An iterative RAG-LLM pipeline converts relational schemas into OWL ontology fragments, achieving LLM-judged quality scores of 4.2 to 4.6 out of 5 on two medical databases.
LLMs4SchemaDiscovery: A Human-in-the-Loop Workflow for Scientific Schema Mining with Large Language Models
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abstract
Extracting structured information from unstructured text is crucial for modeling real-world processes, but traditional schema mining relies on semi-structured data, limiting scalability. This paper introduces schema-miner, a novel tool that combines large language models with human feedback to automate and refine schema extraction. Through an iterative workflow, it organizes properties from text, incorporates expert input, and integrates domain-specific ontologies for semantic depth. Applied to materials science--specifically atomic layer deposition--schema-miner demonstrates that expert-guided LLMs generate semantically rich schemas suitable for diverse real-world applications.
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Retrieval-Augmented Generation of Ontologies from Relational Databases
An iterative RAG-LLM pipeline converts relational schemas into OWL ontology fragments, achieving LLM-judged quality scores of 4.2 to 4.6 out of 5 on two medical databases.