Automated Standardization of Legacy Biomedical Metadata Using an Ontology-Constrained LLM Agent
Summary: arXiv:2604.08552v1 Announce Type: cross
Abstract
Scientific metadata are often incomplete and noncompliant with community standards, limiting dataset findability, interoperability, and reuse. When 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 large language models (LLMs) guided by field names and ontology constraints can improve metadata standardization. However, these approaches treat constraints as static text prompts, relying on the model’s training knowledge alone. In this article, we present an innovative LLM-based metadata standardization system that queries authoritative biomedical terminology services in real time to retrieve canonically correct vocabulary terms on demand.
Methodology
To evaluate the effectiveness of our approach, we conducted an assessment on 839 legacy metadata records sourced from the Human BioMolecular Atlas Program (HuBMAP). This evaluation was performed using an expert-curated gold standard for exact-match assessment.
Key Findings
Our evaluation demonstrates that augmenting the LLM with real-time tool access consistently improves prediction accuracy compared to the LLM alone. This improvement was noted across both ontology-constrained and non-ontology-constrained fields.
Benefits of the New System
- Real-Time Access: By querying biomedical terminology services in real time, the system ensures that vocabulary terms used are up-to-date and accurate.
- Enhanced Compliance: The automated standardization process leads to greater compliance with community standards, enhancing the overall quality of datasets.
- Scalability: This approach is practical and scalable, making it suitable for large datasets that require efficient metadata standardization.
- Improved Interoperability: With better standardized metadata, datasets become more interoperable, facilitating easier data sharing and collaboration across research communities.
Conclusion
The integration of real-time querying capabilities into LLMs marks a significant advancement in the automated standardization of biomedical metadata. By addressing the shortcomings of previous methodologies, this new system not only enhances data quality but also promotes the principles of findability, accessibility, interoperability, and reuse (FAIR) in scientific research.
As the biomedical field continues to evolve, the need for standardized, machine-actionable metadata will only grow. Our findings suggest that leveraging LLMs in conjunction with real-time data retrieval from authoritative sources offers a promising pathway forward in this critical area of research.