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

Dario Malchiodi 1 Andrea G. B. Tettamanzi 2
2 WIMMICS - Web-Instrumented Man-Machine Interactions, Communities and Semantics
CRISAM - Inria Sophia Antipolis - Méditerranée , Laboratoire I3S - SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
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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https://hal.archives-ouvertes.fr/hal-01822443
Contributor : Andrea G. B. Tettamanzi <>
Submitted on : Monday, June 25, 2018 - 10:17:12 AM
Last modification on : Tuesday, May 26, 2020 - 6:50:41 PM

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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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