Efficient Querying of Federated Large-Scale Clinical RDF Knowledge Graphs in the Swiss Personalized Health Network

The Swiss Personalized Health Network developed a national federated framework for semantically described medical data, in particular hospital clinical routine data. Instead of centralizing patient-level information, hospitals perform semantic coding and standardization locally and store SPHN-compliant data in a triple store. These decentralized RDF datasets, following the FAIR (Findable, Accessible, Interoperable, Reusable) principles, together exceed 12 billion triples across more than 800,000 patients, all signed a broad consent. In this work, we address the computational challenge of efficiently querying and integrating these distributed RDF resources through SPARQL. Our use cases focus on feasibility queries and value distribution, which allow researchers to assess the potential availability of patient cohorts across hospitals without disclosing sensitive patient-level information. We present methods for optimizing SPARQL querying, tailored to the characteristics of large-scale federated and complex clinical data. We evaluate these approaches by iteratively testing optimized queries on the SPHN Federated Clinical Routine Dataset, which spans 125 SPHN concepts including demographics, diagnoses, procedures, medications, laboratory results, vital signs, clinical scores, allergies, microbiology, intensive care data, oncology, and biological samples. With this approach, we’ve built a set of rules to consider for gradually optimizing SPARQL queries. Our results demonstrate that optimized SPARQL query planning and execution can significantly reduce response times without compromising semantic interoperability.