Encyclopedia of the Sciences of Learning

2012 Edition
| Editors: Norbert M. Seel

Inductive Logic Programming

Reference work entry
DOI: https://doi.org/10.1007/978-1-4419-1428-6_1064

Synonyms

Definition

Inductive Logic Programming (ILP) (De Raedt 2008; Nienhuys-Cheng and De Wolf 1997) is a family of methods for automated learning (or machine learning) of general rules from specific data and background knowledge. Unlike other machine learning methods, ILP uses the expressive language of the first-order predicate logic to represent input data, background knowledge, and learned hypotheses. This makes ILP suitable for data mining applications in domains characterized by nontrivially structured data, such as biochemistry or natural language processing. Since learned hypotheses can acquire the form of logic programs, the goal of ILP may be formulated as automated induction of the latter; hence, the name inductive logic programming.

Theoretical Background

Consider a task where an ILP algorithm receives examples of toxic and nontoxic chemical compounds. From these examples, it learns a general hypothesis, according to which toxicity...

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Notes

Acknowledgments

The author is supported by the project 103/10/1875 of the Czech Science Foundation.

References

  1. De Raedt, L. (2008). Logical and relational learning. Heidelberg: Springer.CrossRefGoogle Scholar
  2. Muggleton, S. H. (2006). Exceeding human limits. Nature, 440(7083), 409–410.CrossRefGoogle Scholar
  3. Nienhuys-Cheng, S.-H., & De Wolf, R. (1997). Foundations of Inductive Logic Programming. Heidelberg: Springer.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Czech Technical University in PraguePrague 6Czech Republic