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Conference papers
Predicting the Possibilistic Score of OWL Axioms through Support Vector Regression
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.
Cited literature [28 references]
https://hal.archives-ouvertes.fr/hal-01894495
Contributor : Andrea G. B. Tettamanzi <>
Submitted on : Friday, October 12, 2018 - 2:55:11 PM
Last modification on : Monday, October 12, 2020 - 10:30:36 AM
Long-term archiving on: : Sunday, January 13, 2019 - 2:27:58 PM
Contributor : Andrea G. B. Tettamanzi <>
Submitted on : Friday, October 12, 2018 - 2:55:11 PM
Last modification on : Monday, October 12, 2020 - 10:30:36 AM
Long-term archiving on: : Sunday, January 13, 2019 - 2:27:58 PM
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