerce. IEEE Intelligent Systems 16(4), 54–59.Google Scholar
Fensel, D., VanHarmelen, F., Horrocks, I., McGuinness, D., & Patel–Schneider, P. (2001b). OIL: An ontology infrastructure for the semantic web. IEEE Intelligent Systems 16(2), 38–45.Google Scholar
Fleischanderl, G., Friedrich, G., Haselböck, A., Schreiner, H., & Stumptner, M. (1998). Configuring large systems using generative constraint satisfaction. IEEE Intelligent Systems 13(4), 59–68.Google Scholar
Gray, P., Hui, K., & Preece, A. (2001). An expressive constraint language for Semantic Web applications. Proc. IJCAI 2001 Workshop on E-Business and the Intelligent Web, pp. 46–53.
Grosof, B. (2001). Standardizing XML rules. Proc. IJCAI 2001 Workshop on E-Business and the Intelligent Web, pp. 2–3.
Gruber, T. (1992). Ontolingua: A Mechanism to Support Portable Ontologies. Technical Report KSL 91-66. Stanford, CA: KSL.
Gruber, T., Olsen, R., & Runkel, J. (1996). The configuration design ontologies and the VT elevator domain theory. International Journal of Human–Computer Studies 44(3/4), 569–598.
Günther, A. & Cunis, R. (1992). Flexible control in expert systems for construction tasks. Journal of Applied Intelligence 2(4), 369–385.Google Scholar
Günther, A. (1995). Wissensbasiertes Konfigurieren: Ergebnisse aus dem Projekt PROKON. Sankt Augustin, Germany: Infix.
Heinrich, M. & Jüngst, E. (1991). A resource-based paradigm for the configuring of technical systems from modular components. Proc. 7th IEEE Conf. AI Applications (CAIA), pp. 257–264.
Hella, L., Libkin, L., Nurmonen, J., & Wong, L. (2001). Logics with aggregate operators. Journal ACM 48(4), 880–907.Google Scholar
Immerman, N. (1982). Upper and lower bounds for first-order expressibility. Journal of Computer and System Sciences 25, 76–98.
Jacobson, I., Christerson, M., & Övergaard, G. (1992). Object-oriented Software Engineering—A Use-Case Driven Approach. Reading, MA: Addison–Wesley.
Klein, R., Buchheit, M., & Nutt, W. (1994). Configuration as model construction: The constructive problem solving approach. Proc. 3rd Int. Conf. Artificial Intelligence in Design, AID'94, pp. 201–218.
Mailharro, D. (1998). A classification and constraint-based framework for configuration. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 12(4), 383–397.Google Scholar
McGuinness, D. & Wright, J. (1998). Conceptual modeling for configuration: A description logic-based approach. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 12(4), 333–344.Google Scholar
McIlraith, S., Son, T., & Zeng, H. (2001). Mobilizing the Semantic Web with DAML-enabled web services. Proc. IJCAI 2001 Workshop on E-Business and the Intelligent Web, pp. 29–39.
Mittal, S. & Falkenhainer, B. (1990). Dynamic constraint satisfaction problems. Proc. National Conf. Artificial Intelligence (AAAI 90), pp. 25–32.
Mittal, S. & Frayman, F. (1989). Towards a generic model of configuration tasks. Proc. 11th Int. Joint Conf. Artificial Intelligence, pp. 1395–1401.
OMG. (1999). XMI Specification. Available on-line at www.omg.org.
Owsnicki–Klewe, B. (1988). Configuration as a consistency maintenance task. Proc. GWAI-88—The 12th German Workshop on Artificial Intelligence, pp. 77–87.
Ramachandran, S. & Gil, Y. (1999). Knowledge acquisition for configuration tasks: The EXPECT approach. Proc. AAAI Workshop on Configuration, Technical Report WS-99-05, pp. 29–34.
Rumbaugh, J., Blaha, M., Premerlani, W., Eddy, F., & Lorensen, W. (1991). Object-Oriented Modeling and Design. Englewood Cliffs, NJ: Prentice–Hall.
Rumbaugh, J., Jacobson, I., & Booch, G. (1998). The Unified Modeling Language Reference Manual. Reading, MA: Addison–Wesley.
Sattler, U. (2000). Description logics for the representation of aggregated objects. Proc. 14th Eur. Conf. Artificial Intelligence (ECAI 2000), pp. 239–243.
Schröder, C., Möller, R., & Lutz, C. (1996). A partial logical reconstruction of PLAKON/KONWERK. Proc. Workshop on Knowledge Representation and Configuration, DFKI Memo D-96-04, pp. 55–64.
Silaghi, M., Sam–Haroud, D., & Faltings, B. (2000). Asynchronous search with aggregations. Proc. 17th National Conf. Artificial Intelligence (AAAI), pp. 917–922.
Simons, P., Niemelä, I., & Soininen, T. (2002). Extending and implementing the stable model semantics. Artificial Intelligence 138(1–2). 181–234.
Smith, C., Inder, R., & Chung, P. (1988). Knowledge acquisition and representation for product configuration. Proc. 1st Int. Conf. Industrial and Engineering Applications of AI and Expert Systems (IEA/AIE'88), pp. 805–811.
Soininen, T., Tiihonen, J., Männistö, T., & Sulonen, R. (1998). Towards a general ontology of configuration. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 12(4), 357–372.Google Scholar
Stumptner, M. (1997). An overview of knowledge-based configuration. AI Communications 10(2), 111–125.Google Scholar
Stumptner, M., Friedrich, G., & Haselböck, A. (1998). Generative constraint-based configuration of large technical systems. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 12(4), 307–320.Google Scholar
Swartout, B. & Gil, Y. (1995). EXPECT: Explicit representation for flexible acquisition. Proc. 9th Knowledge Acquisition for Knowledge-Based Systems Workshop (KAW-95).
VanHarmelen, F., Patel–Schneider, P., & Horrocks, I. (2001). A Model–Theoretic Semantics for DAML+OIL. Available on-line at www.daml.org.
Weida, R. (1996). Closed terminologies in description logics. AAAI Fall Symposium 1996, pp. 11–18.
Wright, J., Weixelbaum, E., Vesonder, G., Brown, K., Palmer, S., Berman, J., & Moore, H. (1993). A knowledge-based configurator that supports sales, engineering, and manufacturing at AT&T Network Systems. AI Magazine 14(3), 69–80.Google Scholar
Yokoo, M., Durfee, E., Ishida, T., & Kuwabara, K. (1998). The distributed constraint satisfaction problem. IEEE Transactions on Knowledge and Data Engineering 10(5), 673–685.Google Scholar