用知识图谱和大模型提升刑法条文推荐准确率
Leverage Knowledge Graph and Large Language Model for Law Article Recommendation: A Case Study of Chinese Criminal Law
- 构建案件增强型法条知识图谱，融合法条与判例关系
- 推荐准确率从0.549提升至0.694，显著优于基线
- 适合法律AI研究者与智慧司法系统开发者
司法效率关乎社会稳定。全球多地基层法院面临案件积压，裁判依赖法官个人认知，智能辅助工具不足。为此，我们提出一种结合知识图谱（KG）与大语言模型（LLM）的高效法条推荐方法。首先构建案件增强型法条知识图谱（CLAKG），存储现行法条、历史判例及其关联，并采用基于LLM的自动化构建方法。在此基础上，设计闭环法条推荐框架，融合图嵌入检索与KG引导的LLM推理。在“中国裁判文书网”判决书数据集上的实验表明，该方法将推荐准确率从0.549提升至0.694，显著优于多个强基线模型。为支持可复现性与后续研究，所有源代码及处理后的数据集均已公开于GitHub。
原文摘要 · Abstract (English)
Judicial efficiency is critical to social stability. However, in many countries worldwide, grassroots courts face substantial case backlogs, and judicial decisions remain heavily dependent on judges' cognitive efforts, with insufficient intelligent tools to enhance efficiency. To address this issue, we propose a highly efficient law article recommendation approach combining a Knowledge Graph (KG) and a Large Language Model (LLM). First, we construct a Case-Enhanced Law Article Knowledge Graph (CLAKG) to store current law articles, historical case information, and their interconnections, alongside an LLM-based automated construction method. Building on this, we propose a closed-loop law article recommendation framework integrating graph embedding-based retrieval and KG-grounded LLM reasoning. Experiments on judgment documents from China Judgments Online demonstrate that our method boosts law article recommendation accuracy from 0.549 to 0.694, outperforming strong baselines significantly. To support reproducibility and future research, all source code and processed datasets are publicly available on GitHub (see Data Availability Statement).
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