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Inductive logic programming

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Inductive logic programming (ILP) is a machine learning approach, which uses techniques of logic programming. From a database of facts and expected results, which are divided into positive and negative examples, an ILP system tries to derive a logic program that proves all the positive and none of the negative examples.

Schema: positive examples + negative examples + background knowledge = rules.

Inductive logic programming is particularly useful in natural language processing.

References

  • S. Muggleton, Inverse Entailment and Progol, New Generation Computing Journal, Vol. 13, pp. 245-286, 1995.
  • S. Muggleton and L. De Raedt. Inductive logic programming: Theory and methods. Journal of Logic Programming, 19,20:629-679, 1994.
  • N. Lavrac and S. Dzeroski. Inductive Logic Programming: Techniques and Applications. Ellis Horwood, New York, 1994, ISBN 0-13-457870-8 Publicly available online version

Implementations

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