ArticleJournal of biomedical semantics2024
Dynamic Retrieval Augmented Generation of Ontologies using Artificial Intelligence (DRAGON-AI).
Article in Journal of biomedical semantics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
24 citing papers in PubMed.
- AI semantics for biomedical data integration.bioRxiv : the preprint server for biology · 2026Article
- Utilizing BERTopic modeling for concept discovery in the domain of gerotranscendence and solitude.Journal of biomedical semantics · 2026Article
- Advancing FAIR data towards comparable, organized, predictive AI-ready data for community validation.Communications biology · 2026Review
- The Cell Ontology in the age of single-cell omics.Scientific data · 2026Article
- Mondo: integrating disease terminology across communities.Genetics · 2026Article
- Improving Retrieval Augmented Generation for Health Care by Fine-Tuning Clinical Embedding Models: Development and Evaluation Study.Journal of medical Internet research · 2026Article
- A unified knowledge graph linking foodomics to chemical-disease networks and flavor profiles.NPJ science of food · 2026Article
- The Gene Ontology knowledgebase in 2026.Nucleic acids research · 2026Article
- Evaluating RAG and Non-RAG Pipelines for Concept Discovery in Environmental Health Ontologies.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- AI in biocuration: challenges, opportunities, and a roadmap for sustainable integration.Bioinformatics advances · 2026Article
- Using GPT-4 to annotate the severity of all phenotypic abnormalities within the human phenotype ontology.Frontiers in digital health · 2026Article
- Empowering biological knowledgebases: advances in human-in-the-loop AI-driven literature curation.Bioinformatics advances · 2026Review
- Large Language Models in Bio-Ontology Research: A Review.Bioengineering (Basel, Switzerland) · 2025Review
- Retrieval-augmented generation for answering Breast Imaging Reporting and Data System (BI-RADS)-related questions with large language models.Diagnostic and interventional radiology (Ankara, Turkey) · 2025Article
- Automating candidate gene prioritization with large language models: from naive scoring to literature-grounded validation.Bioinformatics (Oxford, England) · 2025Article
- Chemical classification program synthesis using generative artificial intelligence.Journal of cheminformatics · 2025Article
- Utilizing BERTopic Modeling for Concept Discovery in the Domain of Gerotranscendence and Solitude.Research square · 2025Article
- A conceptual framework for human-AI collaborative genome annotation.Briefings in bioinformatics · 2025Review
- Assessing the performance of generative artificial intelligence in retrieving information against manually curated genetic and genomic data.Database : the journal of biological databases and curation · 2025Article
- A change language for ontologies and knowledge graphs.Database : the journal of biological databases and curation · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
30 authors.
Funding
Abstract
backgroundOntologies are fundamental components of informatics infrastructure in domains such as biomedical, environmental, and food sciences, representing consensus knowledge in an accurate and computable form. However, their construction and maintenance demand substantial resources and necessitate substantial collaboration between domain experts, curators, and ontology experts. We present Dynamic Retrieval Augmented Generation of Ontologies using AI (DRAGON-AI), an ontology generation method employing Large Language Models (LLMs) and Retrieval Augmented Generation (RAG). DRAGON-AI can generate textual and logical ontology components, drawing from existing knowledge in multiple ontologies and unstructured text sources.
resultsWe assessed performance of DRAGON-AI on de novo term construction across ten diverse ontologies, making use of extensive manual evaluation of results. Our method has high precision for relationship generation, but has slightly lower precision than from logic-based reasoning. Our method is also able to generate definitions deemed acceptable by expert evaluators, but these scored worse than human-authored definitions. Notably, evaluators with the highest level of confidence in a domain were better able to discern flaws in AI-generated definitions. We also demonstrated the ability of DRAGON-AI to incorporate natural language instructions in the form of GitHub issues.
conclusionsThese findings suggest DRAGON-AI's potential to substantially aid the manual ontology construction process. However, our results also underscore the importance of having expert curators and ontology editors drive the ontology generation process.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.