Knowledge representation of a multi-centre adolescent and young adult (AYA) cancer infrastructure; development of the STRONG AYA knowledge graph

Abstract

Purpose: rare diseases are difficult to fully capture, and regularly call for large, geographically dispersed initiatives. Such initiatives are often met with data harmonization challenges. These challenges render data incompatible and impede successful realization. The STRONG AYA project is such an initiative, specifically focusing on adolescent and young adult (AYAs) with cancer. STRONG AYA is setting up a federated data infrastructure containing data of varying format. Here, we elaborate on how we used health care–agnostic semantic web technologies to overcome such challenges.

Methods: we structured the STRONG AYA case-mix and core outcome measures concepts and their properties as knowledge graphs. Having identified the corresponding standard terminologies, we developed a semantic map on the basis of the knowledge graphs and the here introduced annotation helper plugin for Flyover. Flyover is a tool that converts structured data into resource description framework (RDF) triples and enables semantic interoperability. As a demonstration, we mapped data that are to be included in the STRONG AYA infrastructure.

Results: the knowledge graphs provided a comprehensive overview of the large number of STRONG AYA concepts. The semantic terminology mapping and annotation helper allowed us to query data with incomprehensible terminologies, without changing them. Both the knowledge graphs and semantic map were made available on a Hugo webpage for increased transparency and understanding.

Conclusion: the use of semantic web technologies, such as RDF and knowledge graphs, is a viable solution to overcome challenges regarding data interoperability and reusability for a federated AYA cancer data infrastructure without being bound to rigid standardized schemas. The linkage of semantically meaningful concepts to otherwise incomprehensible data elements demonstrates how by using these domain-agnostic technologies we made nonstandardized health care data interoperable.

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ORCID for Anne-Sophie Darlington: orcid.org/0000-0003-4387-7278

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