Development of a multi-aspect ontology for decision support in production systems

Development of new data-driven concepts based on artificial intelligence methods leads to the appearance of new approaches to decision support in production systems aimed at increasing their efficiency. However, the use of existing uncoordinated data and knowledge to improve the quality of decision-making processes remains a challenging task due to the diversity of their terminologies and cognitive models. The paper proposes an approach to development of a multi-aspect ontology for decision support in production maintenance. The multi-aspect ontology is based on a layered approach to integrating knowledge about various aspects of a complex problem domain (its constituents or subdomains) while preserving the autonomy of the original ontologies. The developed multi-aspect ontology supports interaction between aspects using inference mechanisms what increases the efficiency of information flows and the degree of automation of related processes. The given example shows that the proposed approach can significantly reduce the involvement of human workers in maintenance processes in an enterprise, as well as the cognitive load on operators and maintenance technicians.