The Missing Link: Joint Legal Citation Prediction using Heterogeneous Graph Enrichment

TL;DR

This paper presents a novel GNN-based model that effectively predicts legal citations by integrating semantic and topological data, significantly enhancing accuracy and robustness in legal citation prediction tasks.

Contribution

It introduces an enriched relational graph convolutional approach for joint prediction of case and norm citations, improving accuracy and efficiency over existing methods.

Findings

Improved average precision by 3.1 points

Enhanced performance in data sparsity by 8.5 points

Achieved robust results in inductive prediction scenarios

Abstract

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.