Verifying RDF Triples Using LLMs with Traceable Arguments: A Method for Large-Scale Knowledge Graph Validation

Knowledge Graph Validation Solutions Overview: A new technique uses Large Language Models (LLMs) to check RDF triples, maintaining accurate knowledge graphs (KGs) important in various industries, like biosciences. Key Value: The method overcomes LLMs limitation in tracing data sources by comparing external texts with RDF triples for verification, ensuring reliable reasoning. Benefits: - Ensures accuracy and reliability of KGs - Avoids relying solely on LLMs' internal knowledge - Demonstrated effective in biosciences testing - Achieves 88% accuracy in identifying true statements - Human supervision improves the verification process Implementation: - Apply this method to popular knowledge graphs like Wikidata - Automatically retrieve RDF triples for verification - Combine human expertise with automation for best results Conclusion: This approach automates KG verification with a focus on human oversight, demonstrating LLMs' potential in scalable and traceable knowledge validation.