Advertisement

Knowledge discovery in databases: Exploiting knowledge-level redescription

  • James Cupit
  • Nigel Shadbolt
Data Mining
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1076)

Abstract

Within this paper, we analyse the nature of knowledge discovery in database. We conclude that it is similar to that of knowledge acquisition, yet unique in that it employs pre-existing data collected for reasons other than analysis. The post-hoc nature of KDD means that the database is often unfit for analysis using traditional machine-learning techniques. We present a methodology for KDD that attempts to overcome this problem. Knowledge elicitation techniques are employed to define the structure of an appropriate learning dataset and to relate this structure to the raw database. The raw database is then redescribed in terms of the new structure before machine learning tools are applied. We also present CASTLE, a software workbench designed to support this methodology, and illustrate it's usage upon a worked example drawn from the Sisyphus-I room allocation problem.

Keywords

Principle Component Analysis Knowledge Acquisition Domain Ontology Inference Model Domain Description 
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. Austin, J.L. (1955) How to do things with words. Oxford University Press, New York.Google Scholar
  2. Breuker, J., Wielinga, B., Van Someren, M., De Hoog, R., Schreiber, G., De Greef, P., Bredeweg, B., Wielemaker, L., Billault, J-P., Davoodi, M. and Hayward, S. (1987) Model Driven Knowledge Acquisition: interpretation models. ESPRIT Project P1098 Deliverable D1. University of Amsterdam and STL Ltd.Google Scholar
  3. Chandrasekaran, B. (1988) Generic tasks as building blocks for knowledge-based systems: the diagnosis and routine design examples. The Knowledge Engineering Review, 3(3), 183–210.Google Scholar
  4. Chi, M.T.H., Glasser, R. and Farr, M.J. (1988) The nature of expertise. Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
  5. Clark, P. and Niblett, T. (1979) The CN2 induction algorithm. Machine Learning 3, 263–283.Google Scholar
  6. Corbridge, C., Rugg, G., Major, N., Shadbolt, N. and Burton, M. (1994) Laddering: technique and tool use in knowledge acquisition. Knowledge Acquisition (1994), 6, 315–341.Google Scholar
  7. Cupit, J., Major, N. and Shadbolt, N. (1994) REKAP: A methodology for the automated construction of real-time and distributed knowledge-based systems. Proceedings of AP94.Google Scholar
  8. Cupit, J. and Shadbolt, N. (1994) Representational redescription within knowledge intensive data-mining. Proceedings of JKAW 1994.Google Scholar
  9. Frawley, W. Piatetsky-Shapiro, G. and Matheus, C.J. (1991) Knowledge Discovery in Databases: An overview. In Piatetsky-Shapiro and Frawley (eds). Knowledge discovery in databases (1991). AAAI Press.Google Scholar
  10. Ganascia, J., Thomas, J. and Laublet, P. (1993) Integrating models of knowledge and machine learning. Proceedings of ECML, 1993. pp 396–401. Springer-Verlag.Google Scholar
  11. Mannila, H.(1995) Aspects of data mining. In Kodratoff, Nakhaeizadeh and Taylor (eds) Statistics, Machine Learning and Knowledge Discovery in Databases. MLnet workshop notes, ECML-95.Google Scholar
  12. O'Hara, K. (1993) A Representation of KADS-I Interpretation Models Using A Decompositional Approach. In Löckenhoff, C. Fensel, D. and Studer, R. (Eds.) Proceedings of the 3rd KADS Meeting, pp 147–169. Siemens AG, Munich.Google Scholar
  13. Piatetsky-Shapiro, G. and Frawley, W. (eds). (1991) Knowledge discovery in databases. AAAI Press.Google Scholar
  14. Quinlan, J.R. and Rivest, R.L. (1994) Inferring decisions trees using the minimum description length principle. Information and computation. 80. pp 227–248.Google Scholar
  15. Rouveirol, C. and Albert, P. (1994) Knowledge level model of a configuable learning system. Proceedings of EKAW 1994. pp. 374–393. Springer-Verlag.Google Scholar
  16. Rummelhart, D.E and McClelland, J.L. (eds). (1990) Parallel Distributed Cognition: Explorations in the Microstructure of Cognition: vol 1, Foundations (pp. 318–62), Cambridge, MA: MIT Press.Google Scholar
  17. Russel, S.J., and Grosof, B.N. (1990) Declarative bias: an overview. In Change of representation and inductive bias. ed. by P Benjamin.Google Scholar
  18. Terpstra, P., Van Heijst, G., Shadbolt, N. and Wielinga, B. (1993) Knowledge Acquisition Process Support Through Generalized Directive Models. In David, J-M., Krivine, J-P. and Simmons, R. (eds.) Second Generation Expert Systems, pp 428–454. Springer-Verlag.Google Scholar
  19. Thomas, J. Ganascia, J and Laublet, P. (1993) Model-based knowledge acquisition and knowledge-biased machine learning: an example of a principled association. In Procdeedings of IJCAI workshop 16, Chambery.Google Scholar
  20. Shadbolt, N. and Burton, M. (1989) The empirical study of knowledge elicitation techniques. SIGART Newsletter, 108, April 1989.Google Scholar
  21. Shadbolt, N., Motta, E. and Rouge, A. (1993) Constructing knowledge-based systems. IEEE software, November, 34–39.Google Scholar
  22. Shadbolt, N. and Wielinga, B. (1990) Knowledge based knowledge acquisition: the next generation of support tools. In B.J. Wielinga, B. Gaines, G. Scheiber and M. Van Sommeren (eds) Current Trends in Knowledge Acquisition, 313–338, Amsterdam. IOS Press.Google Scholar
  23. Schlimmer, J. Mitchell, J and McDermott, J. (1991) Justification-based refinement of expert knowledge. In Piatetsky-Shapiro and Frawley (eds). Knowledge discovery in databases (1991). AAAI Press.Google Scholar
  24. Wielinga, B.J., vad de Velde, W., Schreiber, G. and Akkermans, H. (1992). The Common KADS Framework for knowledge modelling. Proceedings of the 7th KA workshop, Banff, Alberta, Canada.Google Scholar
  25. Wittgenstein, L. (1958) Philosphical Investigations. Blackwell, Oxford.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • James Cupit
    • 1
  • Nigel Shadbolt
    • 1
  1. 1.Artificial Intelligence Group, Department of PsychologyNottingham UniversityUK

Personalised recommendations