Enabling Clinical Research with Semantic Knowledge Graphs: The Cancer Virtual Lab Platform

The secondary use of clinical data for research in life sciences is still hindered by data fragmentation, heterogeneity, and limited semantic interoperability across healthcare systems. Semantic technologies and knowledge graphs have emerged as promising enablers to overcome these challenges, yet their adoption in operational research platforms remains limited. In this paper, we present the Cancer Virtual Lab (CVL), a semantic platform designed to enable clinical research through the integration of standardized data representations, biomedical ontologies, and knowledge graph technologies. CVL leverages HL7 FHIR–based data models and RDF/OWL representations to transform heterogeneous real-world oncology data into interoperable, provenance-aware semantic knowledge graphs. The platform has been applied to a large-scale, real-world oncology dataset comprising 36,335 patient records, in which 1,093,705 hospital stay records were successfully converted into 1,151,559 distinct RDF-based FHIR resources. This semantic backbone supports advanced querying, ontology-driven reasoning, and explainable inference over clinical cohorts, enabling reproducible and transparent research workflows. Beyond data integration, CVL provides user-facing tools for researchers and clinicians, including semantic cohort identification, interactive knowledge graph exploration, and natural-language access to clinical data mediated by AI-based agents. Through architectural descriptions and illustrative screenshots, we demonstrate the feasibility and practical impact of semantic knowledge graphs as a foundation for advanced analytics, AI-driven decision support, and large-scale reuse of clinical data in life sciences research.