Predicting the possibilistic score of OWL axioms through modified support vector clustering.
SAC 2018: Symposium on Applied Computing Pau France April, 2018(2018)
摘要
We address the problem of predicting a score for candidate axioms within the context of ontology learning. The prediction is based on a learning procedure based on support vector clustering originally developed for inferring the membership functions of fuzzy sets, and on a similarity measure for subsumption axioms based on semantic considerations and reminiscent of the Jaccard index. We show that the proposed method successfully learns the possibilistic score in a knowledge base consisting of pairs of candidate OWL axioms, meanwhile highlighting that a small subset of the considered axioms turns out harder to learn than the remainder.
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关键词
Support Vector Clustering, Possibilistic OWL Axiom Scoring
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