Affiliations: [a] School of Computer Engineering, Jiangsu University of Technology, Changzhou, Jiangsu, China
| [b] Jiangsu Key Laboratory of Recycling and Reuse Technology for Mechanical and Electronic Products, Changshu, Jiangsu, China
| [c] Department of Applied Mathematics, Iran University of Science and Technology, Narmak, Tehran, Iran
| [d] School of Information, Yunnan Normal University, Kunming, Yunnan, China
Correspondence:
[*]
Corresponding author. Linli Zhu, School of Computer Engineering, Jiangsu University of Technology, Changzhou, Jiangsu 213001, China. Tel.: +86 13401656599; E-mail: [email protected].
Abstract: In recent years, the ontology problem has gained attention in machine learning and it has many applications in various fields. Ontology similarity computation plays a critical role in practical implementations. In ontology learning setting, one learns a real-valued score function that assigns scores to ontology vertices. Then, the similarity between vertices is weighted in terms of the difference between their corresponding scores. The purpose of this paper is to report a new ontology learning algorithm for ontology similarity measuring and ontology mapping by means of magnitude preserving. The classes of ontology loss function are considered in regularization ontology framework, the ontology function is supposed to be linear, and the gradient descent implement is presented for getting the optimal ontology function. The result data from our four simulation experiments imply that the new proposed ontology trick has high efficiency and accuracy in biology and plant science with regard to ontology similarity measure, and humanoid robotics and education science with regard to ontology mapping.
