Ontology-based Analysis of Neuralstem Learning Based on Data Integration

This paper explores the problem of automated ontology construction based on the semantic analysis of natural language texts, with the goal of improving data integration and enhancing knowledge extraction in intelligent domain environments (IDEs). A key challenge addressed is the limited effectiveness of current technologies when semantic data sources are incomplete or inconsistent, resulting in poor reusability and integration with existing information systems.The proposed approach focuses on the development of ontologies for training artificial intelligence (AI) and neural networks (NN) through automated processing of textual data. The methodology is grounded in cognitive-semantic analysis, utilizing principles from category theory, mathematical and relational algebra, and formal logic. This enables the transformation of ontological dictionaries and semantic structures derived from natural language into standardized open knowledge representation formats such as RDF and OWL.The paper also presents a classification framework for information units and their sources, highlighting structural features of semantic resources relevant to ontology modeling. A unified ontology quality assessment criterion is introduced, which takes into account the alignment of cognitive and semantic structures as well as the usability of ontologies for continuous updates and automated evaluation.An additional contribution is the integration of metaheuristic optimization techniques inspired by biological systems, allowing for adaptive and scalable algorithmic structures independent of specific problem domains. These methods enhance the efficiency of neural learning processes and support the evolution of optimization strategies within smart information systems.The results demonstrate that the proposed framework provides a viable foundation for ontology-based knowledge engineering, particularly in systems that require high levels of adaptability, semantic coherence, and computational efficiency.