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.
Predicting the Possibilistic Score of OWL Axioms Through Support Vector Regression / D. Malchiodi, C. da Costa Pereira, A.G.B. Tettamanzi (LECTURE NOTES IN ARTIFICIAL INTELLIGENCE). - In: Scalable Uncertainty Management / [a cura di] D. Ciucci, G. Pasi, B. Vantaggi. - Prima edizione. - [s.l] : Springer, 2018. - ISBN 9783030004606. - pp. 380-386 (( Intervento presentato al 12. convegno SUM tenutosi a Milano nel 2018 [10.1007/978-3-030-00461-3_28].
Predicting the Possibilistic Score of OWL Axioms Through Support Vector Regression
D. Malchiodi
Primo
;A.G.B. TettamanziUltimo
2018
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.File | Dimensione | Formato | |
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