Abstract. Advances in medical AI and data analytics require large amounts of patient data. Due to privacy concerns, such data is not always available. Synthetic data generation promises a solution to provide the required data despite privacy restrictions. In this paper, we therefore introduce SynMedRDF, an open-source tool to generate synthetic Electronic Health Records. It ensures clinical accuracy by using real-world probabilities and correlations. The data is output as an RDF knowledge graph, enabling structure- and semantics-aware sharing, linking, and analysis.