Towards Declarative Programming of Conceptual Models

  • Jens-Uwe Möller
Conference paper
Part of the Informatik-Fachberichte book series (INFORMATIK, volume 303)


This article introduces some basic functions and architectural issues, that help to build a tool for programming conceptual models, and that is not specific for a particular problem class or problem solving method. Our work is based on the KADS-method, that had to be modified in some points, to enable declarative programming of inference knowledge as well as domain knowledge. It is shown, how knowledge sources can be described as semantic network modules. Knowledge sources are instantiated from generic descriptions.

All resulting semantic networks are part of a modular knowledge base, each module representing the knowledge on its own right level of granularity. Functions are introduced, that define views between semantic networks. They help connecting declarative representation of knowledge sources on the inference layer to parts of the domain layer network. We only contemplate the interconnection of domain and inference layer.


Domain Knowledge Network Module Semantic Network Knowledge Source Small Room 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. Ange91.
    Angele, J.; Fensel, D.; Landes, D.; and Studer, R. Explorative Prototyping in KADS. Tech. Rept. 214, Universität Karlsruhe, Institut für angewandte Informatik und formale Besehreibungsverfahren, Karlsruhe, 1991.Google Scholar
  2. Ange91a.
    Ange91a. Angele, J.; Fensel, D.; Landes, D.; and Studer, R. KARL: An executable language for the conceptual model. In Proc. 6th Banff Knowledge Acquisition for Knowledge-Based Systems Workshop, Banff, Alberta, 1991, pp. 1. 1–20.Google Scholar
  3. Brac85.
    Brachman, R.J.; and Schmölze, J.G. An Overview of the KL-One Knowledge Representation System. Cognitive Science, 9 (1985), 171–216.CrossRefGoogle Scholar
  4. Brac90.
    Brachman, R.J.; McGuinness, D. L.; Patel-Schneider, P. F.; Alperin Resnik, L.; and Borgida, A. Living with CLASSIC: When and How to Use a KL-ONE-Like Language. In Principles of Semantic Networks, Sowa, J., Morgan Kaufmann Publishers, Inc., 1990.Google Scholar
  5. Breu86.
    Breuker, J.; and Wielinga, B. Models of Expertise. In Proc. of the European Conference on Artificial Intelligence ECAI-86, 1986, pp. 306–318.Google Scholar
  6. Breu87.
    Breuker, J.; Wielinga, B.; van Someren, M.; De Hong, R.; Schreiber, G.; De Greef, P.; Bredeweg, B.; Wielemaker, J.; Billault, J.P.; Davoodi, M.; and Hayward, S. Model-Driven Knowledge Acquisition: Interpretation Models. Tech. Rept. Task A1,Memo 87, KADS-Report Esprit Projektl098, Univ. Amsterdam Dept. of Social Science Informatics, Amsterdam, 1987.Google Scholar
  7. Chan87.
    Chandrasekaran, B. Towards a Functional Architecture for Intelligence Based on Generic Information Processing Tasks. In Proc. of the 10th International Joint Conference on Artificial Intelligence, Milano, 1987, pp. 1183–1192.Google Scholar
  8. Harm90.
    van Harmelen, F.; Akkermans, H.; Balder, J.; Schreiber, G.; and Wielinga, B. Formal Specification of Knowledge Models. Tech. Rept. Task 1.4, RFL/ECN/I.4/1, REFLECT-Report Esprit 3178, Univ. Amsterdam Dept. of Social Science Informatics, Amsterdam, 1990.Google Scholar
  9. Lins90.
    Linster, M. Declarative Problem Solving Procedures as a Basis for Knowledge Acquisition: A First Proposal Tech. Rept. 448, Arbeitspapiere der GMD, GMD, St. Augustin, 1990.Google Scholar
  10. Marc88.
    Marcus, S. Automating Knowledge Acquisition for Expert Systems, Kluwer Academic Publishers, Boston (1988).MATHGoogle Scholar
  11. Müll91.
    Müller, S.M.; and Sprenger, M. Dialogabhängige Erklärungsstrategien für modellbasierte Expertensysteme — Das Projekt DIAMOD. Tech. Rept. 2, DIAMOD, GMD, St. Augustin, 1991.Google Scholar
  12. Muse89a.
    Musen, M.A. Building and Extending Models. Machine Learning, 4 (1989), 347–375.Google Scholar
  13. Muse89b.
    Musen, M.A. Automated Generation of Model-Based Knowledge Acquisition Tools, Pitman, London (1989).Google Scholar
  14. Spre91.
    Sprenger, M. Explanation Strategies for KADS-based Expert Systems. Tech. Rept. 10, DIAMOD, GMD, St. Augustin, 1991.Google Scholar
  15. Swar83.
    Swartout, W.R. XPLAIN: A System for Creating and Explaining Expert Consulting Programs. Artificial Intelligence, 21 (1983), 285–325.CrossRefGoogle Scholar
  16. Swar91.
    Swartout, W.R.; Paris, C.; Moore, J. Design for Explainable Expert Systems. IEEE Expert, 6 (1991), 58–64.CrossRefGoogle Scholar
  17. Voss90.
    Voss, A.; Karbach, W.; Drouven, U.; and Schukey, R. Operationalization of a synthetic problem. Tech. Rept. Task 1.2.1, ESPRIT Basic Research Project P3178, GMD F3-XPS, Bonn, 1990.Google Scholar
  18. Wiel84.
    Wielinga, B.; and Breuker, J. Interpretation of verbal data for knowledge acquisition. In Proc. European Conference on Artificial Intelligence, Advances in AI, O’Shea, T., Elsevier Science Publishers B. V., ( North-Holland ), 1984, pp. 41–50.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1992

Authors and Affiliations

  • Jens-Uwe Möller
    • 1
  1. 1.Dept. Computational LinguisticsUniversity of BielefeldBielefeldGermany

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