Reporting Requests Modelling in European Legislation with a Hybrid AI Approach

Reporting requests in EU legislation require institutions to submit reports for legislative monitoring and policy implementation. As these meta-norms grow in complexity, compliance becomes increasingly challenging and burdensome. This paper leverages a Hybrid AI approach to detect, extract, and track reporting requests within the EU legislation using the RRVM ontology and European legal standards (AKN4EU, CELLAR, ELI). We investigate four interconnected and integrated areas: (1) detecting reporting requests and their concepts in legislative texts, (2) navigating normative references, (3) converting extracted legal knowledge into RDF for a Knowledge Graph, and (4) tracking modifications of reporting requests over time. Our approach compares machine learning (ML) and large language models (LLMs) for detection, demonstrating the advantages and limitations of both. By structuring Reporting Requests into a dynamic knowledge graph, our method improves the handling of Reporting Requests, reduces their administrative burden, and supports better legislative drafting and policy monitoring.