A Semantic-based Variables Selection for Ontology Learning Taking Jaccard Alignment as Case

Abstract

In the past decade, research on numerical schemes on ontology learning has been quite intensive. Several learning approaches have been proposed to help developers during the maintenance process. Most of the proposed approaches do not process the curse of dimensionality and the semantic contained in the information structure. A novel semantic-based method for ontology learning, which can provide improvement in both alignment and learning, is described. Good comparisons with the experimental studies demonstrate the multidisciplinary applications of our approach.