Abstract
While the number and size of Semantic Web knowledge bases increases, their maintenance and quality assurance are still difficult. In this article, we present ORE, a tool for repairing and enriching OWL ontologies. State-of-the-art methods in ontology debugging and supervised machine learning form the basis of ORE and are adapted or extended so as to work well in practice. ORE supports the detection of a variety of ontology modelling problems and guides the user through the process of resolving them. Furthermore, the tool allows to extend an ontology through (semi-)automatic supervised learning. A wizard-like process helps the user to resolve potential issues after axioms are added. © 2010 Springer-Verlag.
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CITATION STYLE
Lehmann, J., & Bühmann, L. (2010). ORE - A tool for repairing and enriching knowledge bases. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6497 LNCS, pp. 177–193). Springer Verlag. https://doi.org/10.1007/978-3-642-17749-1_12
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