Abstract
Automatic knowledge base enrichment methods rely critically on candidate axiom scoring. The most popular scoring heuristics proposed in the literature are based on statistical inference. We argue that such a probability-based framework is not always completely satisfactory and propose a novel, alternative scoring heuristics expressed in terms of possibility theory, whereby a candidate axiom receives a bipolar score consisting of a degree of possibility and a degree of necessity. We evaluate our proposal by applying it to the problem of testing SubClassOf axioms against the DBpedia RDF dataset.
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Tettamanzi, A.G.B., Faron-Zucker, C., Gandon, F. (2014). Testing OWL Axioms against RDF Facts: A Possibilistic Approach. 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_39
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DOI: https://doi.org/10.1007/978-3-319-13704-9_39
Publisher Name: Springer, Cham
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