Industrial manufacturing processes representation is a key challenge for leveraging interoperability among business partners. The Semantic representation of information enables the creation of intelligent systems, which can interpret and understand potentially automated tasks, harnessing added-value decision-making processes. Particularly, the Semantic Web can provide a cutting-edge formal representation and knowledge-driven set of technologies to enable automation of industrial manufacturing processes. This paper presents an ontology and a proof-of-concept implementation to describe the automation of decision-making processes which model human behavior, representing the interaction with the overall environment. The model is based on different situations a problem might yield and the correspondent behavioural responses which should be generated. Using the concept of ''Situation'' as the conceptual corner-stone and building block of descriptions, we discuss how semantics provides a natural knowledge representation strategy, which eases the resource-intensive process of acquiring knowledge. The validation milestones of the system come from a real-world company where the system has been in production mode for a remarkably successful time, a mechanical parts factory.
ontology; conceptual model; process representation; knowledge representation