Work

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

Year: 2025

Type: article

Abstract: PURPOSE: The goal of our work is to develop a multi-view validation framework for evaluating LLM-generated knowledge graph (KG) triples. The proposed approach aims to address the lack of established validation procedure in the context of LLM-supported KG construction. METHODS: The proposed framework evaluates the LLM-generated triples across three dimensions: semantic plausibility, ontology-grounded type compatibility, and structural importance. We demonstrate the performance for GPT-4 generated concept-specific (e.g., for medications, diagnosis, procedures) triples in the context of chronic kidney disease (CKD). RESULTS: The proposed approach consistently achieves high-quality results across evaluated GPT-4 generated triples, strong semantic plausibility (semantic score mean: 0.79), excellent type compatibility (type score mean: 0.84), and high structural importance of entities within the CKD knowledge domain (ResourceRank mean: 0.94). CONCLUSION: The validation framework offers a reliable and scalable method for evaluating quality and validity of LLM-generated triples across three views: semantic plausibility, type compatibility, and structural importance. The framework demonstrates robust performance in filtering high-quality triples and lays a strong foundation for fast and reliable medical KG construction and validation.

Source: International Journal of Computer Assisted Radiology and Surgery

DOI: 10.1007/s11548-025-03495-x

Authors: Aditya Kumar, Dilpreet Singh, Mario Cypko, Oliver Amft

Institution: University of Freiburg

Language: English

Topic: Biomedical Text Mining and Ontologies

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $4,890