# Dynamic Retrieval Augmented Generation of Ontologies using Artificial Intelligence (DRAGON-AI) arXiv: https://arxiv.org/abs/2312.10904 ยท HTML: https://mlsanity.com/paper/2312.10904/ ## Key facts - Cited by 14 papers in the map (data through 14 Oct 2026). - Placed in Knowledge-graph and structured-knowledge RAG, one of 15 directions in Retrieval-augmented generation. - Builds on 16 of its 64 references that are in the map. - First posted on arXiv 18 Dec 2023. ## Placed in - Retrieval-augmented generation / [Knowledge-graph and structured-knowledge RAG](https://mlsanity.com/rag/knowledge-graph-and-structured-knowledge-rag/) ## Builds on (most cited first, 16 of 16) - [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023) - [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022) - [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020) - [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021) - [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019) - [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013) - [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023) - [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023) - [Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs](https://mlsanity.com/paper/1603.09320/) (2016) - [GitHub Copilot AI pair programmer: Asset or Liability?](https://mlsanity.com/paper/2206.15331/) (2022) - [Eight Things to Know about Large Language Models](https://mlsanity.com/paper/2304.00612/) (2023) - [OWL2Vec*: Embedding of OWL Ontologies](https://mlsanity.com/paper/2009.14654/) (2020) - [Structured prompt interrogation and recursive extraction of semantics (SPIRES): A method for populating knowledge bases using zero-shot learning](https://mlsanity.com/paper/2304.02711/) (2023) - [Data Contamination Through the Lens of Time](https://mlsanity.com/paper/2310.10628/) (2023) - [Ontology Development Kit: a toolkit for building, maintaining, and standardising biomedical ontologies](https://mlsanity.com/paper/2207.02056/) (2022) - [Generating Ontologies from Templates: A Rule-Based Approach for Capturing Regularity](https://mlsanity.com/paper/1809.10436/) (2018) ## Built on by (most cited first, 14 of 14) - [From human experts to machines: An LLM supported approach to ontology and knowledge graph construction](https://mlsanity.com/paper/2403.08345/) (2024) - [HM-RAG: Hierarchical Multi-Agent Multimodal Retrieval Augmented Generation](https://mlsanity.com/paper/2504.12330/) (2025) - [Observations on Building RAG Systems for Technical Documents](https://mlsanity.com/paper/2404.00657/) (2024) - [The Artificial Intelligence Ontology: LLM-assisted construction of AI concept hierarchies](https://mlsanity.com/paper/2404.03044/) (2024) - [CurateGPT: A flexible language-model assisted biocuration tool](https://mlsanity.com/paper/2411.00046/) (2024) - [DSTI at LLMs4OL 2024 Task A: Intrinsic versus extrinsic knowledge for type classification](https://mlsanity.com/paper/2408.14236/) (2024) - [Chemical classification program synthesis using generative artificial intelligence](https://mlsanity.com/paper/2505.18470/) (2025) - [MedVQA-TREE: A Multimodal Reasoning and Retrieval Framework for Sarcopenia Prediction](https://mlsanity.com/paper/2508.19319/) (2025) - [CMOMgen: Complex Multi-Ontology Alignment via Pattern-Guided In-Context Learning](https://mlsanity.com/paper/2510.21656/) (2025) - [Beyond Fine-Tuning: Robust Food Entity Linking under Ontology Drift with FoodOntoRAG](https://mlsanity.com/paper/2603.09758/) (2026) - [DN-Hypo-Pipeline: An AI-Driven Workflow for Generating Hypotheses using Large Language Models and Scientific Explanations](https://mlsanity.com/paper/2606.08532/) (2026) - [Agents for Everyone: A Workshop Framework for Building Agentic AI Capabilities in a Distributed Curation Community](https://mlsanity.com/paper/2608.27675/) (2026) - [CQ4OE: A benchmark for assessing LLM-assisted ontology generation from competency questions](https://mlsanity.com/paper/2609.26029/) (2026) - [Ontology-Grounded, Reasoner-Verified Benchmarks for Evaluating LLM Reasoning in Scientific AI](https://mlsanity.com/paper/2610.00682/) (2026) ## Abstract (arXiv) Background: Ontologies 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. Results: We 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. Conclusions: These 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.