From conceptual model to internal model

  • John Debenham
Communications Session 2B Intelligent Information Systems
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1325)


A uniform formalism is used to model all stages of knowledge-based systems design. In this approach data, information and knowledge are all represented in this single uniform formalism. This formalism incorporates two classes of constraints which are applied to data, information and to knowledge. A conceptual model is a representation of the system expertise using this formalism. An internal model is derived from the conceptual model and from a specification of the system transactions and the performance constraints. The internal model is a complete system specification. The internal model is derived in two steps. First, the conceptual model and a specification of the system transactions are used to derive the functional model. The functional model shows how the knowledge in the conceptual model should be employed to deliver the transactions. Second, the internal model is derived from the functional model and from the performance constraints. Using a broad definition of ‘best’, the problem of deriving the best functional model, and the problem of deriving an internal model are both NP-complete.


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  1. 1.
    J.K. Debenham, “Knowledge Simplification”, in proceedings 9th International Symposium on Methodologies for Intelligent Systems ISMIS'96, Zakopane, Poland, June 1996, pp305–314.Google Scholar
  2. 2.
    J.K. Debenham, “Knowledge Constraints”, in proceedings Eighth International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems IEA/AIE'95, Melbourne, June 1995, pp553–562.Google Scholar
  3. 3.
    J.K. Debenham, “Integrating Knowledge Base and Database”, in proceedings 10th ACM Annual Symposium on Applied Computing SAC'96, Philadelphia, February 1996, pp28–32.Google Scholar
  4. 4.
    J.K. Debenham, “Unification of Knowledge Acquisition and Knowledge Representation”, in proceedings International Conference on Information Processing and Management of Uncertainty in Knowledge Based Systems IPMU'96, Granada, Spain, July 1996, pp897–902.Google Scholar
  5. 5.
    R. Capobianchi, M. Mautref, M. van Keulen and H. Balsters, “An Architecture and Methodology for the Design and Development of Technical Information Systems”, in proceedings 9th International Symposium on Methodologies for Intelligent Systems ISMIS'96, Zakopane, Poland, June 1996, pp511–520.Google Scholar
  6. 6.
    F. Lehner, H.F. Hofman, R. Setzer, and R. Maier, “Maintenance of Knowledge Bases”, in proceedings Fourth International Conference DEXA93, Prague, September 1993, pp436–447.Google Scholar
  7. 7.
    H. Katsuno and A.O. Mendelzon, “On the Difference between Updating a Knowledge Base and Revising It”, in proceedings Second International Conference on Principles of Knowledge Representation and Reasoning, KR'91, Morgan Kaufmann, 1991.Google Scholar
  8. 8.
    J.K. Debenham, “Understanding Expert Systems Maintenance”, in proceedings Sixth International Conference on Database and Expert Systems Applications DEXA'95, London, September, 1995.Google Scholar
  9. 9.
    S. Sahni, “Computationally Related Problems”, SIAM Computing, Vol 3, No 4, (1974), pp 262–279.Google Scholar
  10. 10.
    M.R. Garey, D.S. Johnson, and L. Stockmeyer, “Some simplified NPcomplete graph problems.”, Theoretical Computer Science, Vol 1, No 3, 1976, pp 237–267.Google Scholar
  11. 11.
    S. Even, “Graph Algorithms”, Computer Science Press, 1979.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • John Debenham
    • 1
  1. 1.Key Centre for Advanced Computing SciencesUniversity of TechnologySydneyAustralia

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