Knowledge Discovery Approach based on Knowledge Engineering and Ontology for Recommender Intelligent Expert System

Knowledge engineering is the superset study of artificial intelligence and computer science which plays a predominant role in representing knowledge. In the real world representing knowledge is a complex mechanism since it requires a lot of human effort, proof of study, truth values, figures and documents which is epistemology. Ethnographic observations are essential for building knowledge. There are many problems in today’s era of digital transformation where a lack exists. To represent knowledge, understanding the domain concepts, and relationship existence plays a key role. To ensure authentic relations, broader thinking of ontologies is very important. In the proposed research various concepts of knowledge engineering, and knowledge management are represented and illustrated to analyze the relations among the parametric constraints and root cause of the problem Classification of ontologies theory was applied to prove the hypothetical study of theoretical literature. Upper and Lower ontologies are implemented for top-down and bottom-up approaches for specificity. The proposed architecture will enhance and provide a feasible solution for knowledge representation using applied ontologies. The proposed architecture can be applied to any domain application, however constraints and parameters are important to build. Companies dealing with semantics and ontologies with higher probability is presented.