GraphLawPH: Benchmarking GNN Architectures for Task-Specific Knowledge Discovery in Philippine Statutes

Abstract

Building on GraphLawPH, the first knowledge graph of Philippine Republic Acts, this study benchmarks three graph neural network (GNN) architectures—GAT, GCN, and GraphSAGE—and compares them to a high-dimensional Legal-BERT baseline to determine their suitability for statutory legal embedding. Using a corpus of 12,124 Republic Acts linked by 3,721 citation edges, the researchers evaluate the models as high-fidelity semantic compressors that reduce dimensionality by 91.7% while maintaining extreme representational alignment. Performance was rigorously assessed on semantic similarity detection and community discovery using retrieval metrics and structural alignment scores. The results reveal a 3.1% graph-gain for GAT in semantic retrieval (MRR=0.7870) and a massive 2,680% modularity improvement for GCN (0.4227) over the text-only baseline (0.0152), meeting established benchmarks for substantial community structure. These findings resolve the Sparsity Paradox for the corpus’s 81.79% isolated nodes, proving that transferable inductive biases from the connected minority effectively preserve semantic integrity across the entire legal graph. This study provides the first empirical benchmark for Philippine statutory GNNs, delivering guidance for selecting architectures that balance local semantic precision with global structural coherence.