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AI systems have super-human ability in making flawless logical conclusions from available data and knowledge. To unleash this power, data and knowledge have to be modelled and encoded in a machine-processable form. In this lecture, we will talk about technologies for knowledge representation, have a look at knowledge representation languages, and discuss examples. We will talk about ontology engineering, it’s fascinating history and contemporary applications. You will learn basics principles for designing and using ontologies. We will analyse examples of knowledge modelling, its pitfalls and difficulties. We then will inspect several ontologies for the representation of key scientific processes and its components: hypotheses, experimental protocols, scientific investigations. In particular, we will focus on ontologies for representing and encoding data mining and machine learning studies. We will discuss the role of data and knowledge models for the Open Science framework. Finally, we will have a close look at the case study: formalisation of knowledge for a robot scientist – an AI system for automated scientific discovery. You will learn how knowledge models and ontologies support robot scientists.