用知识图谱还原法院判決中从事实到法律的推理路径。
Capturing Legal Reasoning Paths from Facts to Law in Court Judgments using Knowledge Graphs
- 通过提示工程提取判決中的法律推理要素，构建结构化知识图谱。
- 在648份日本行政法院判決上验证，法律条文召回准确率优于大模型基线。
- 适合法律AI研究者、司法智能化从业者使用，可解释性强。
法院判決揭示了法律规则如何被解释并应用于具体事实，是理解系统性法律推理的基础。然而，现有自动化方法（包括大语言模型）常无法识别相关法律背景，难以准确追踪事实与法律规范之间的关联，且可能扭曲司法推理的层级结构，限制了对法院实际适用法律过程的理解。本文针对这些挑战，基于648份日本行政法院判決构建法律知识图谱。方法利用提示式大语言模型提取法律推理组件，标准化法律条文引用，并通过法律推论本体将事实、规范与法律适用关联起来。所生成的图谱完整呈现真实判決中的法律推理结构，使隐含推理显式化且机器可读。我们使用专家标注数据评估系统，结果表明其在从事实中检索相关法律条文方面，显著优于大语言模型基线与检索增强方法。
原文摘要 · Abstract (English)
Court judgments reveal how legal rules have been interpreted and applied to facts, providing a foundation for understanding structured legal reasoning. However, existing automated approaches for capturing legal reasoning, including large language models, often fail to identify the relevant legal context, do not accurately trace how facts relate to legal norms, and may misrepresent the layered structure of judicial reasoning. These limitations hinder the ability to capture how courts apply the law to facts in practice. In this paper, we address these challenges by constructing a legal knowledge graph from 648 Japanese administrative court decisions. Our method extracts components of legal reasoning using prompt-based large language models, normalizes references to legal provisions, and links facts, norms, and legal applications through an ontology of legal inference. The resulting graph captures the full structure of legal reasoning as it appears in real court decisions, making implicit reasoning explicit and machine-readable. We evaluate our system using expert annotated data, and find that it achieves more accurate retrieval of relevant legal provisions from facts than large language model baselines and retrieval-augmented methods.
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