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 satisfactoryand 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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CITATION STYLE
Tettamanzi, A. G. B., Faron-zucker, C., & Gandon, F. (2014). Testing owl axioms against RDF facts: A possibilistic approach. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8876, pp. 519–530). Springer Verlag. https://doi.org/10.1007/978-3-319-13704-9_39
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