Background: The growing amount of biomedical knowledge about cancer in combination with genome-scale patient profiling data offers unprecedented opportunities for personalized oncology. However, the large amounts of knowledge and data require scalable approaches to providing actionable information to support clinicians in decision-making [1]. Objective: To develop software and methods that integrate all relevant clinical and genomic data about patients and that enables the discovery of optimal personalized treatment options, together with the supporting literature knowledge and data. Methods: We exploit a Semantic Knowledge Graph (SKG), a type of database that represents medical data in the form of objects and relationships, linking previously unconnected information across several cancer databases. To build up this SKG (OncodashKB), we use the BioCypher library [2]. We then integrate clinical data from patients with high-grade serous ovarian cancer, including information on genome changes collected as part of the DECIDER project (https://deciderproject.eu). The SKG can then be queried to gather evidence paths linking patient-specific alterations to actionable drugs. Results: Our approach provides a fully automated, systematic, and reproducible data integration workflow, along with the use of existing expert-made ontologies to provide interoperability and semantic descriptions. The integrated data is assessed by experts on molecular tumor boards and allows for the exploration of relevant clinical and genomic patient data in a visually accessible format, designed for ease of interpretation by clinicians. Importantly, we expect the system to reveal compelling findings/indications from multiple lines of converging evidence to a strong case from multi-omics patient data and a broad and automated review of the latest clinical and experimental knowledge. Conclusion: Decision support systems using graph databases emerge as valuable tools by revealing new connections between various patient data and treatment options shown in an easy-to-understand format.