Abstract
In today's world, artificial intelligence (AI) and machine learning (ML) play a key role in many areas of human activity. However, the complexity and insufficient definition of the relevant models can create problems of their efficiency, interpretability and management when implementing technologies based on AI and ML. That is why it is important to use ontological engineering methods to improve these models. Ontological engineering is an approach that makes it possible to represent knowledge and dependencies between them in special information structures, so-called ontologies. Ontologies define concepts, relationships, and rules that describe a domain or problem area. At the same time, these information structures, in comparison with others, have extended functionality, which brings the manipulation of ontologies closer to the human thinking process.
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