Property Graph for Exploring Similarities in Real-World Oncology Standards

Real-world oncology standards have reached a high level of maturity, yet documentation about the relationship between standards is limited. Mapping standards similarities is the foundational step towards enabling data integration and sharing across different standards. Entity mapping is an iterative process to find corresponding elements in different semantic spaces with incremental specificity. It can identify areas of overlap and complement, thereby facilitating manual and automated integration efforts. We aim to provide a baseline description by comparing the entities and standard values in real-world oncology standards using property graph. Empirical exploration using property graph showed that all standards shared entities that represent important oncology domains spanning across demographic, diagnostic, and therapeutic details. Overlapping and complementary entities and values were reviewed via direct matching and automated calculation of text embedding similarity. Property graph effectively modeled complex oncology standards while enabling contextual understanding through relationship exploration. The considered approach demonstrated the feasibility of integrating diverse sources and interoperable sharing of artefacts.