METHODS OF ONTOLOGY ENGINEERING IN DISTRIBUTED INFORMATION SYSTEMS - SHAPYRO, OLEKSII; LIASHYK, VOLODYMYR (Herald of Khmelnytskyi National University. Technical sciences, 2025)
This paper presents a comprehensive analysis of the application of the mathematical apparatus of ontology engineering for the development, implementation, and maintenance of distributed information systems, demonstrated through the example of energy information systems. The study provides an in‐depth examination of the classification problem of technological processes that accompany the design and deployment of distributed information systems. It reviews current methods and introduces novel theoretical approaches while reporting on practical experiments aimed at implementing these methods in real-world scenarios. A key emphasis of the work is placed on the rigor of formalization and the employment of both taxonomic and meronomic methods, combined with categorical analysis and the construction of decision-making models. Fundamental concepts such as “natural classification,” “archetype,” and “homomorphism” are explored in detail, alongside the underlying principles of organizing information systems based on ontologies. In doing so, the research demonstrates how formal mathematical models can capture both the structural and semantic nuances inherent in complex systems. Moreover, the paper investigates the integration of heterogeneous data sources and the unification of knowledge representation through ontological models. The proposed approaches allow for the identification and formalization of intrinsic relationships between system components, which are essential for establishing a coherent and adaptable classification framework. The research further includes the development and experimental evaluation of a prototype decision support system, implemented in Python, that utilizes clustering algorithms and production-rule mechanisms for automatic knowledge inference. Experimental results indicate that the ontology-based approach not only enhances classification accuracy—achieving figures close to 95% compared to expert assessments—but also improves system response times and adaptability in dynamic environments. Overall, the study demonstrates the potential of the described mathematical framework for managing and optimizing distributed information systems in the energy sector. It lays a robust theoretical foundation for future research in ontology-based system integration and adaptive decision support, outlining directions for further enhancements such as the incorporation of fuzzy logic techniques and the establishment of unified standards for interoperability among diverse ontological systems.