人与大语言模型合作系统的本体学更新:饮食生活方式领域的案例研究
Jinsun Jung1,2, Ricky Taira3, Hyeoneui Kim1,2
1College of Nursing, Seoul National University, Seoul 03080, Republic of Korea.
Journal of the American Medical Informatics Association : JAMIA
|February 20, 2026
概括
人类-大型语言模型 (LLM) 协作通过利用LLM进行快速本地更新和人类专家来维护结构完整性,提高效率和可扩展性来增强本体工程.
科学领域:
- 生物医学信息学 生物医学信息学
- 本体学 工程学 工程学
背景情况:
- 系统的本体学更新对于维护复杂的生物医学知识库至关重要.
- 传统的方法可能是耗时和劳动密集的.
研究的目的:
- 开发和评估一个协作框架,将人类的专业知识与大型语言模型 (LLM) 整合起来,以便有效地更新本体学.
- 为了证明这种方法,使用饮食生活方式本体学 (DILON).
主要方法:
- 四个最先进的LLM语义上注释了来自英语和韩国来源的100个饮食问卷项目.
- 通过LLM生成的概念在模型中进行了调和,并由人类专家进行了改进.
- 专家在DILON内部使用Protégé进行了概念审查和策划,以确保准确性和一致性.
主要成果:
- LLM,特别是Claude Sonnet 4,在概念采集,冗余检测和层次改进等局部任务中表现出色.
- 全球本体学优化和结构完整性需要系统的人类专家审查和干预.
结论:
- 人类-LLM合作提供了互补的优势,LLM加速重复性任务,人类确保整体连贯性.
- 这种综合方法提高了生物医学本体学工程和维护的效率,可扩展性和可持续性.
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