Industry 4.0, has significantly altered industrial processes by integrating advanced technologies such as Cyber-Physical Systems, Internet of Things (IoT), cloud computing and AI. Despite these advancements, the industry is facing challenges with achieving effective communication and interoperability. Through the wide variety of advances that has been made, an upstream of different heterogeneous data sources and protocols have emerged, even when studying a specific domain such as the industrial sector. It has led to fragmented interoperability and somewhat unreliable information exchange. This thesis presents research that explores the potential of ontologies and semantic modeling to address some of these challenges by providing explicit descriptions of concepts and their relationships, thereby enhancing a shared understanding and vocabulary, improving interoperability and stakeholder communication. Furthermore, efforts are made to enable system communication through OPC UA and the Arrowhead framework, to enable seamless interoperability.

Despite the possible benefits of ontologies, challenges such as the need for experience and expertise in ontology development is required to created and maintain their reliability. Introducing the Industrial Data Ontology (IDO) as an industrial upper ontology, a newly adopted ISO standard, enables a higher level of knowledge abstraction. IDO describes industrial assets and processes throughout their lifecycle.

The findings underscore the transformative potential of semantic models, ontologies, and seamless interoperability to enhance the quality of industrial information exchange and a more sustainable and reliable process. Future directions include exploring the integration of real-time data with semantic benefits, enhancing business transaction process, and implement semantic explicitness to a Service-Oriented Architecture (SOA) such as the Arrowhead framework.

In recent years, there has been a significant rise in the importance of traceability, particularly concerning the origins and manufacturing processes of specific products. This heightened emphasis on traceability has been driven by governmental and corporate initiatives aimed at advancing circular economy strategies. Digital Product Passports (DPP) are regarded as a prominent tool to enable decentralized cross-sectorial data connections. A DPP is a data set that summarizes the components, materials and chemical substances in a product which is complemented and information on repair events, disposal instructions, and environmental impact information. 

Gathering such information is a highly complex cross-sectorial endeavor. The requirements and standards on how data shall be designed and shared need to be clearly stated to achieve sustainable benefits. Semantic modeling using open standards can provide the DPP system with a shared knowledge of domain-specific structures for how data should be created and what queries can be performed. Moreover, the intricate data interconnections and traceability requirements require the enabling of bidirectional tracing of products, actors, and resources. The Interplanetary File System (IPFS) in combination with Resource Description Framework (RDF) allows for the decentralized storage of structured data. This can then be complemented with IPNS for the traversal of interconnected data nodes. 

This paper presents a Proof of Concept (PoC) comprising several microservices: The DPP management system which uses IPFS for decentralized data storage and IPNS for bidirectional traceability. The DPP definition system that utilizes semantic modeling technologies to standardize and provide cross-sectoral knowledge on design standards for the implemented passports. Furthermore, we utilize RDF-based ontologies for collecting decentralized interconnected data. This approach allows for semantic querying using the SPARQL protocol, thereby enhancing the system’s ability to retrieve and analyze data.