Ontology revision

  • Norman Foo
Invited Papers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 954)


Knowledge systems as currently configured are static in their concept sets. As knowledge maintenance becomes more sophisticated, the need to address issues concerning dynamic concept sets will naturally arise. Such dynamics is properly called ontology revision, or in the simpler case, expansion. A number of sub-disciplines in artificial intelligence, philosophy and recursion theory have results that are relevant to ontology expansion even though their motivations were quite different. More recently in artificial intelligence ontologies have been explicitly considered. This paper is partly a summary of early results, and partly an account of ongoing work in this area.


ontology concept formation theoretical term predicate invention theory change induction type hierarchy action 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [B&J]
    G. Boolos and R. Jeffery, “Computability and Logic”, 2ed, Cambridge University Press, 1980.Google Scholar
  2. [BUC]
    J.R. Buchi, “Turing Machines and the Entscheidungsproblem”, Math. Annalen, 148, 1962, pp 201–213.Google Scholar
  3. [CAR]
    R. Carnap, “Testability and Meaning”, Philosophy of Science, III, 1936, pp 419–471; IV, 1937, pp 1–40.Google Scholar
  4. [KLE]
    S.C. Kleene, “Finite Axiomatizability of Theories in the Predicate Calculus Using Additional Predicate Symbols”, Memoirs of the American Mathematical Society, no. 10,1952, pp 27–68.Google Scholar
  5. [K&F]
    R. Kwok and N. Foo, “Detecting, Diagnosing and Correcting Faults in a Theory of Actions”, Proceedings of the Second Automated Reasoning Day, Bribie Island, 1994, 5p.Google Scholar
  6. [LLO]
    J. Lloyd, “Mathematical Foundations of Logic Programming”, Springer Verlag, 1984.Google Scholar
  7. [LYN]
    R. Lyndon, “Properties Preserved Under Homomorphisms”, Pacific Journal of Mathematics, 9,1959, pp 143–154.Google Scholar
  8. [MCC]
    J. McCarthy, “Applications of Circumscription to Formalising Commonsense Knowledge”, Artificial Intelligence, 28, 1, 1986, pp 89–116.Google Scholar
  9. [MGB]
    S. Muggleton and R. Buntine, “Machine Invention of First-Order Predicates by Inverting Resolution”, Fifth International Conference on Machine Learning, 1988, Morgan Kaufmann, pp 339–352.Google Scholar
  10. [NAG]
    E. Nagel, “The Structure of Science”, Routledge and Kegan Paul, 1961.Google Scholar
  11. [QUI]
    J.R. Quinlan, “Learning Logical Definitions from Relations”, Machine Learning, vol.5, no. 3,1990, pp 239–266.Google Scholar
  12. [REI]
    R. Reiter, “Nonmonotonic Reasoning”, Annual Review of Computer Science, 2,1987, pp 147–186.Google Scholar
  13. [SAN]
    E. Sandewall, “Features and Fluents”, Oxford University Press, 1994.Google Scholar
  14. [SHA]
    E. Shapiro, “Inductive Inference of Theories from Facts”, TR 192, Dept of Computer Science, Yale University, 1981.Google Scholar
  15. [SHS]
    W-M. Shen and H.A. Simon, “Fitness Requirements for Scientific Theories Containing Recursive Theoretical Terms”, British Journal of Philosophy, 44,1993, pp 641–652.Google Scholar
  16. [STA]
    I. Stahl, “On the Untility of Predicate Invention in Inductive Logic Programming”, Proceedings of the European Conference on Machine Learning, ECML-94, 1994, pp 272–286.Google Scholar
  17. [C&L]
    CL. Chang and R.C.T. Lee, “Symbolic Logic and Mechanical Theorem Proving”, Academic Press, 1973.Google Scholar
  18. [CLA]
    K. Clark, “Negation as Failure”, in Logic and Databases, ed. H. Gallaire and J. Minker, Plenum Press, 1978, pp 293–322.Google Scholar
  19. [CRA]
    W Craig, “On Axiomatizability Within A System”, The Journal of Symbolic Logic, vol. 18, no. 1, pp 30–32.Google Scholar
  20. [C&V]
    W. Craig and R.L Vaught, “Finite Axiomatizability Using Additional Predicates”, The Journal of Symbolic Logic, vol. 23, no. 3, pp 289–308.Google Scholar
  21. [END]
    H. Enderton, “A Mathematical Introduction to Logic”, Academic Press, 1972.Google Scholar
  22. [FO1]
    N. Foo, “Comments on Defining Software by Continuous Smooth Functions”, IEEE Transactions on Software Engineering, vol. 19, no. 3, 1993, pp 307–309.Google Scholar
  23. [FO2]
    N. Foo, “How Theories Fail — A Preliminary Report”, Proceedings National Conference on Information Technology, Penang, Malaysia, 1991, pp 244–251.Google Scholar
  24. [F&T]
    N. Foo and T. Tang, “An Inductive Principle for Learning Logical Definitions from Relations”, Proceedings of the Seventh Australian Joint Conference on Artificial Intelligence, World Scientific Press, 1994, pp 45–52.Google Scholar
  25. [FGRT]
    N. Foo, B. Garner, A. Rao, and E. Tsui, “Semantic Distance in Conceptual Graphs”, in “Current Directions in Conceptual Structure Research”, (eds) P Eklund, T Nagle, J Nagle and L Gerhotz, Ellis Horwood, 1992, pp 149–154.Google Scholar
  26. [GOW]
    H. Gaifman, D. Osherson and S. Weinstein, “A Reason for Theoretical Terms”, Erkenntnis, 32, 1990, pp 149–159.Google Scholar
  27. [GAR]
    P. Gardenfors, “Knowledge in Flux”, Bradford Books, MIT Press, 1988.Google Scholar
  28. [GF1]
    G. Gibbon and N. Foo, “Predicate Discovery in a Model Identification Framework”, Proceedings of the Sixth Australian Joint Conference on Artificial Intelligence, World Scientific Press, 1993, pp 65–70.Google Scholar
  29. [GF2]
    G. Gibbon and N. Foo, “A General Framework for Concept Formation”, Proceedings of the Seventh Australian Joint Conference on Artificial Intelligence, World Scientific Press, 1994, pp 53–59.Google Scholar
  30. [HEM]
    C. Hempel, “Fundamentals of Concept Formation in Empirical Science”, International Encyclopedia of Unified Science, vol. 2, no. 7, University of Chicago Press, 1952.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • Norman Foo
    • 1
  1. 1.Knowledge Systems Group, Department of Computer ScienceUniversity of SydneySydneyAustralia

Personalised recommendations