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, necessitating
substantial collaborative efforts of 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). This method can generate
textual and logical ontology components, drawing from existing knowledge in
multiple ontologies, as well as unstructured textual sources.
We assessed DRAGON-AI across ten diverse ontologies, making use of extensive
manual evaluation of results. We demonstrate high precision for relationship
generation, close to but lower than precision from logic-based reasoning. We
also demonstrate definition generation comparable with but lower than
human-generated definitions. Notably, expert evaluators were better able to
discern subtle flaws in AI-generated definitions. We also demonstrated the
ability of DRAGON-AI to incorporate natural language instructions in the form
of GitHub issues.
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.

应用大型语言模型和增强生成检索技术，我们提出了 DRAGON-AI（动态检索增强本体生成），这种本体生成方法可以从现有知识和非结构化文本源中提取文本和逻辑结构的本体组成部分。我们通过对十个不同的本体进行了广泛的手动评估，证明了 DRAGON-AI 在关系生成和定义生成方面的高准确度。研究结果表明 DRAGON-AI 有望在手动本体构建过程中提供重要的帮助，但也强调了专家管理者和本体编辑在本体生成过程中的重要性。