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Updated: Feb 6, 2026

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
Published on: October 13, 2023
Towards FAIRer Biological Knowledge Networks Using a Hybrid Linked Data and Graph Database Approach
Marco Brandizi1, Ajit Singh1, Christopher Rawlings1
1Rothamsted Research, Computational and Analytical Sciences Department, Harpenden, AL5 2JQ, UK.
None:
The speed and accuracy of new scientific discoveries - be it by humans or artificial intelligence - depends on the quality of the underlying data and on the technology to connect, search and share the data efficiently. In recent years, we have seen the rise of graph databases and semi-formal data models such as knowledge graphs to facilitate software approaches to scientific discovery. These approaches extend work based on formalised models, such as the Semantic Web. In this paper, we present our developments to connect, search and share data about genome-scale knowledge networks (GSKN). We have developed a simple application ontology based on OWL/RDF with mappings to standard schemas. We are employing the ontology to power data access services like resolvable URIs, SPARQL endpoints, JSON-LD web APIs and Neo4j-based knowledge graphs. We demonstrate how the proposed ontology and graph databases considerably improve search and access to interoperable and reusable biological knowledge (i.e. the FAIRness data principles).
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