GraphCompliance: Structure-Aware Regulatory Compliance through Policy-Context Graph Alignment

Abstract

Regulatory compliance is an important requirement in many industrial and administrative settings. Yet compliance assessment remains difficult because regulatory requirements are distributed across structured documents and may depend on hierarchy, definitions, conditions, and cross-references, while the evidence relevant to a particular requirement may be embedded among multiple interacting actors, events, objects, and conditions in the assessed context. We present GraphCompliance, a structure-aware framework that represents regulatory documents as a schema-guided Policy Graph and contextual information as a Context Graph of entity-relation triples. GraphCompliance aligns the two graphs through a Compliance Gate, which identifies relevant regulatory requirements and evaluates them against the corresponding contextual evidence. This design makes regulatory structure explicit while organizing contextual facts into a comparable form, helping preserve structural dependencies and reduce terminology mismatch across different regulatory settings. Evaluation on EU2UK, GDPR-13, and CONTRACT yields Matthews correlation coefficients (MCC) of 0.802, 0.682, and 0.793, respectively. These values are numerically higher than those of the strongest compared methods on all three datasets. Ablation results further show that the key components of GraphCompliance contribute to its overall performance. These results support the utility of structured policy-context alignment for compliance-related prediction across heterogeneous regulatory settings. The source code of GraphCompliance is available at https://github.com/CHUNG-JiSeong/GraphCompliance.git.