Аннотация
This article examines the problem of automatic implementation of new concepts in ontology in the context of constant updating of knowledge and increasing the volume of textual data. The object of research is the process by which new concepts can be integrated into hierarchical and non-taxonomic structures of ontology, while maintaining their logical and semantic consistency. The purpose of the work is to create a three-stage structure to automate the search, refinement, and optimal implementation of ideas. This structure should be built on the basis of modern machine learning and natural language processing technologies. Logical inference, contrast learning and large language models and pre-prepared language models are used as research methods. At the initial stage of the framework, larger language models are used to create formal OWL axioms and semantic connections, taking into account universal and existential logical constraints. In the second stage, contrast learning is used to refine vector representations of ideas and improve classification accuracy. The third stage is the logical verification and improvement of non-taxonomic relations with the help of ontological reasoners and external sources of knowledge. The biomedical ontology of SNOMED CT and the corpus of Kazakh data were used for experimental evaluation. The results show significant statistical superiority over existing methods, as well as high accuracy of concept placement (up to 91%). The main scientific value of the work lies in the fact that it integrates logical constraints and contextual analysis into the task of ontological expansion. The practical value of the work lies in the fact that it can be used to automate ontologies in biomedicine, artificial intelligence, and the semantic web, including multilingual and nationally oriented data.