TY - JOUR T1 - Concept learning in description logics using refinement operators JF - Machine Learning Y1 - 2010 A1 - Jens Lehmann A1 - Pascal Hitzler KW - description logics KW - Inductive logic programming KW - OWL KW - refinement operators KW - Semantic Web KW - Structured Machine Learning AB -

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.

VL - 78 UR - http://springerlink.metapress.com/content/c040n45u15qrnu44/ ER -