A Polynomial Approach to the Constructive Induction of Structural Knowledge
Machine Learning - Special issue on evaluating and changing representation
Tow-down Induction of Logic Programs from Incomplete Samples
ILP '96 Selected Papers from the 6th International Workshop on Inductive Logic Programming
Combining Divide-and-Conquer and Separate-and-Conquer for Efficient and Effective Rule Induction
ILP '99 Proceedings of the 9th International Workshop on Inductive Logic Programming
A Refinement Operator for Description Logics
ILP '00 Proceedings of the 10th International Conference on Inductive Logic Programming
ILP '00 Proceedings of the 10th International Conference on Inductive Logic Programming
Learning from examples with unspecified attribute values
Information and Computation
Description logic programs: combining logic programs with description logic
WWW '03 Proceedings of the 12th international conference on World Wide Web
Downward Refinement in the ALN Description Logic
HIS '04 Proceedings of the Fourth International Conference on Hybrid Intelligent Systems
The Description Logic Handbook
The Description Logic Handbook
An algorithm based on counterfactuals for concept learning in the Semantic Web
Applied Intelligence
ESWC '07 Proceedings of the 4th European conference on The Semantic Web: Research and Applications
Hybrid Learning of Ontology Classes
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
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
Metric-based stochastic conceptual clustering for ontologies
Information Systems
Completing description logic knowledge bases using formal concept analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Handbook on Ontologies
Concept learning in description logics using refinement operators
Machine Learning
DL-Learner: Learning Concepts in Description Logics
The Journal of Machine Learning Research
Learnability of description logic programs
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
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
Ontology Learning and Population: Bridging the Gap between Text and Knowledge - Volume 167 Frontiers in Artificial Intelligence and Applications
Induction of concepts in web ontologies through terminological decision trees
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Class expression learning for ontology engineering
Web Semantics: Science, Services and Agents on the World Wide Web
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This paper presents an approach to ontology construction pursued through the induction of concept descriptions expressed in Description Logics. The author surveys the theoretical foundations of the standard representations for formal ontologies in the Semantic Web. After stating the learning problem in this peculiar context, a FOIL-like algorithm is presented that can be applied to learn DL concept descriptions. The algorithm performs a search through a space of candidate concept definitions by means of refinement operators. This process is guided by heuristics that are based on the available examples. The author discusses related theoretical aspects of learning with the inherent incompleteness underlying the semantics of this representation. The experimental evaluation of the system DL-Foil, which implements the learning algorithm, was carried out in two series of sessions on real ontologies from standard repositories for different domains expressed in diverse description logics.