Graph Laplacian Based Ontology Regularization Distance Framework for Ontology Similarity Measuring and Ontology Mapping

Abstract

The core of a large number of ontology engineering applications is,similarity measuring, and the essence of ontology mapping and its applications are also similarity computation. There are a great variety of learning techniques are designed for ontology similarity calculating in different engineering circumstances. In these learning settings, all the semantic information of concept is enclosed in the p dimensional vector. In this paper, the ontology similarity measuring is considered via geometric distance calculating between the vectors of ontology vertex corresponding to. We use regularization framework to solute the optimal ontology distance function and apply graph Laplacian to make full use of the unlabeled ontology information. Finally, two simulation experiments imply that our graph Laplacian based ontology regularization distance framework works well in plant and humanoid robotics ontology applications in which higher precision ratio results are obtained from our algorithm than previous ontology learning tricks.