While machine learning is one subfield of AI, this chapter discusses several methods from another subfield, semantic modelling. It supports the learning models and is an enabling technique for 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 transformations or topology changes based on the meaning of their variables. We show a sensitivity analysis based on perturbation theory that systematically scans the input of a learning model and provides analytical estimates for its output. 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.