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Updated: Jun 24, 2025

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

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

Published on: October 13, 2023

KRAGEN: a knowledge graph-enhanced RAG framework for biomedical problem solving using large language models.

Nicholas Matsumoto¹, Jay Moran¹, Hyunjun Choi¹

¹Department of Computational Biomedicine, Center for Artificial Intelligence Research and Education, Cedars Sinai Medical Center, West Hollywood, CA 90069, United States.

Bioinformatics (Oxford, England)

|June 3, 2024

Summary

This summary is machine-generated.

The Knowledge Retrieval Augmented Generation ENgine (KRAGEN) addresses large language model limitations by integrating knowledge graphs and advanced prompting. This novel approach enhances factual consistency and reduces irrelevant information in complex problem-solving.

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Last Updated: Jun 24, 2025

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Published on: September 20, 2018

Area of Science:

Artificial Intelligence
Natural Language Processing
Knowledge Representation

Background:

Large language models (LLMs) face challenges in factual consistency and data accuracy, particularly in specialized domains like biomedicine.
LLM limitations include generating hallucinations and being influenced by noisy data, impacting trustworthiness and compliance.

Purpose of the Study:

To introduce the Knowledge Retrieval Augmented Generation ENgine (KRAGEN) as a solution for complex problem-solving using LLMs.
To mitigate LLM limitations such as hallucinations and improve the accuracy of generated insights.

Main Methods:

KRAGEN combines knowledge graphs, Retrieval Augmented Generation (RAG), and advanced prompting techniques.
Knowledge graphs are converted into a vector database for efficient fact retrieval via RAG.
Employs graph-of-thoughts (GoT) prompting to decompose problems, retrieve relevant knowledge, and synthesize solutions.

Main Results:

KRAGEN effectively limits hallucinations by grounding responses in retrieved factual knowledge.
The system dynamically breaks down complex problems into manageable subproblems for systematic resolution.
Provides a graph visualization for users to assess the logic and structure of the problem-solving process.

Conclusions:

KRAGEN offers a robust framework for enhancing the reliability and accuracy of LLM-driven problem-solving.
The integration of knowledge graphs and advanced RAG techniques represents a significant advancement in AI.
KRAGEN is available as open-source, facilitating further research and application development.