Automatic relationship construction in domain ontology engineering using semantic and thematic graph generation process and convolution neural network

Abstract

In recent studies, Ontology construction plays an important role in translating raw text into useful knowledge. The proposed methodology supports efficient retrieval using multidimensional theory and implements integrated data training techniques before enter the trial process. The proposed approach has used the Semantic and Thematic Graph Generation Process to extract useful knowledge, and uses data mining techniques and web solutions to present knowledge as well as improve search speed and information retrieval accuracy. Established ontology can help clarify what it means for different ideas and relationships. Due to the rise of the ontology repository, the process of matching can take a long time. To avoid this, the method produces a hierarchical structure with in-depth interpretation of the data. A system is designed to remove domain dependencies using a dynamic labeling scheme using basic theorem, and the results show that it is possible to automatically and independently construct an independent domain.