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Updated: Oct 27, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
Published on: October 13, 2023
Disease ontologies for knowledge graphs
Natalja Kurbatova1, Rowan Swiers2
1Data Infrastructure & Tools, Data Science & Artificial Intelligence, R&D, AstraZeneca, Cambridge, UK. natalie.kurbatova@astrazeneca.com.
Background:
Data integration to build a biomedical knowledge graph is a challenging task. There are multiple disease ontologies used in data sources and publications, each having its hierarchy. A common task is to map between ontologies, find disease clusters and finally build a representation of the chosen disease area. There is a shortage of published resources and tools to facilitate interactive, efficient and flexible cross-referencing and analysis of multiple disease ontologies commonly found in data sources and research.
Results:
Our results are represented as a knowledge graph solution that uses disease ontology cross-references and facilitates switching between ontology hierarchies for data integration and other tasks.
Conclusions:
Grakn core with pre-installed "Disease ontologies for knowledge graphs" facilitates the biomedical knowledge graph build and provides an elegant solution for the multiple disease ontologies problem.
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