Advertisement

Systematic building of conceptual classification systems with C-KAT

Problem Solving Models Support Tools
Part of the Lecture Notes in Computer Science book series (LNCS, volume 723)

Abstract

C-KAT is a method and a tool which supports the design of “feature oriented” classification systems. During the design of these systems, one is very often confronted with the problem of the “calculation of the attribute cross-product”. It arises because the examination of the dependency and compatibility relations between the attributes leads to the need to generate the cross-product of their features. The C-KAT method uses a specialised Heuristic Classification conceptual model named “classification by structural shift” which sees the classification process as the matching of different classifications of the same set of objects or situations organised around different structural principles. To manage the complexity induced by the cross-product, C-KAT supports the use of a least commitment strategy which applies in a context of constraint-directed reasoning. The method is presented using a detailed example from the field of industrial fire-insurance.

Keywords

Knowledge Acquisition Commitment Strategy Dependency Network Inference Structure Abstraction Hierarchy 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Alexander, J.H., Freiling, M.J., Shulman, S. J., Rehfuss, S. & Messick, S. L., Ontological Analysis: an ongoing experiment, in J.H. Boose, B.R. Gaines (eds), Knowledge Acquisition Tools for Expert Systems, Knowledge-Based Systems Vol. 2, pp. 25–39, Academic Press, 1988.Google Scholar
  2. 2.
    Bachelard, G., Le Pluralisme cohérent de la chimie moderne, Vrin, Paris, 1973.Google Scholar
  3. 3.
    J.H.Boose, Personal construct theory and the transfer of human expertise. Proc. AAAI-84. American Association for Artificial Intelligence, pp. 27–33, 1984.Google Scholar
  4. 4.
    J.H. Boose, J.M Bradshaw-Expertise transfer and complex problems: using AQUINAS as a knowledge-acquisition workbench for knowledge-based systems, in J.H. Boose, B.R. Gaines (eds), Knowledge Acquisition Tools for Expert Systems, Knowledge-Based Systems Vol. 2, pp. 26–39, Academic Press 1988.Google Scholar
  5. 5.
    BREUKER J. et al. Model-Driven Knowledge Acquisition Interpretation models — KADS, Esprit Project 1098, Deliverable task A1, 1987.Google Scholar
  6. 7.
    BRUNER J.S., GOODNOW J., AUSTIN G., A Study of Thinking, Wiley, New York, 1956.Google Scholar
  7. 8.
    CHARLET J., ACTE: acquisition des connaissances par interprétation d'un modèle causal, Revue d'Intelligence Artificielle, 6, pages 75–99, 1992.Google Scholar
  8. 9.
    CLANCEY, W., Heuristic Classification, Artificial Intelligence Journal, 27, pp. 289–350, 1985Google Scholar
  9. 10.
    ESHELMAN, L., MOLE: A Knowledge-Acquisition Tool for Cover-and-Differentiate Systems', Automating knowledge acquistion for expert systems', Sandra Marcus, ed. Kluwer Academic Publisher, Norwell, pp. 37–79.Google Scholar
  10. 11.
    GAINES, B.R. (1990). An architecture for integrated knowledge acquisition systems. Proceedings of the 5th AAAI Knowledge Acquisition for Knowledge-Based Systems Workshop, Banff, Canada.Google Scholar
  11. 12.
    GAPPA U., POEK, K., Common ground and differences of the KADS and Strong-Problem-Solving-Shell approach, in [28].Google Scholar
  12. 13.
    GREBOVAL C., KASSEL G., An approach to operationalize conceptual models: the shell Aide, in [28].Google Scholar
  13. 14.
    F.V. HARMELEN, J. BALDER, (ML)2: A formal language for KADS models of expertise, Knowledge Acquisition (1992) 4, 127–161.Google Scholar
  14. 15.
    JONKER W., SPEE J.W., Yet an other formalisation of KADS conceptual models, in [28].Google Scholar
  15. 16.
    KAHN, G. MORE: From Observing Knowledge Engineers to Automating Knowledge Acquisition, Automating knowledge acquistion for expert systems, Sandra Marcus, ed., pp. 7–33, Kluwer Academic Publisher, Norwell, 1988.Google Scholar
  16. 17.
    G.A.Kelly, The psychology of personal constructs. New York: Norton, 1955.Google Scholar
  17. 18.
    LARICHEV O.I., A new approach to the solution of expert classification problems, in [28].Google Scholar
  18. 19.
    MERVIS C.B., ROSCH E., Categorization of natural objects, Ann. Rev. Psychol. 32, pp.89–115, 1981.Google Scholar
  19. 20.
    E.Motta, T.Rajan and M.Eisenstadt, Knowledge acquisition as a process of model refinement. Knowledge Acquisition, 2 (1), pp. 21–49, 1990.Google Scholar
  20. 21.
    M.A.Musen, Conceptual Models of Interactive Knowledge Acquisition Tools, Knowledge Acquisition V1 N∘1, pp.73–88, 1989.Google Scholar
  21. 22.
    K. O'Hara, N. Shadbolt, P. Laublet, M. Zacklad and B. Leroux, Knowledge Acquisition Methodology, VITAL Report ID 212-08/1992.Google Scholar
  22. 23.
    ROSCH, E., Natural Categories, Cognitive Psychology, 4, pp. 328–350, 1973.Google Scholar
  23. 24.
    SHAW, M.L.G., On becoming a personal scientist, interactive computer elicitation of personal models of the world, Academic Press, 1980.Google Scholar
  24. 25.
    STEELS, L., COMMET: A componential methodology for knowledge engineering, Esprit project CONSTRUCT. Deliverable WP 2/3/4 (synthesis)Google Scholar
  25. 26.
    TERPSTRA, P., Anjewierden, A., van Heijst, G., de Hood, R., Ramaparany, F., Reichgelt, H., Shadbolt, N. and Wielinga, B. KA process support in KEW. ACKnowledge Report ACK-UVA-A2-DEL-000-prel,1991.Google Scholar
  26. 27.
    TORT P., La raison classificatoire, Aubier, Paris, 1989.Google Scholar
  27. 28.
    TH, WETTER, K.-D. ALTHOFF, J. BOOSE, B.R. GAINES, M. LINSTER and F. SCHMALHOFER (eds.), Current Developments in Knowledge Acquisition — EKAW 92, Lecture Notes in Artificial Intelligence, pp. 75–95, Springer 1992.Google Scholar
  28. 28.
    M. ZACKLAD, D. FONTAINE, C-KAT: un outil d'aide à l'acquisition des connaissances pour les systèmes en classification heuristique. In Actes des 12emes Journées Internationales: les Systèmes Experts & leurs Applications, Conférence Scientifique, pages 177–194, Avignon, France, 1–6 juin 1992.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1993

Authors and Affiliations

  1. 1.Département du Génie Informatique URA CNRS 817Université de CompiègneCompiègne CedexFrance

Personalised recommendations