通过KG意识的对比学习和受约束的本地LLM推理进行患者地址解析
Jinzhe Li1, Xin Pan2, Yanchao Jia1
1Information Center, Civil Aviation General Hospital, Chaoyanglu, Beijing, 100123, Beijing, China.
Scientific reports
|February 9, 2026
概括
AddrKG-LLM 改进了使用知识图 (KG) 和大语言模型 (LLM) 的杂地址解析. 这一框架提高了结构化地址数据的准确性和可控性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 地址解析对于大型信息系统至关重要,但面临着模糊性和隐私方面的挑战.
- 现有的方法与语义和结构问题,幻觉和部署限制作斗争.
研究的目的:
- 引入AddrKG-LLM,这是一个用于准确和可控制地址解析的新框架.
- 解决当前处理杂,缩短的自由文本地址的方法的局限性.
主要方法:
- 一个两阶段的框架,将知识图 (KG) 意识的检索与局限于模式的大型语言模型 (LLM) 解码相结合.
- 在多视图管理图表上进行对比学习,用于候选人检索和重新排名 (Recall@K).
- 在本地,候选限制的LLM对JSON兼容输出进行解码,确保现场一致性和KG对齐.
主要成果:
- 与基线相比,AddrKG-LLM在微观 ([公式:见文本]) 和宏观 ([公式:见文本]) 准确度方面表现出一致的收益.
- 该框架取得了有利的Recall@K,表明高黄金覆盖率.
- 评估使用了非识别的真实世界记录,评估结构一致性和系统属性,如延迟.
结论:
- AddrKG-LLM为构建杂地址提供了一个准确,可控制和可部署的解决方案.
- 将KG意识的检索与有限的本地LLM解码相结合,有效地克服了现有的挑战.
- 该方法增强了大规模信息系统的数据标准化.
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