Abstract
Inductive logic programming is the subfield of machine learning that uses First-Order Logic to represent hypotheses and data. Because first-order logic is expressive and declarative, inductive logic programming specifically targets problems involving structured data and background knowledge. Inductive logic programming tackles a wide variety of problems in machine learning, including classification, regression, clustering, and reinforcement learning, often using “upgrades” of existing propositional machine learning systems. It relies on logic for knowledge representation and reasoning purposes. Notions of coverage, generality, and operators for traversing the space of hypotheses are grounded in logic; see also Logic of Generality. Inductive logic programming systems have been applied to important applications in bio- and chemo-informatics, natural language processing, and web mining.
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Notes
- 1.
A comprehensive introduction to inductive logic programming can be found in the book by De Raedt (2008) on logical and relational learning. Early surveys of inductive logic programming are contained in Muggleton and De Raedt (1994) and Lavrač and Džeroski (1994) and an account of its early history is provided in Sammut (1993). More recent collections on current trends can be found in the proceedings of the annual Inductive Logic Programming Conference (published in Springer’s Lectures Notes in Computer Science Series) and special issues of the Machine Learning Journal. A summary of some key future challenges is given in Muggleton et al. (2012). An interesting collection of inductive logic programming and multi-relational data mining works are provided in Džeroski and Lavrač (2001). The upgrading methodology is described in detail in Van Laer and De Raedt (2001). More information on logical issues in inductive logic programming are given in the entry Logic of Generality in this encyclopedia, whereas the entries Statistical Relational Learning and Graph Mining are recommended for those interested in frameworks tackling similar problems using other types of representations.
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Raedt, L.D. (2017). Inductive Logic Programming. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_135
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