利用知识图谱和大语言模型进行法律文书推荐：中国刑法案例研究
Leverage Knowledge Graph and Large Language Model for Law Article Recommendation: A Case Study of Chinese Criminal Law
AI总结：
本文提出结合知识图谱和大语言模型的法律文书推荐方法，通过构建案例增强的知识图谱和闭环框架，提升了推荐准确性，优于现有基线方法。
摘要：
司法效率对于社会稳定至关重要。然而，在全球许多国家，基层法院面临严重的案件积压，司法决定仍然严重依赖法官的认知努力，缺乏足够的智能工具来提高效率。为了解决这个问题，我们提出了一种高效的法律文书推荐方法，结合了知识图谱（KG）和大语言模型（LLM）。首先，我们构建了一个案例增强的法律文书知识图谱（CLAKG）来存储当前法律文书、历史案件信息及其相互关联，以及基于LLM的自动构建方法。在此基础上，我们提出了一种闭环法律文书推荐框架，整合了基于图嵌入的检索和基于知识图谱的LLM推理。在“中国裁判文书网”的判决文书上的实验表明，我们的方法将法律文书推荐准确性从0.549提升到0.694，显著优于强基线。为了支持可重复性和未来研究，所有源代码和处理过的数据集都已公开在GitHub上（见数据可用性声明）.
英文摘要：
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).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
