The FAIR data principles enable companies to make data reusable and human- and machine-actionable. More and more organizations from the Pharma and Life Sciences community are striving to apply these principles internally and to utilize publicly available data to advance R&D and other functions. One important aspect in this process is modeling domain expert knowledge – including the utilization of public knowledge in the form of ontologies and mapping this knowledge to internal terms, processes, use cases and needs. The process of knowledge modeling (or semantic modeling) to capture the meaning of data and ensure correct interpretation and usage is a key pillar to FAIR data. This process can only succeed if domain experts / SMEs can perform the modeling task without relying on ontologists or other experienced data modelers. In this tutorial, we will present an approach that supports this process and enables you, the domain expert, to model your respective domain. We will cover examples from R&D research and target discovery, including the use of public ontologies like Mondo or NCI, through clinical trial scoping and consent modeling, to manufacturing, supply chain management and market access. The session will include a mix of theoretical learning, best practices and hands-on modeling exercises to familiarize you with the concepts and give you a chance to apply this knowledge.