ROBOKOP KG and KGB: Integrated Knowledge Graphs from Federated Sources
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Updated: Jan 3, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
Published on: June 13, 2025
Chris Bizon1, Steven Cox1, James Balhoff1
1Renaissance Computing Institute , University of North Carolina at Chapel Hill , Chapel Hill , North Carolina 27517 , United States.
Abstract:
A proliferation of data sources has led to the notional existence of an implicit Knowledge Graph (KG) that contains vast amounts of biological knowledge contributed by distributed Application Programming Interfaces (APIs). However, challenges arise when integrating data across multiple APIs due to incompatible semantic types, identifier schemes, and data formats. We present ROBOKOP KG ( http://robokopkg.renci.org ), which is a KG that was initially built to support the open biomedical question-answering application, ROBOKOP (Reasoning Over Biomedical Objects linked in Knowledge-Oriented Pathways) ( http://robokop.renci.org ). Additionally, we present the ROBOKOP Knowledge Graph Builder (KGB), which constructs the KG and provides an extensible framework to handle graph query over and integration of federated data sources.
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