Multiagent Collaborative Entity Linking for Historical Characters in Ancient Chinese Texts
Yang Fan1, He Jiacheng1, Liu Chang1, Zhang Qi2, Liu Liu1
1.School of Information Management, Nanjing Agricultural University, Nanjing 210095 2.School of Economics and Management, Shanxi University, Taiyuan 030031
摘要史籍人物实体链接面临古代汉语人物指称形式多样化、文本缺乏标点符号、历史人物知识库构建滞后等独特挑战，传统通用实体链接方法在古籍文献处理中表现不佳，亟须开发针对古籍文本特点的专门化解决方案。因此，本研究提出了多智能体协同的史籍人物实体链接方法。首先，以《史记》作为实验对象，通过整合中国历代人物传记资料库（China Biographical Database，CBDB）和人工标注《史记》人物关系语料构建史籍人物知识库。其次，基于LangGraph多智能体协作框架，设计包含实体识别智能体、候选实体集构建智能体和候选实体重排序智能体的协同工作机制，创新性地采用融合BM25（best match 25）等文本相似度算法与大语言模型多维度语义评分的双重评分机制进行实体映射排序，通过智能体间的信息交互和协商机制，实现从史籍人物实体指称到知识库标准实体的精准链接。实验结果表明，Qwen3-32B取得最优性能（recall@5：0.908；accuracy：0.947），相较于基线模型BERT（bidirectional encoder representations from transformers）分别提升了9.0和17.7个百分点。同时，消融实验证明了各模块均产生正向贡献，其中重排序机制贡献最为显著，证实了人物背景知识注入下大语言模型语义评分的有效性。本研究创新性地提出了基于多智能体协同的史籍人物实体链接方法，为古籍实体链接研究提供了新思路。
杨帆, 何嘉诚, 刘畅, 张琪, 刘浏. 基于多智能体协同的史籍人物实体链接研究[J]. 情报学报, 2026, 45(7): 1068-1082.
Yang Fan, He Jiacheng, Liu Chang, Zhang Qi, Liu Liu. Multiagent Collaborative Entity Linking for Historical Characters in Ancient Chinese Texts. 情报学报, 2026, 45(7): 1068-1082.
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