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

Creating a fast domain independent ontology through knowledge acquisition is a key problem to be addressed in the domain of knowledge engineering. Updating and validation is impossible without the intervention of domain experts, which is an expensive and tedious process. Thereby, an automatic system to model the ontology has become essential. This manuscript presents a machine learning model based on heterogeneous data from multiple domains including agriculture, health care, food and banking, etc. The proposed model creates a complete domain independent process that helps in populating the ontology automatically by extracting the text from multiple sources by applying natural language processing and various techniques of data extraction. The ontology instances are classified based on the domain. A Jaccord Relationship extraction process and the Neural Network Approval for Automated Theory is used for retrieval of data, automated indexing, mapping and knowledge discovery and rule generation. The results and solutions show the proposed model can automatically and efficiently construct automated Ontology