An Architectural Methodology for Developing Domain Ontology for Efficient Knowledge Management for AI Systems

In the context of artificial intelligence (AI) and knowledge representation, ontology is especially important for organizing and presenting domain-specific knowledge. The study highlights how important it is to have a thorough understanding of domain material in order for AI systems to work well. The aim of this research is to offer a clear, organized architectural technique for developing domain ontology in the ever-evolving domains of knowledge management and information technology. The main objective is to tackle issues with ontology engineering, scalability, and adaptability in the contemporary knowledge-driven period. The suggested approach deviates from conventional ontology construction techniques by embracing a collaborative, modular, and extensible paradigm. There are five steps in the process, and each one is regulated by particular guidelines and regulations designed to ensure effective ontology engineering. This work offers a novel architectural approach to the construction of ontology, filling a vacuum in the literature by providing a systematic way to create new ontology or reuse preexisting ones. The process speeds up the creation of ontology, allowing programmers to concentrate on the connections between ideas and objects inside a domain. The study effectively implements the recommended methods to develop a controlled vocabulary and computer science ontology that are specifically suited for a course recommendation system by AI systems. The results show that the suggested method covers about 95% of the problems in ontology development. The study makes a significant contribution to society by encouraging effective ontology development, which is essential for the advancement of knowledge management and AI.