Abstract
BackgroundData 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.ResultsOur 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.ConclusionsGrakn 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.
Highlights
Data integration to build a biomedical knowledge graph is a challenging task
The following sentence can describe the data integration challenge: one disease—multiple disease terms (IDs) and hierarchies originating from different disease ontologies
Grakn knowledge base We provide a Grakn schema with logical rules to make ontological inferences and a preloaded Grakn database
Summary
Disease ontologies are used for annotation, integration and analysis of biological data, and knowledge graph construction. The record from cross referencing file for “chronic kidney disease” (Fig. 1) shows that the disease term has tid = “MONDO_0005300” and defines 6 matching pairs: μ =< tid, tj > , where tj ∈ {MeSH:“D007676”, UMLS:”C0022661”, EFO: “EFO_0003884”, NCIT:”NCIT_C80078”, DOID: “DOID_784”, ICD-10: “N18.9”}. This induces 6 triples of the form < tid, tj, 1 > in our ontological matching, all other tj will map to.
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