Resumen
For risk management decisions to be objective and effective, they must be based on accurate data, presented in a timely manner and processed from a variety of sources. The basis for the integration of such data is an ontology that provides a basis for building risk assessment models using various artificial intelligence methods and is capable of continuously evolving in response to changes in the internal and external environment of the organization. Approaches to building an end-to-end automated process of creating and developing an ontology of a risk management system domain based on methods proven by world practice are proposed. The proposed approaches cover the main tasks of ontological engineering, taking into account the specifics of risk management, incl. (1) conceptualization of the subject area in relation to the problems of risk management, (2) ontological reengineering based on the conceptualization of the legacy risk management system and the study of the historical array of results of the application of such a system, (3) assessment of the quality of the ontology, (4) evolution of the ontology in response to changes internal and external environment of the organization. Examples of ontology application in the intellectual risk management system are presented.