Part2 chapter1

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Concept Learning

Inductive Logic Programming for Ontology Learning
Authors Jens Lehmann, Nicola Fanizzi, Lorenz Bühmann, Claudia d'Amato
Part Lexical and Logical Learning
Topics ILP, Concept Learning
Projects DL-FOIL, DL-Learner
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The chapter "Inductive Logic Programming for Ontology Learning" was written by Jens Lehmann, Nicola Fanizzi, Lorenz Bühmann, Claudia d'Amato.

Abstract

One of the bottlenecks of the ontology construction process is the amount of work required with various figures playing a role in it: domain experts contribute their knowledge that has to be formalized by knowledge engineers so that it can be mechanized. As the gap between these roles likely makes the process slow and burdensome, this problem may be tackled by resorting to machine learning techniques. By adopting algorithms from inductive logic programming, the effort of the domain expert can be reduced, i.e. he has to label individual resources as instances of the target concept. From those labels, axioms can be induced, which can then be confirmed by the knowledge engineer. In this chapter, we survey existing methods in this area and illustrate three different algorithms in more detail. Some basics like refinement operators, decision trees and information gain are described. Finally, we briefly present implementations of those algorithms.

Topics / Key Words

ILP, Concept Learning


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Facts about Part2 chapter1RDF feed
AbstractOne of the bottlenecks of the ontology con One of the bottlenecks of the ontology construction process is the amount of work required with various figures playing a role in it: domain experts contribute

their knowledge that has to be formalized by knowledge engineers so that it can be mechanized. As the gap between these roles likely makes the process slow and burdensome, this problem may be tackled by resorting to machine learning techniques. By adopting algorithms from inductive logic programming, the effort of the domain expert can be reduced, i.e. he has to label individual resources as instances of the target concept. From those labels, axioms can be induced, which can then be confirmed by the knowledge engineer. In this chapter, we survey existing methods in this area and illustrate three different algorithms in more detail. Some basics like

refinement operators, decision trees and information gain are described. Finally, we briefly present implementations of those algorithms.
esent implementations of those algorithms.
AuthorJens Lehmann  +, Nicola Fanizzi  +, Lorenz Bühmann  +, and Claudia d'Amato  +
Book part titleLexical and Logical Learning  +
Chapter titleInductive Logic Programming for Ontology Learning  +
ProjectsDL-FOIL  +, and DL-Learner  +
TopicsILP  +, and Concept Learning  +
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