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Related Experiment Video

Updated: Nov 29, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

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Visualization Environment for Federated Knowledge Graphs: Development of an Interactive Biomedical Query Language and

Steven Cox¹, Stanley C Ahalt¹, James Balhoff¹

¹Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.

JMIR Medical Informatics

|November 23, 2020

Summary

This summary is machine-generated.

We developed Translator Query Language (TranQL), an interactive environment for querying and exploring federated biomedical knowledge graphs. TranQL enables rapid retrieval of insights from diverse data sources for translational science.

Keywords:

application programming interface biomedical data clinical data clinical practice federation knowledge graphs ontologies semantic harmonization translational science visualization

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Area of Science:

Biomedical Informatics
Translational Science
Data Federation

Background:

Integrating large biomedical knowledge graphs is challenging due to issues with query routing, answer merging, and result exploration.
Federating graph-oriented APIs requires semantic integration using common upper-level ontologies.

Purpose of the Study:

To develop an interactive environment for querying, visualizing, and exploring federated knowledge graphs.
To address challenges in semantic integration and data federation for biomedical research.

Main Methods:

Developed Translator Query Language (TranQL), a biomedical query language and web application interface.
Utilized the Biolink data model as an upper-level ontology and API standard.
Designed TranQL to map queries to federated knowledge sources and merge answers into a knowledge graph.

Main Results:

Validated TranQL with two real-world use cases relevant to translational science.
Successfully queried federated Translator API endpoints (ICEES and ROBOKOP).
Retrieved, visualized, and evaluated answers from integrated clinical, environmental, and biomedical data.

Conclusions:

TranQL facilitates asking complex questions across federated biomedical knowledge sources.
Provides rapid answers and valuable insights for translational research and clinical practice.
Enables deep exploration of federated knowledge graphs for scientific discovery.