2025/04/01 by Sameer Sadruddin, Sadruddin, Sameer, Jennifer D’Souza +16 · 4 citations
Biochemistry, Genetics and Molecular Biology · Decision Sciences · Materials Science · #Biomedical Text Mining and Ontologies #Conceptual schema #Data modeling #Language model #Limiting #Machine Learning in Materials Science #Schema (genetic algorithms) #Schema matching #Scientific Computing and Data Management #Unstructured data #Workflow
paper · pdf · doi:10.48550/arxiv.2504.00752
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2025/04/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.