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Disease ontologies for knowledge graphs.

Natalja Kurbatova1, Rowan Swiers2

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Summary
This summary is machine-generated.

Building a biomedical knowledge graph is complex due to multiple disease ontologies. Our solution integrates these ontologies, enabling efficient data analysis and knowledge discovery.

Keywords:
Data integrationKnowledge graphOntologies

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Area of Science:

  • Biomedical Informatics
  • Knowledge Representation
  • Ontology Engineering

Background:

  • Data integration for biomedical knowledge graphs is challenging due to diverse disease ontologies with unique hierarchies.
  • Mapping between ontologies, identifying disease clusters, and representing specific disease areas are common but difficult tasks.
  • A lack of accessible tools hinders interactive, efficient, and flexible cross-referencing of multiple disease ontologies.

Purpose of the Study:

  • To present a knowledge graph solution for integrating multiple disease ontologies.
  • To facilitate cross-referencing and analysis of disparate disease ontologies.
  • To address the challenges in building comprehensive biomedical knowledge graphs.

Main Methods:

  • Development of a knowledge graph solution leveraging disease ontology cross-references.
  • Implementation of functionality for seamless switching between different ontology hierarchies.
  • Utilizing Grakn core with pre-installed "Disease ontologies for knowledge graphs".

Main Results:

  • A functional knowledge graph solution that effectively integrates multiple disease ontologies.
  • Demonstrated ability to switch between ontology hierarchies for enhanced data integration.
  • Successful representation of disease areas through integrated ontological data.

Conclusions:

  • Grakn core, augmented with "Disease ontologies for knowledge graphs", simplifies biomedical knowledge graph construction.
  • The proposed solution offers an elegant and effective approach to managing multiple, heterogeneous disease ontologies.
  • Facilitates more robust data integration and analysis in biomedical research.