Machine Learning
A Refinement Operator for Description Logics
ILP '00 Proceedings of the 10th International Conference on Inductive Logic Programming
Learning from examples with unspecified attribute values
Information and Computation
The Description Logic Handbook
The Description Logic Handbook
An algorithm based on counterfactuals for concept learning in the Semantic Web
Applied Intelligence
DL-FOIL Concept Learning in Description Logics
ILP '08 Proceedings of the 18th international conference on Inductive Logic Programming
Statistical Learning for Inductive Query Answering on OWL Ontologies
ISWC '08 Proceedings of the 7th International Conference on The Semantic Web
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
Top-down induction of first-order logical decision trees
Artificial Intelligence
Concept learning in description logics using refinement operators
Machine Learning
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
Concept Induction in Description Logics Using Information-Theoretic Heuristics
International Journal on Semantic Web & Information Systems
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A new framework for the induction of logical decision trees is presented. Differently from the original setting, tests at the tree nodes are expressed with Description Logic concepts. This has a number of advantages: expressive terminological languages are endowed with full negation, thus allowing for a more natural division of the individuals at each test node; these logics support the standard ontology languages for representing knowledge bases in the Semantic Web. A top-down method for inducing terminological decision trees is proposed as an adaptation of well-known tree-induction methods. This offers an alternative way for learning in Description logics as concept descriptions can be associated to the terminological trees. A new version of the System TermiTIS, implementing the methods, is experimentally evaluated on ontologies from popular repositories.