RAGulating Compliance: A Multi-Agent Knowledge Graph for Regulatory QA

• URL: http://arxiv.org/abs/2508.09893v1
• Date: Wed, 13 Aug 2025 15:51:05 GMT
• Title: RAGulating Compliance: A Multi-Agent Knowledge Graph for Regulatory QA
• Authors: Bhavik Agarwal, Hemant Sunil Jomraj, Simone Kaplunov, Jack Krolick, Viktoria Rojkova,
• Abstract summary: Regulatory compliance question answering (QA) requires precise, verifiable information.<n>We present a novel multi-agent framework that integrates a Knowledge Graph (KG) of Regulatory triplets with Retrieval-Augmented Generation (RAG)<n>Our hybrid system outperforms conventional methods in complex regulatory queries, ensuring factual correctness with embedded triplets.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Regulatory compliance question answering (QA) requires precise, verifiable information, and domain-specific expertise, posing challenges for Large Language Models (LLMs). In this work, we present a novel multi-agent framework that integrates a Knowledge Graph (KG) of Regulatory triplets with Retrieval-Augmented Generation (RAG) to address these demands. First, agents build and maintain an ontology-free KG by extracting subject--predicate--object (SPO) triplets from regulatory documents and systematically cleaning, normalizing, deduplicating, and updating them. Second, these triplets are embedded and stored along with their corresponding textual sections and metadata in a single enriched vector database, allowing for both graph-based reasoning and efficient information retrieval. Third, an orchestrated agent pipeline leverages triplet-level retrieval for question answering, ensuring high semantic alignment between user queries and the factual "who-did-what-to-whom" core captured by the graph. Our hybrid system outperforms conventional methods in complex regulatory queries, ensuring factual correctness with embedded triplets, enabling traceability through a unified vector database, and enhancing understanding through subgraph visualization, providing a robust foundation for compliance-driven and broader audit-focused applications.

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