While machine learning is a subfield of AI, this chapter discusses several semantic methods to support the learning models and to enable explainability. Synonyms, taxonomies and ontologies are introduced. They digitize the true meaning of something as knowledge models, allowing us to store this meaning and make it accessible for computer programs like our machine learning. Physics-informed approaches benefit from these knowledge models, as they can now automatically request preparational steps or topology changes based on the meaning of their variables. The chapter concludes with a discussion about the different audiences and stakeholders of machine learning and AI. These audiences require different forms of explainability, different key performance indicators and different solution descriptions.