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 , 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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Communication dans un congrès
33rd Symposium on Applied Computing (SAC 2018), Apr 2018, Pau, France. ACM, 2018, SAC 2018: Symposium on Applied Computing, April 9–13, 2018, Pau, France. 〈https://www.sigapp.org/sac/sac2018/〉. 〈10.1145/3167132.3167345〉
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Soumis le : lundi 25 juin 2018 - 10:17:12
Dernière modification le : mardi 26 juin 2018 - 01:18:00

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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. ACM, 2018, SAC 2018: Symposium on Applied Computing, April 9–13, 2018, Pau, France. 〈https://www.sigapp.org/sac/sac2018/〉. 〈10.1145/3167132.3167345〉. 〈hal-01822443〉

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