Semantic Framework for IT-OT Integration in Industrial Environments

This paper presents a semantic framework to bridge the gap between IT-OT integration in industrial environments. The proposed solution addresses fundamental challenges of PHM (prognostics and health management) by providing contextualized semantic information from the shop floor to enterprise IT systems. Built upon an OPCUA (Open Platform Communications Unified Architecture) aggregation server architecture, the framework leverages OPCUA Information Models and companion specifications as its foundation for semantic representation. By transforming these models into knowledge graphs stored in RDF format, the system enables sophisticated semantic information retrieval through SPARQL-based semantic queries that can traverse complex relationships between equipment, processes, and operational parameters. The framework further implements GraphQL to automatically generate a Type schema derived from OPCUA types, creating a unified query interface that facilitates IT-like interaction with industrial data. This semantic approach significantly improves fault diagnostics, predictive maintenance, and anomaly detection by preserving contextual relationships that are often lost in traditional data integration methods. Furthermore, the GraphQL schema provides a structured foundation for generative AI applications to formulate contextually appropriate queries, extract relevant maintenance insights, and generate human-interpretable explanations of equipment health patterns, all while maintaining semantic fidelity across the IT-OT boundary. The vertical integration capability ensures that domain-specific models remain coherent across organizational levels such as line, area, floor, etc., enabling PHM practitioners to implement more effective condition-based maintenance strategies with improved visibility into causal factors affecting equipment reliability and performance.