Predicting the Possibilistic Score of OWL Axioms Through Support Vector Regression - researchr publication related

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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. In Davide Ciucci, Gabriella Pasi, Barbara Vantaggi, editors, Scalable Uncertainty Management - 12th International Conference, SUM 2018, Milan, Italy, October 3-5, 2018, Proceedings. Volume 11142 of Lecture Notes in Computer Science, pages 380-386, Springer, 2018. [doi]

The following publications are possibly variants of this publication:

Predicting the possibilistic score of OWL axioms through modified support vector clusteringDario Malchiodi, Andrea G. B. Tettamanzi. SAC 2018: 1984-1991 [doi]

Predicting the Score of Atomic Candidate OWL Class AxiomsAli Ballout, Andrea G. B. Tettamanzi, Célia da Costa Pereira. webi 2022: 72-79 [doi]

Possibilistic testing of OWL axioms against RDF dataAndrea G. B. Tettamanzi, Catherine Faron-Zucker, Fabien Gandon. ijar, 91:114-130, 2017. [doi]

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