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Conference Papers Year : 2018

Predicting the Possibilistic Score of OWL Axioms through Modified Support Vector Clustering

Abstract

We address the problem of predicting a score for candidate axioms within the context of ontology learning. The prediction is based on a learning procedure based on support vector clustering originally developed for inferring the membership functions of fuzzy sets, and on a similarity measure for subsumption axioms based on semantic considerations and reminiscent of the Jaccard index. We show that the proposed method successfully learns the possibilistic score in a knowledge base consisting of pairs of candidate OWL axioms, meanwhile highlighting that a small subset of the considered axioms turns out harder to learn than the remainder.
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Dates and versions

hal-01822443 , version 1 (25-06-2018)

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Dario Malchiodi, Andrea G. B. Tettamanzi. Predicting the Possibilistic Score of OWL Axioms through Modified Support Vector Clustering. 33rd Symposium on Applied Computing (SAC 2018), Apr 2018, Pau, France. ⟨10.1145/3167132.3167345⟩. ⟨hal-01822443⟩
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