SVM-Based Multi-Dividing Ontology Learning Algorithm and Similarity Measuring on Topological Indices - Zhu, Linli; Hua, Gang; Baskonus, Haci Mehmet; Gao, Wei (Frontiers in Physics, 2020)
Ontology is one of the oldest terminologies in physics and is used to describe the origin and most essential attributes of all things in the world. With the development of contemporary science, ontology was given a specific definition and then introduced into the computer science as a conceptual model to describe the relationship between objects. In the past decade, the algorithms and applications in the ontology-related field have attracted the attention of many scholars. Most of the computational formulas in ontology algorithms are out of heuristic design ideas. For example, researchers use the ontology's own structural characteristics to design a calculation formula for a specific ontology from four different perspectives: name, instance, structure and attribute. In this paper, we first focus on how to apply these valuable heuristic elements to ontology learning and prediction. We combine these four heuristic elements with deep learning network and back propagation methods to obtain new ontology algorithm for prediction applications. Second, a support vector machines based multi-dividing ontology learning algorithm is proposed. Finally, we pay attention to the similarity of topological indices in chemical graph theory, and apply SVM-based multi-dividing ontology learning algorithms to give some calculation results of similarity between topological indices.