Predicting the Possibilistic Score of OWL Axioms Through Support Vector Regression

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