Graphing the European Green Deal: A Graph Retrieval-Augmented Generation Pipeline for Policy Documents Analysis

The European Green Deal (EGD) is the European Union’s comprehensive growth strategy for achieving climate neutrality by 2050. It comprises 17 interrelated policy documents, spanning sectors from energy and transport to biodiversity and sustainable finance. Despite their collective importance, these documents are characterized by significant heterogeneity in structure, terminology, and scope, making it challenging for non-technical stakeholders to navigate, cross-reference, extract, and validate information across their corpus as a whole. Considering the limitations of Natural Language Processing (NLP) approaches targeting the accessibility of policy documents and the lack of prior work explicitly focusing on the EGD and sustainability, we introduce a graph retrieval-augmented generation (GraphRAG) pipeline for natural language question answering (QA) over the EGD corpus. Our contributions include the conceptualization of a generalizable entity type set for policy documents for the EGD and its representation in the form of a knowledge graph, the development of two novel graph-based retrieval strategies that exploit the pre-computed structural properties of the knowledge graph, and the release of a specialized evaluation dataset, built on persona profiles matching real-world user profiles. The implementation and evaluation of the proposed approach are detailed, highlighting its effectiveness for the analysis of policy documents for the EGD against other GraphRAG baselines.