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.

Predicting the possibilistic score of OWL axioms through modified support vector clustering / D. Malchiodi, A.G.B. Tettamanzi - In: SAC '18 : Proceedings of the 33rd Annual ACM Symposium on Applied Computing / [a cura di] H. Haddad, R. L. Wainwright, R. Chbeir. - New York : ACM, 2018. - ISBN 9781450351911. - pp. 1984-1991 (( Intervento presentato al 33. convegno Annual ACM Symposium on Applied Computing tenutosi a Pau nel 2018 [10.1145/3167132.3167345].

Predicting the possibilistic score of OWL axioms through modified support vector clustering

D. Malchiodi;A.G.B. Tettamanzi
2018

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.
Possibilistic OWL axiom scoring; Support vector clustering; Software
Settore INF/01 - Informatica
2018
ACM Special Interest Group on Applied Computing (SIGAPP)
Book Part (author)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/584401
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