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.