从百万份乌克兰判决书构建法律引用图谱，揭示司法实践中的法律领域边界与立法重要性预测。
Automatic Construction of a Legal Citation Graph from 100 Million Ukrainian Court Decisions: Large-Scale Extraction, Topological Analysis, and Ontology-Driven Clustering
- 基于正则表达式在普通硬件上5小时内自动提取5亿条引用关系，准确率100%
- 引用网络呈现幂律分布，社区划分精准识别民事、刑事等法律领域
- 引用特征可近乎完美预测未来立法重要性，适合法律智能化与政策分析
从1.007亿份乌克兰法院判决中提取出5亿条引用边，揭示司法引用结构无需监督即可编码法律领域边界，并以接近完美的准确率预测未来立法重要性。我们构建了首个大规模引用图谱，基于完整的EDRSR注册库（9950万篇全文，1.1TB），通过正则表达式在商品级硬件上约5小时完成6类引用关系的提取，200份判决验证样本精度达1.00（95%置信区间：[0.982, 1.000]）。三大发现：(1) 度分布服从幂律（α = 1.57 ± 0.008），乌克兰法院网络接近欧盟法院但低于美国最高法院，存在被数百万判决引用的枢纽条文；(2) 在共引用投影上使用Louvain算法检测社区，恢复民事、刑事、行政、商业等法律领域，模块度Q = 0.44–0.55，时间稳定性高（不同周期间NMI = 0.83–0.86），构成基于司法实践的自动法律本体；(3) 引用特征预测前1000篇重要条文的AUC达0.9984，显著优于频率基线（P@1000 = 0.655）；时间动态显示立法体制变革如相变，2022年入侵导致引用熵从11.02飙升至13.49，出现新兴战时立法节点。该引用衍生本体已用于构建大模型辅助法律分析的工作流记忆系统，对接本体控制范式。抽取流程、分析代码与聚合统计已作为开放数据发布。
原文摘要 · Abstract (English)
Half a billion citation edges extracted from 100.7 million Ukrainian court decisions reveal that judicial citation structure encodes legal domain boundaries without supervision and predicts future legislative importance with near-perfect accuracy. We construct the first large-scale citation graph from the complete EDRSR registry (99.5 million full texts, 1.1 TB), extracting 502 million citation links across six types via regex on commodity hardware in approximately 5 hours, with precision of 1.00 on a 200-decision validation sample (95% Wilson CI: [0.982, 1.000]). Three principal findings emerge. (1) The degree distribution follows a power law (alpha = 1.57 +/- 0.008), placing the Ukrainian court network near the EU Court of Justice and below the US Supreme Court, with hub articles cited by millions of decisions. (2) Louvain community detection on the co-citation projection recovers legal domain boundaries (civil, criminal, administrative, commercial) with modularity Q = 0.44-0.55 and temporal stability (NMI = 0.83-0.86 across periods), constituting an automatically constructed legal ontology grounded in judicial practice. (3) Citation features predict top-1000 articles with AUC = 0.9984, substantially outperforming a naive frequency baseline (P@1000 = 0.655); temporal dynamics detect legislative regime changes as phase transitions and the 2022 invasion as a citation entropy spike (H: 11.02 -> 13.49) with emergent wartime legislation nodes. The citation-derived ontology is operationalized as the domain layer of a workflow memory system for LLM-assisted legal analysis, connecting to the ontology-controlled paradigm. The extraction pipeline, analysis code, and aggregated statistics are released as open data.
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