CO2 (Co-Compliance Officer): An LLM-Based Ontology-Driven Methodology for Generating Knowledge Graphs and AI Compliance Checking
Venkata Sai Prathyush Turaga, Tímea Páhi, Simon Tjoa, Sima Siami‐Namini, Akbar Siami Namin · IEEE Access · 2025
With the advent of Artificial Intelligence (AI) technology and immense transitions to industry, it is important to regulate and establish standards for this fascinating technology. As a result, AI-powered applications need to be checked for compliance with AI regulations. Although compliance checks have been extensively discussed by the research community and industry, AI compliance checks and verification still need to be explored, and pitfalls need to be identified. This paper aims at automating the extraction of AI regulations using the knowledge graph and the ontology defined for legal documents. The paper first reports the initial effort to create knowledge graphs for legal documents (e.g., the EU AI Act) using different language models trained in general textual documents where general approaches in extracting Named-Entity Recognition (NER) are employed. Triplets in the form ofare extracted followed by enhancement achieved through augmenting NER tags by information captured using dependency parsers. The paper then presents a methodology called Co-Compliance Officer (CO2) where Large Language Models (LLMs) are employed to build knowledge graphs and NER which are solely for parsing and extracting legal documents. The LLM ontology is then used to create effective tags that are specifically designed to extract legal documents and the relationships between legal entities. The created knowledge graphs are then used to perform several verification analysis on legal documents, including inconsistency checks and violations. The results indicate that Large Language Models (LLMs) can be helpful in compliance checking of legal documents, in particular, the documents created for AI compliance checking.