ONTOLOGICAL APPROACH TO CREATING SUBJECT-ORIENTED TECHNOLOGIES IN THE FIELD OF IoT

Abstract

The work is devoted to the development of subject-oriented ontologies in the field of measurements and Internet of Things (IoT) technology with the aim of generating new knowledge and enhancing the efficiency of data interpretation and system operation. A generalized method of ontology construction is proposed, which defines the key components of such ontologies, including domain-specific concepts, semantic relations between them, and rules for axiom derivation. The study emphasizes that the ontology-based approach provides a universal mechanism for formalizing knowledge in measurement-related domains, ensuring semantic consistency and interoperability between heterogeneous information systems. The implementation of the ontology makes it possible to unify, at the conceptual level, the most significant elements of the subject area: basic concepts (such as measurement objects, measuring instruments, and measurement procedures), a set of semantic relations (for example, identifiers of measuring components, measurement results, and operating conditions), and functions for interpreting the results of measurement technologies (such as error estimation, determination of inter-verification and inter-calibration intervals, and evaluation of measurement accuracy).

In addition, the knowledge base is proposed to include algorithms related to measurement accuracy and reliability—covering the determination of systematic and random errors, prediction of their future values, identification of the probability distribution of error components, calculation of calibration and verification periods, and forecasting the overall reliability of IoT systems and their subsystems. This enables not only precise error analysis but also proactive reliability management, which is critically important for long-term operation of IoT infrastructures. The paper also presents a detailed analysis of toolkits available for building subject-oriented ontologies. Such tools allow for the automation of multiple stages of ontology creation, including conceptual modeling, integration with external data sources, semantic alignment of heterogeneous datasets, and the management, visualization, and analysis of ontological structures. Thus, the proposed methodology serves as a foundation for developing intelligent measurement support systems within Industry 4.0 and IoT contexts, contributing to greater transparency, adaptability, and efficiency of modern cyber-physical systems.