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Predicting the Possibilistic Score of OWL Axioms through Support Vector Regression

Dario Malchiodi 1 Célia da Costa Pereira 2 Andrea G. B. Tettamanzi 3
2 Laboratoire d'Informatique, Signaux, et Systèmes de Sophia-Antipolis (I3S) / Equipe KEIA
Laboratoire I3S - SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
3 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 : Within the context of ontology learning, we consider the problem of selecting candidate axioms through a suitable score. Focusing on subsumption axioms, this score is learned coupling support vector regression with a special similarity measure inspired by the Jaccard index and justified by semantic considerations. We show preliminary results obtained when the proposed methodology is applied to pairs of candidate OWL axioms, and compare them with an analogous inference procedure based on fuzzy membership induction.
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https://hal.archives-ouvertes.fr/hal-01894495
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Submitted on : Friday, October 12, 2018 - 2:55:11 PM
Last modification on : Thursday, March 5, 2020 - 12:20:45 PM
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Dario Malchiodi, Célia da Costa Pereira, Andrea G. B. Tettamanzi. Predicting the Possibilistic Score of OWL Axioms through Support Vector Regression. 12th International Conference on Scalable Uncertainty Management (SUM 2018), Oct 2018, Milan, Italy. pp.380-386. ⟨hal-01894495⟩

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