LLMs4SchemaDiscovery: A Human-in-the-Loop Workflow for Scientific Schema Mining with Large Language Models

• URL: http://arxiv.org/abs/2504.00752v1
• Date: Tue, 01 Apr 2025 13:03:33 GMT
• Title: LLMs4SchemaDiscovery: A Human-in-the-Loop Workflow for Scientific Schema Mining with Large Language Models
• Authors: Sameer Sadruddin, Jennifer D'Souza, Eleni Poupaki, Alex Watkins, Hamed Babaei Giglou, Anisa Rula, Bora Karasulu, Sören Auer, Adrie Mackus, Erwin Kessels,
• Abstract summary: Traditional schema mining relies on semi-structured data, limiting scalability.<n>This paper introduces schema-miner, a novel tool that combines large language models with human feedback to automate and refine schema extraction.
• Score: 0.22470290096767
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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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