通过使用大型语言模型代理进行在线聚类来扩展本体论
Guanchen Wu1, Chen Ling1, Ilana Graetz2
1Department of Computer Science, Emory University, Atlanta, GA, United States.
Frontiers in big data
|October 22, 2024
概括
本研究引入了一种新的框架,用于使用流数据和大型语言模型 (LLM) 自动扩展本体. 它增强了癌症幸存者的医学知识代表性,人类监督最小.
科学领域:
- 计算语言学 计算语言学
- 知识表示 知识表示
- 医疗信息学 医疗信息学
背景情况:
- 实体学对于结构化知识表示和共享理解至关重要.
- 现有的本体扩展方法通常需要大量的注释数据和领域专业知识.
- 医疗保健数据,特别是患者生成的内容,往往是无结构的和庞大的.
研究的目的:
- 提出一种新的框架,以零射击的方式从流数据中自动扩展本体学.
- 为了利用大型语言模型 (LLM) 在医学本体学中进行症状类型和分类.
- 增强乳腺和膀癌幸存者的医学知识代表性.
主要方法:
- 开发了一个使用在线和等级集群的零射击本体学扩展框架.
- 从事症状类型 (分类) 和症状分类 (整合) 的LLM从患者论坛数据.
- 实现了双相模型,使用多个LLM来实现准确和无的集成.
主要成果:
- 证明有效地将新的医学知识融入现有本体学中.
- 实现了实时,结构化的症状分类和整合,最小的人类监督.
- 通过定量分析和数据可视化验证了框架的有效性.
结论:
- 拟议的框架成功地将医疗本体学从非结构化流媒体数据扩展.
- 与集群结合的LLM为医疗保健中的基于知识的系统提供了一种高效的零射击方法.
- 这种方法促进了动态和全面的医学知识图的开发.
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