On the relative expressiveness of description logics and predicate logics
Artificial Intelligence
Top-down induction of first-order logical decision trees
Artificial Intelligence
Ontology Learning for the Semantic Web
IEEE Intelligent Systems
Machine Learning
Ontology Matching
The Description Logic Handbook
The Description Logic Handbook
An algorithm based on counterfactuals for concept learning in the Semantic Web
Applied Intelligence
Completing description logic knowledge bases using formal concept analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Covering vs divide-and-conquer for top-down induction of logic programs
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Query answering and ontology population: an inductive approach
ESWC'08 Proceedings of the 5th European semantic web conference on The semantic web: research and applications
A refinement operator based learning algorithm for the ALC description logic
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
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A concept learning framework for terminological representations is introduced. It is grounded on a method for inducing logic decision trees as an adaptation of the classic tree induction methods to the Description Logics representations adopted in the Semantic Web context. Differently from the original setting of logical trees based on clausal representations, tree-nodes contain terminological concept descriptions (corresponding to OWL-DL classes) which makes it appealing for the Semantic Web applications. The method has been implemented in a prototypical system which has been experimentally evaluated on real ontologies.