Data-Driven Discovery Science with FAIR Knowledge Graphs Despite the existence of vast amounts of biomedical data, these remain difficult to find and to productively reuse in machine learning and other Artificial Intelligence technologies. In this talk, I will discuss the role of the FAIR Guiding Principles to make AI-ready biomedical data, and their representation as knowledge graphs not only enables powerful ontology-backed semantic queries, but also can be used to predict missing information, as well as to check the quality of knowledge collected. The main idea of the talk is to introduce the FAIR principles (what they are and what they are not), and how their application with semantic web technologies (ontologies/linked data) creates improved possibilities for large scale data integration, answering sophisticated questions using automated reasoners, and predicting new relations/validating data using graph embeddings. The audience will gain insight into the state of the art in a carefully presented manner that introduces principles, approaches, and outcomes relevant to Health AI.