From extensional to intensional knowledge: Inductive logic programming techniques and their application to deductive databases

  • Peter A. Flach
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1472)


This chapter aims at demonstrating that inductive logic programming (ILP), a recently established subfield of machine learning that induces first-order clausal theories from examples, combines very well with the area of deductive databases. In the context of deductive databases, induction can be defined as inference of intensional knowledge from extensional knowledge. Classification-oriented ILP approaches correspond to induction of view definitions (IDB rules), while description-oriented ILP approaches correspond to induction of integrity constraints. The applicability of ILP methods in deductive databases thus includes induction of IDB rules and learning of integrity constraints. Further possible applications are reverse engineering, query optimisation and intensional answers, and data mining. The chapter gives an accessible introduction to ILP with particular emphasis on applications in deductive databases.


Integrity Constraint Inductive Logic Inductive Logic Programming Horn Clause Deductive Database 
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. 1.
    D. Angluin, M. Frazier & L. Pitt. Learning conjunctions of Horn clauses. Machine Learning, 9(2/3):147–164, 1992.zbMATHCrossRefGoogle Scholar
  2. 2.
    I. Bratko & S. Muggleton. Applications of Inductive Logic Programming. Comm. ACM 38(11):65–70, November 1995.CrossRefGoogle Scholar
  3. 3.
    L. De Raedt & M. Bruynooghe. A theory of clausal discovery. Proc. 13th Int. Joint Conf. on Artificial Intelligence, Morgan Kaufmann, pp.1058–1063, 1993.Google Scholar
  4. 4.
    L. De Raedt, editor. Advances in Inductive Logic Programming. IOS Press, 1996.Google Scholar
  5. 5.
    L. De Raedt & L. Dehaspe. Clausal discovery. Machine Learning, 26(2/3):99–146, 1997.zbMATHCrossRefGoogle Scholar
  6. 6.
    S. DŽeroski & I. Bratko. Applications of Inductive Logic Programming. In [4], pp.65–81.Google Scholar
  7. 7.
    P. Flach. Simply Logical — intelligent reasoning by example. John Wiley, 1994.Google Scholar
  8. 8.
    P.A. Flach. Inductive characterisation of database relations. Proc. Fifth Int. Symp. on Methodologies for Intelligent Systems ISMIS'90, Z.W. Ras, M. Zemankowa & M.L. Emrich (editors), North-Holland, pp.371–378, 1990. Full version appeared as ITK Research Report 23, Inst. for Language Technology & Artificial Intelligence, Tilburg University.Google Scholar
  9. 9.
    P.A. Flach. Predicate invention in Inductive Data Engineering. Proc. Eur. Conf. on Machine Learning ECML'93, P.B. Brazdil (editor), Lecture Notes in Artificial Intelligence 667, Springer-Verlag, pp.83–94, 1993.Google Scholar
  10. 10.
    P.A. Flach. Conjectures — an inquiry concerning the logic of induction. PhD thesis, Tilburg University, April 1995.Google Scholar
  11. 11.
    P.A. Flach. Rationality postulates for induction. Proc. 6th Int. Conf. on Theoretical Aspects of Rationality and Knowledge, Yoav Shoham (ed.), pp.267–281. Morgan Kaufmann, 1996.Google Scholar
  12. 12.
    P.A. Flach. Normal forms for Inductive Logic Programming. Proc. 7th Int. Worksh. on Inductive Logic Programming ILP-97, N. Lavrac & S. DŽeroski (eds.), Lecture Notes in Artificial Intelligence 1297, pp.149–156. Springer-Verlag, 1997.Google Scholar
  13. 13.
    P.A. Flach & N. Lachiche. Cooking up integrity constraints with Primus. Technical Report, Department of Computer Science, University of Bristol, 1997.Google Scholar
  14. 14.
    H. Gallaire, J. Minker & J.-M. Nicolas. Logic and databases: a deductive approach. Computing Surveys 16 (2): 153–185, 1984.zbMATHMathSciNetCrossRefGoogle Scholar
  15. 15.
    G. Gottlob. Subsumption and implication. Inf. Proc. Letters 24:109–111, 1987.zbMATHMathSciNetCrossRefGoogle Scholar
  16. 16.
    N. Helft. Induction as nonmonotonic inference. Proc. First Int. Conf. on Knowledge Representation and Reasoning KR'89, Morgan Kaufmann, pp.149–156, 1989.Google Scholar
  17. 17.
    Y. Huhtala, J. Kärkkäinen, P. Porkka & H. Toivonen. Efficient Discovery of Functional and Approximate Dependencies Using Partitions. Proc. 14th Int. Conf. on Data Engineering, IEEE Computer Society Press, February 1998.Google Scholar
  18. 18.
    P. Idestam-Almquist. Generalization of clauses. PhD thesis, Stockholm University, October 1993. IdestamPhD93Google Scholar
  19. 19.
    P. Idestam-Almquist. Generalization of Clauses under Implication. J. AI Research, 3:467–489, 1995.zbMATHGoogle Scholar
  20. 20.
    P. van der Laag. An analysis of refinement operators in Inductive Logic Programming. PhD Thesis, Erasmus University Rotterdam, December 1995.Google Scholar
  21. 21.
    N. Lavrač & S. DŽeroski. Inductive Logic Programming: techniques and applications. Ellis Horwood, 1994.Google Scholar
  22. 22.
    N. Lavrač, S. DŽeroski & I. Bratko. Handling imperfect data in Inductive Logic Programming. In [4], pp.48–64.Google Scholar
  23. 23.
    N. Lavrač, I. Weber, D. Zupanič, D. Kazakov, O. ©tepánková & S. DŽeroski. ILPNET repositories on WWW: Inductive Logic Programming systems, datasets and bibliography. AI Communications 9(4):157–206, 1996.Google Scholar
  24. 24.
    D.W. Loveland & G. Nadathur. Proof procedures for logic programming. Handbook of Logic in Artificial Intelligence and Logic Programming, Vol. 5, D.M. Gabbay, C.J. Hogger & J.A. Robinson (editors), Oxford University Press, pp.163–234, 1998.Google Scholar
  25. 25.
    D. Maier. The theory of relational databases. Computer Science Press, 1983.Google Scholar
  26. 26.
    H. Mannila & K.-J. Räihä. Algorithms for inferring functional dependencies from relations. Data & Knowledge Engineering 12:83–99, 1994.zbMATHCrossRefGoogle Scholar
  27. 27.
    S. Muggleton & W. Buntine. Machine invention of first-order predicates by inverting resolution. Proc. Fifth Int. Conf. on Machine Learning, J. Laird (ed.), Morgan Kaufmann, San Mateo, pp.339–352, 1988. Also in [30], pp.261–280.Google Scholar
  28. 28.
    S. Muggleton & C. Feng. Efficient induction of logic programs. Proc. First Conf. on Algorithmic Learning Theory, Ohmsha, Tokyo, 1990. Also in [30], pp.281–298.Google Scholar
  29. 29.
    S. Muggleton. Inductive Logic Programming. New Generation Computing, 8(4):295–317, 1991. Also in [30], pp.3–27.zbMATHCrossRefGoogle Scholar
  30. 30.
    S. Muggleton, editor. Inductive Logic Programming. Academic Press, 1992. MuggletonBook92Google Scholar
  31. 31.
    S. Muggleton & L. De Raedt. Inductive Logic Programming: theory and methods. J. Logic Programming, 19/20:629–679, 1994.CrossRefGoogle Scholar
  32. 32.
    S. Muggleton. Inverse entailment and Progol. New Generation Computing, 13:245–286, 1995.Google Scholar
  33. 33.
    C. Nédellec, C. Rouveirol, H. Adé, F. Bergadano & B. Tausend. Declarative bias in Inductive Logic Programming. In [4], pp.82–103.Google Scholar
  34. 34.
    J. Paredaens, P. De Bra, M. Gyssens & D. Van Guch. The structure of the relational database model. Springer-Verlag, 1989.Google Scholar
  35. 35.
    G. Plotkin. A note on inductive generalisation. Machine Intelligence 5, B. Meltzer & D. Michie (editors), North-Holland, pp.153–163, 1970.Google Scholar
  36. 36.
    G. Plotkin. A further note on inductive generalisation. Machine Intelligence 6, B. Meltzer & D. Michie (editors), North-Holland, pp.101–124, 1971.Google Scholar
  37. 37.
    J.R. Quinlan. Learning logical definitions from relations. Machine Learning, 5(3):239–266, 1990.Google Scholar
  38. 38.
    C. Rouveirol. Flattening and saturation: two representation changes for generalization. Machine Learning, 14(2):219–232, 1994.zbMATHCrossRefGoogle Scholar
  39. 39.
    R. Reiter. Towards a logical reconstruction of relational database theory. On conceptual modelling: perspectives from Artificial Intelligence, databases and programming languages, M.L. Brodie, J. Mylopoulos & J.W. Schmidt (editors), Springer-Verlag, pp.191–233, 1984.Google Scholar
  40. 40.
    I. Savnik & P.A. Flach. Bottom-up induction of functional dependencies from relations. Proc. AAAI '93 Workshop Knowledge Discovery in Databases, G. Piatetsky-Shapiro (editor), pp.174–185, 1993.Google Scholar
  41. 41.
    E.Y. Shapiro. Inductive inference of theories from facts. Techn. rep. 192, Comp. Sc. Dep., Yale University, 1981.Google Scholar
  42. 42.
    E.Y. Shapiro. Algorithmic program debugging. MIT Press, 1983.Google Scholar
  43. 43.
    I. Stahl. Compression measures in ILP. In [4], pp.295–307.Google Scholar
  44. 44.
    L. Valiant. A theory of the learnable. Comm. ACM 27:1134–1142, 1984.zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Peter A. Flach
    • 1
  1. 1.Department of Computer ScienceUniversity of BristolBristolUK

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