Automated Standardization of Legacy Biomedical Metadata Using an Ontology-Constrained LLM Agent
arXiv:2604.08552v2 Announce Type: replace-cross Abstract: Scientific metadata are often incomplete and noncompliant with community standards, limiting dataset findability, interoperability, and reuse. Even when standard metadata reporting guidelines exist, they typically lack machine-actionable representations. Producing FAIR datasets requires encoding metadata standards as machine-actionable templates with rich field specifications and precise value constraints. Recent work has shown that LLMs guided by field names and ontology constraints can improve metadata standardization, but these appro
Ongoing advancements in large language models (LLMs) and the increasing need for FAIR data principles in scientific research drive the development of automated metadata standardization tools.
This development addresses a critical bottleneck in scientific data reuse, improving the efficiency of research and facilitating greater interoperability across biomedical datasets.
Legacy and newly generated biomedical metadata can be more readily standardized and made machine-actionable, significantly reducing manual effort and errors in data preparation.
- · Biomedical researchers
- · Data scientists
- · LLM developers
- · Bioinformatics platforms
- · Manual data curation services
- · Organizations with siloed, non-standardized data
Research data becomes more findable, accessible, interoperable, and reusable (FAIR).
Accelerated pace of scientific discovery and potentially new insights derived from integrated datasets.
New research paradigms emerge, heavily reliant on automated data preparation and AI-driven analysis across vast, standardized data lakes.
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Read at arXiv cs.AI