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
ORE - a tool for repairing and enriching knowledge bases
ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part II
Class expression learning for ontology engineering
Web Semantics: Science, Services and Agents on the World Wide 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
AutoSPARQL: let users query your knowledge base
ESWC'11 Proceedings of the 8th extended semantic web conference on The semantic web: research and applications - Volume Part I
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
DC proposal: ontology learning from noisy linked data
ISWC'11 Proceedings of the 10th international conference on The semantic web - Volume Part II
Induction of robust classifiers for web ontologies through kernel machines
Web Semantics: Science, Services and Agents on the World Wide Web
Learning probabilistic Description logic concepts: under different Assumptions on missing knowledge
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Moving beyond SameAs with PLATO: partonomy detection for linked data
Proceedings of the 23rd ACM conference on Hypertext and social media
A bisimulation-based method of concept learning for knowledge bases in description logics
Proceedings of the Third Symposium on Information and Communication Technology
An approach to parallel class expression learning
RuleML'12 Proceedings of the 6th international conference on Rules on the Web: research and applications
Concept learning for EL++ by refinement and reinforcement
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
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
On c-learnability in description logics
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part I
Concept Induction in Description Logics Using Information-Theoretic Heuristics
International Journal on Semantic Web & Information Systems
Using Similarity-Based Approaches for Continuous Ontology Development
International Journal on Semantic Web & Information Systems
Relational concept analysis: mining concept lattices from multi-relational data
Annals of Mathematics and Artificial Intelligence
Concept adjustment for description logics
Proceedings of the seventh international conference on Knowledge capture
User-driven quality evaluation of DBpedia
Proceedings of the 9th International Conference on Semantic Systems
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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With the advent of the Semantic Web, description logics have become one of the most prominent paradigms for knowledge representation and reasoning. Progress in research and applications, however, is constrained by the lack of well-structured knowledge bases consisting of a sophisticated schema and instance data adhering to this schema. It is paramount that suitable automated methods for their acquisition, maintenance, and evolution will be developed. In this paper, we provide a learning algorithm based on refinement operators for the description logic ALCQ including support for concrete roles. We develop the algorithm from thorough theoretical foundations by identifying possible abstract property combinations which refinement operators for description logics can have. Using these investigations as a basis, we derive a practically useful complete and proper refinement operator. The operator is then cast into a learning algorithm and evaluated using our implementation DL-Learner. The results of the evaluation show that our approach is superior to other learning approaches on description logics, and is competitive with established ILP systems.