用图神经网络联合预测法律判决与规范引用，提升准确率与效率。
The Missing Link: Joint Legal Citation Prediction using Heterogeneous Graph Enrichment
- 融合语义与拓扑信息的异构图模型，增强引用预测能力。
- 在数据稀疏场景下平均精度提升3.1点，引用预测效率几乎翻倍。
- 适合法律AI、司法研究者及需要自动化引用推荐的系统使用。
法律体系高度依赖法律规范与过往判例之间的交叉引用。从业者、新手及法律AI系统需获取相关引用信息以支持判断与评估。本文提出一种基于图神经网络（GNN）的联合链接预测模型，通过融合语义与拓扑信息，高效识别判例间及判例与规范间的引用关系。引入改进的关联图卷积操作，在扩展增强的引用图上实现语义元信息的拓扑整合，使平均精度提升3.1点，数据稀疏场景下提升8.5点，并在长期及完全归纳预测中表现稳健。联合学习判例与规范引用产生显著协同效应，判例引用预测最高提升4.7点，同时效率接近翻倍。
原文摘要 · Abstract (English)
Legal systems heavily rely on cross-citations of legal norms as well as previous court decisions. Practitioners, novices and legal AI systems need access to these relevant data to inform appraisals and judgments. We propose a Graph-Neural-Network (GNN) link prediction model that can identify Case-Law and Case-Case citations with high proficiency through fusion of semantic and topological information. We introduce adapted relational graph convolutions operating on an extended and enriched version of the original citation graph that allow the topological integration of semantic meta-information. This further improves prediction by 3.1 points of average precision and by 8.5 points in data sparsity as well as showing robust performance over time and in challenging fully inductive prediction. Jointly learning and predicting case and norm citations achieves a large synergistic effect that improves case citation prediction by up to 4.7 points, at almost doubled efficiency.
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