Automated Refinement of First-Order Horn-Clause Domain Theories
Machine Learning
Machine Learning
Foundations of Inductive Logic Programming
Foundations of Inductive Logic Programming
Inducing Logic Programs With Genetic Algorithms: The Genetic Logic Programming System
IEEE Expert: Intelligent Systems and Their Applications
An Experimental Evaluation of Coevolutive Concept Learning
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Analysis of Genetic Algorithms Evolution under Pure Selection
Proceedings of the 6th International Conference on Genetic Algorithms
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Searching the Subsumption Lattice by a Genetic Algorithm
ILP '00 Proceedings of the 10th International Conference on Inductive Logic Programming
A Refinement Operator for Description Logics
ILP '00 Proceedings of the 10th International Conference on Inductive Logic Programming
The description logic handbook: theory, implementation, and applications
The description logic handbook: theory, implementation, and applications
Genetic Programming IV: Routine Human-Competitive Machine Intelligence
Genetic Programming IV: Routine Human-Competitive Machine Intelligence
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
Evolutionary concept learning in first order logic: an overview
AI Communications
A genetic algorithms approach to ILP
ILP'02 Proceedings of the 12th 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
Ideal downward refinement in the EL description logic
ILP'09 Proceedings of the 19th international conference on Inductive logic programming
Learning terminologies in probabilistic description logics
SBIA'10 Proceedings of the 20th Brazilian conference on Advances in artificial intelligence
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
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
Learning probabilistic description logics: a framework and algorithms
MICAI'11 Proceedings of the 10th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
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
Concept Induction in Description Logics Using Information-Theoretic Heuristics
International Journal on Semantic Web & Information 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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Description logics have emerged as one of the most successful formalisms for knowledge representation and reasoning. They are now widely used as a basis for ontologies in the Semantic Web. To extend and analyse ontologies, automated methods for knowledge acquisition and mining are being sought for. Despite its importance for knowledge engineers, the learning problem in description logics has not been investigated as deeply as its counterpart for logic programs.We propose the novel idea of applying evolutionary inspired methods to solve this task. In particular, we show how Genetic Programming can be applied to the learning problem in description logics and combine it with techniques from Inductive Logic Programming. We base our algorithm on thorough theoretical foundations and present a preliminary evaluation.