The Arrowhead Framework Ontology

Modern industrial applications are increasingly built from independently developed systems that discover each other at run time and bind into mission-specific behavior. The challenge is not only collecting data, but preserving enough context to understand and diagnose the behavior of the composed system-of-systems. This paper presents the Arrowhead Framework Ontology, a Semantic Web model that turns Arrowhead deployments into an explicit, queryable operational representation. Using a climate-control demonstrator, we export timestamped RDF snapshots from live systems, ingest them into GraphDB, validate them with SHACL, and apply OWL reasoning to derive additional facts about structure and dependencies. We show that this enables practical diagnosis and impact analysis via SPARQL, even though none of the individual systems were designed to answer such questions. The same representation also supports digital threads for traceability and lifecycle insight, and it can be accessed through a natural-language interface that generates SPARQL queries on behalf of operators. Together, these results illustrate how explicit semantics can provide a non-intrusive path to more transparent and evolvable industrial systems.