Attributive concept descriptions with complements
Artificial Intelligence
A Polynomial Approach to the Constructive Induction of Structural Knowledge
Machine Learning - Special issue on evaluating and changing representation
The Learnability of Description Logics with Equality Constraints
Machine Learning - Special issue on computational learning theory, COLT'92
Foundations of Inductive Logic Programming
Foundations of Inductive Logic Programming
A Refinement Operator for Description Logics
ILP '00 Proceedings of the 10th International Conference on Inductive Logic Programming
Handbook on Ontologies (International Handbooks on Information Systems)
Handbook on Ontologies (International Handbooks on Information Systems)
The Description Logic Handbook
The Description Logic Handbook
Learnability of description logic programs
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
An algorithm based on counterfactuals for concept learning in the Semantic Web
Applied Intelligence
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We tackle the problem of learning ontologies expressed in a rich representation like the logic. This task can be cast as a supervised learning problem to be solved by means of operators for this representation which take into account the available metadata. The properties of such operators are discussed and their effectiveness is empirically tested in the experimentation reported in this paper.