Industrial supply chains often suffer from fragmented knowledge management, leading to inconsistencies in product quality and poor stakeholder communication. This paper introduces a semantic framework that combines ontologies, knowledge graphs, and the Shapes Constraint Language (SHACL) to ensure both semantic expressiveness and data validation in supply chain specifications. Using a case study of a mouthwash producer, we demonstrate how our approach enables automated validation of sourced supplies against predefined requirements, bridging the gap between machine inference and constraint enforcement. By leveraging standardized upper ontologies and semantic reasoning, the framework supports the creation of verifiable Digital Product Passports (DPPs) aligned with circular economy goals. Our results showcase the possibilities to improve traceability, interoperability, and compliance—key requirements for modern, sustainable manufacturing ecosystems.

Modern industrial applications are increasingly built from independently developed systems that discover each other at run time and compose into mission-specific behavior. The challenge is not only to collect data, but to preserve enough context to understand and diagnose the behavior of the resulting system-of-systems. This article presents the arrowhead framework ontology, an explicit, queryable semantic representation of Arrowhead framework deployments. Using a climate-control demonstrator, we export timestamped resource description framework (RDF) snapshots from live systems, ingest them into GraphDB, validate them with shapes constraint language, and apply web ontology language reasoning to derive additional facts about structure and dependencies. We show that this enables practical diagnosis and impact analysis via SPARQL protocol and RDF query language, 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. Together, these results illustrate how explicit semantics can provide a nonintrusive path to more transparent and evolvable industrial systems.