Hybrid learning of ontology classes
J Lehmann - International Workshop on Machine Learning and Data …, 2007 - Springer
International Workshop on Machine Learning and Data Mining in Pattern Recognition, 2007•Springer
Abstract 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 …
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 …
Abstract
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.
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