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Updated: Sep 11, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

A multi-view validation framework for LLM-generated knowledge graphs of chronic kidney disease.

Aditya Kumar^1,2, Dilpreet Singh³, Mario Cypko^4,3

¹Hahn-Schickard, 79110, Freiburg, Germany. aditya.kumar@hahn-schickard.de.

International Journal of Computer Assisted Radiology and Surgery

|August 14, 2025

Summary

We developed a multi-view validation framework to assess Large Language Model (LLM)-generated knowledge graph (KG) triples. This method ensures high-quality medical KG construction by evaluating semantic plausibility, type compatibility, and structural importance.

Keywords:

Chronic Kidney Disease Expert system Healthcare Knowledge graphs Knowledge-driven modelling LLMs Model-guided medicine Nephrology Semantic evaluation Triples Validation medical informatics

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Last Updated: Sep 11, 2025

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Published on: June 13, 2025

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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Published on: December 6, 2024

Area of Science:

Artificial Intelligence
Knowledge Representation
Medical Informatics

Background:

Knowledge graph (KG) construction is crucial for organizing complex information.
Large Language Models (LLMs) show promise in automating KG creation.
A standardized validation method for LLM-generated KGs is currently lacking.

Purpose of the Study:

To develop and present a multi-view validation framework for LLM-generated KG triples.
To address the absence of established validation procedures in LLM-assisted KG construction.
To evaluate the quality and validity of LLM-generated triples.

Main Methods:

The framework assesses triples across three dimensions: semantic plausibility, ontology-grounded type compatibility, and structural importance.
Demonstrated performance using GPT-4 generated concept-specific triples for chronic kidney disease (CKD).
Evaluated semantic scores, type compatibility scores, and entity structural importance (ResourceRank).

Main Results:

The framework achieved high-quality results for GPT-4 generated triples.
Strong semantic plausibility (mean score: 0.79).
Excellent type compatibility (mean score: 0.84).
High structural importance of entities within the CKD domain (mean ResourceRank: 0.94).

Conclusions:

The validation framework provides a reliable and scalable method for assessing LLM-generated KG triples.
It effectively filters high-quality triples based on semantic plausibility, type compatibility, and structural importance.
This work establishes a foundation for efficient and dependable medical KG construction and validation.