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
An algorithm based on counterfactuals for concept learning in the semantic web
IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
An algorithm based on counterfactuals for concept learning in the Semantic Web
Applied Intelligence
Building rules on top of ontologies for the semantic web with inductive logic programming
Theory and Practice of Logic Programming
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
Completing description logic knowledge bases using formal concept analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Foundations of Semantic Web Technologies
Foundations of Semantic Web Technologies
Concept learning in description logics using refinement operators
Machine Learning
DL-Learner: Learning Concepts in Description Logics
The Journal of Machine Learning Research
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
Foundations of refinement operators for description logics
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
Computing least common subsumers in description logics
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Can ILP be applied to large datasets?
ILP'09 Proceedings of the 19th international conference on Inductive logic programming
OntoWiki – a tool for social, semantic collaboration
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
Creating knowledge out of interlinked data: making the web a data washing machine
Proceedings of the International Conference on Web Intelligence, Mining and Semantics
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 interaction framework of service-oriented ontology learning
Proceedings of the 21st ACM international conference on Information and knowledge management
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
Managing the life-cycle of linked data with the LOD2 stack
ISWC'12 Proceedings of the 11th international conference on The Semantic Web - Volume Part II
DEQA: deep web extraction for question answering
ISWC'12 Proceedings of the 11th international conference on The Semantic Web - Volume Part II
Concept Induction in Description Logics Using Information-Theoretic Heuristics
International Journal on Semantic Web & Information Systems
Query generation for semantic datasets
Proceedings of the seventh international conference on Knowledge capture
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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Abstract: While the number of knowledge bases in the Semantic Web increases, the maintenance and creation of ontology schemata still remain a challenge. In particular creating class expressions constitutes one of the more demanding aspects of ontology engineering. In this article we describe how to adapt a semi-automatic method for learning OWL class expressions to the ontology engineering use case. Specifically, we describe how to extend an existing learning algorithm for the class learning problem. We perform rigorous performance optimization of the underlying algorithms for providing instant suggestions to the user. We also present two plugins, which use the algorithm, for the popular Protege and OntoWiki ontology editors and provide a preliminary evaluation on real ontologies.