LLMs4SchemaDiscovery: A Human-in-the-Loop Workflow for Scientific Schema Mining with Large Language Models
It addresses the scalability limitation in schema mining for scientific domains like materials science, though it is incremental as it builds on existing LLM and human-in-the-loop approaches.
The paper tackled automating structured information extraction from unstructured text by introducing schema-miner, a tool that combines large language models with human feedback, and demonstrated its application in materials science for generating semantically rich schemas.
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.