Abstract
Despite an increase in the number of knowledge bases published according to Semantic Web W3C standards, many of those consist primarily of instance data and lack sophisticated schemata, although the availability of such schemata would allow more powerful querying, consistency checking and debugging as well as improved inference. One of the reasons why schemata are still rare is the effort required to create them. Consequently, numerous ontology learning approaches have been developed to simplify the creation of schemata. Those approaches usually either learn structures from text or existing RDF data. In this submission, we present the first approach combining both sources of evidence, in particular we combine an existing logical learning approach with statistical relevance measures applied on textual resources. We perform an experiment involving a manual evaluation on 100 classes of the DBpedia 3.9 dataset and show that the inclusion of relevance measures leads to a significant improvement of the accuracy over the baseline algorithm.
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Bühmann, L., Fleischhacker, D., Lehmann, J., Melo, A., Völker, J. (2014). Inductive Lexical Learning of Class Expressions. In: Janowicz, K., Schlobach, S., Lambrix, P., Hyvönen, E. (eds) Knowledge Engineering and Knowledge Management. EKAW 2014. Lecture Notes in Computer Science(), vol 8876. Springer, Cham. https://doi.org/10.1007/978-3-319-13704-9_4
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DOI: https://doi.org/10.1007/978-3-319-13704-9_4
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