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...
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
De Raedt, L. (2008). Logical and relational learning. Heidelberg: Springer.
Muggleton, S. H. (2006). Exceeding human limits. Nature, 440(7083), 409–410.
Nienhuys-Cheng, S.-H., & De Wolf, R. (1997). Foundations of Inductive Logic Programming. Heidelberg: Springer.
Acknowledgments
The author is supported by the project 103/10/1875 of the Czech Science Foundation.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer Science+Business Media, LLC
About this entry
Cite this entry
Železný, F. (2012). Inductive Logic Programming. In: Seel, N.M. (eds) Encyclopedia of the Sciences of Learning. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1428-6_1064
Download citation
DOI: https://doi.org/10.1007/978-1-4419-1428-6_1064
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4419-1427-9
Online ISBN: 978-1-4419-1428-6
eBook Packages: Humanities, Social Sciences and Law