Attributive concept descriptions with complements
Artificial Intelligence
Ontology Learning for the Semantic Web
IEEE Intelligent Systems
The Description Logic Handbook
The Description Logic Handbook
An algorithm based on counterfactuals for concept learning in the Semantic Web
Applied Intelligence
Hybrid Learning of Ontology Classes
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
A refinement operator based learning algorithm for the ALC description logic
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
Foundations of refinement operators for description logics
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
Class expression learning for ontology engineering
Web Semantics: Science, Services and Agents on the World Wide Web
Introduction to linked data and its lifecycle on the web
RW'11 Proceedings of the 7th international conference on Reasoning web: semantic technologies for the web of data
An approach to parallel class expression learning
RuleML'12 Proceedings of the 6th international conference on Rules on the Web: research and applications
Universal OWL axiom enrichment for large knowledge bases
EKAW'12 Proceedings of the 18th international conference on Knowledge Engineering and Knowledge Management
Introduction to linked data and its lifecycle on the web
RW'13 Proceedings of the 9th international conference on Reasoning Web: semantic technologies for intelligent data access
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Semantic Web, in order to be effective, needs automatic support for building ontologies, because human effort alone cannot cope with the huge quantity of knowledge today available on the web. We present an algorithm, based on a Machine Learning methodology, that can be used to help knowledge engineers in building up ontologies.