Ontology Model for Automatic Duplication Reducing for Industrial Standard Assessment

The entrepreneurs found that data science was redundant and wasteful of management resources in several industry-standard assessments which are duplicated. Due to the lack of advanced technology professionals, the application of highly complex technology is a barrier to implementation. Ontology models and artificial intelligence are effective means of solving such problems. Therefore, this study aims to address the ontological problems that can interpret and rule out complex artificial intelligence issues and ambiguous interpretations. It allows quick and repeatable actions to be performed automatically without an administrator. The proposed ontology model reflects the requirements in the industry-standard while K-Nearest Neighbor (K-NN) algorithm separates the data groups and integrated assessment suite that minimizes the redundancy of requirements from standards.