Industrial systems are increasingly part of connected digital environments, where physical assets, software systems, engineering models, and organizational processes must interact throughout the lifecycle of a plant. While contemporary industrial technologies make it possible to exchange data and compose services across these environments, connectivity alone does not ensure that the information exchanged is interpreted consistently. The resulting gap between technical communication and shared meaning remains a central obstacle to interoperability, traceability, and the effective reuse of industrial knowledge.

This thesis investigates how ontologies and Semantic Web technologies can provide an explicit semantic layer for industrial system-of-systems. It takes the position that semantic interoperability should not require replacing the systems, engineering tools, and standards already in use. Instead, independently developed representations can be connected through a shared, standards-grounded reference ontology while retaining the distinctions and purposes of their respective domains.

The work develops and applies this approach to service-oriented industrial systems based on the Arrowhead Framework. A runtime ontology makes the configured structure and dependencies of a running deployment available as a machine-interpretable knowledge graph. This representation is aligned through the Industrial Data Ontology with plant-design and lifecycle views, enabling relationships to be traced across operational, engineering, and asset-management contexts. The thesis further examines how different formal representations derived from the same running system support different forms of analysis.

The findings show that explicit semantic representations can complement service-oriented architectures and digital-twin approaches by making relationships between systems, services, assets, and lifecycle information queryable, inferable, and validatable. They support applications such as diagnosis, traceability, maintenance-oriented analysis, and cross-domain information integration. The thesis thereby positions semantic technologies as a means of maintaining semantic continuity between evolving industrial operations and the engineering knowledge that surrounds them.