# Supporting Biomedical Discovery with the ROBOKOP Graph Knowledgebase.

> **NIH NIH U24** · UNIV OF NORTH CAROLINA CHAPEL HILL · 2024 · $787,035

## Abstract

The proliferation of high-throughput technologies has led to previously unimaginable growth in biomedical
research data sets and knowledgebases. Nearly all these data and knowledge sources address specialized
areas of biomedical research, leading to natural diversity but also growing disintegration between individual
knowledgebases. This trend generates downstream inefficiencies when applying analytics to enable actionable
knowledge discovery from databases. Growing efforts, both in academia and industry, are focused on the
development of methods and tools to enable semantic integration and concurrent exploration of disparate
biomedical knowledge sources. Recent innovations include the development of biomedical `knowledge graphs'
(KGs) that support knowledge discovery through the application of querying and reasoning algorithms and tools.
Our team has contributed to these efforts by developing a KG-based question-answering system termed
Reasoning Over Biomedical Objects linked in Knowledge-Oriented Pathways (ROBOKOP). Herein, we propose
synergistic research and development efforts that aim to significantly advance the ROBOKOP graph
knowledgebase capabilities to contribute to high-impact applications across diverse biomedical research
domains. Our overarching goal is to equip users with a unique and comprehensive knowledgebase system that
supports the rapid generation of mechanistic hypotheses that can explain, validate, or predict biomedical
phenomena. We will achieve our objectives by executing studies planned under the following Specific Aims: Aim
1. Enrich and Enhance the ROBOKOP graph knowledgebase. We will enhance the data and infrastructure
of the ROBOKOP KB. Aim 2. Provide tools to explore the ROBOKOP graph knowledgebase. We will
enhance the ROBOKOP KG by developing and employing novel reasoning tools for KG mining and edge
inference. Aim 3. Prove utility and promote use of the ROBOKOP graph knowledgebase through impactful
use cases. We will conduct several collaborative proof-of-concept research applications in diverse biomedical
domains and diseases. We will actively promote community engagement, user acceptance, and broader
impact of ROBOKOP. We expect that our diverse, cutting-edge approach to research, development, and
community engagement, coupled with our high-impact biomedical applications, will lead to the formation of a
core group of regular users, promote long-term sustainability, and generate impactful new scientific knowledge
and mechanistic hypotheses for subsequent testing.

## Key facts

- **NIH application ID:** 10877106
- **Project number:** 5U24ES035214-03
- **Recipient organization:** UNIV OF NORTH CAROLINA CHAPEL HILL
- **Principal Investigator:** Christopher Bizon
- **Activity code:** U24 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $787,035
- **Award type:** 5
- **Project period:** 2022-09-05 → 2027-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10877106

## Citation

> US National Institutes of Health, RePORTER application 10877106, Supporting Biomedical Discovery with the ROBOKOP Graph Knowledgebase. (5U24ES035214-03). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10877106. Licensed CC0.

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