Abstract
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.
Highlights
The term “ontology” first appeared in philosophy and physics, and was used to describe the most original appearance and most essential characteristics of things
Being a model for conceptual semantic storage, analysis and management, it has drawn great attention from the fields of computer science and information technology. When it comes to twenty-first century, scholars from various disciplines use ontology tools to deal with various engineering problems, making ontology popular in multidisciplinary research, such as biology, pharmacy, education systems, psychology, medicine, neuroscience, and nanotechnology
Our main aim is to measure the similarity of chemical topological indices in terms of Support Vector Machine (SVM) based multidividing ontology learning algorithm which is stated in section multi-dividing ontology algorithm based on support vector machines
Summary
The term “ontology” first appeared in philosophy and physics, and was used to describe the most original appearance and most essential characteristics of things. Being a model for conceptual semantic storage, analysis and management, it has drawn great attention from the fields of computer science and information technology. When it comes to twenty-first century, scholars from various disciplines use ontology tools to deal with various engineering problems, making ontology popular in multidisciplinary research, such as biology, pharmacy, education systems, psychology, medicine, neuroscience, and nanotechnology. Based on the semantic similarity of disease, an ontology-based fixed genome sequencing and gene sequencing algorithm was proposed by Cannataro et al [1]. Wei et al [3] developed NaviGO for the visualizations and analysis of functional similarities and associations between GO terms and genes. Yang and Tang [5] proposed an approach to combine the faction-based prediction method and GO gene ontology annotation to overcome the interference of false positive and false negative interactions
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