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Concept Learning in Description Logics Using Refinement Operators

By Jens Lehmann and Pascal Hitzler

Abstract

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

Topics: Description logics, Refinement operators, Inductive logic programming, Semantic web, OWL, Structured machine learning, Bioinformatics, Communication, Communication Technology and New Media, Computer Sciences, Databases and Information Systems, Life Sciences, OS and Networks, Physical Sciences and Mathematics, Science and Technology Studies, Social and Behavioral Sciences
Publisher: CORE Scholar
Year: 2010
OAI identifier: oai:corescholar.libraries.wright.edu:cse-1068
Provided by: CORE
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