# Hybrid Multi-Agent GraphRAG for E-Government: Towards a Trustworthy AI Assistant
## 4 Results, E-Governance Use Case and Applications
### Graph RAG Pipeline Results  
The graph pipeline response, based on a knowledge graph architecture that extracts and indexes structured relationships among entities from source documents, demonstrates a strong capacity for generating a policy-oriented and thematically organized summary, as presented in Figure 5. Unlike the embedding pipeline, the graph search mechanism relies less on narrative cohesion and more on the retrieval of conceptually linked data points. The output, consistent with the prompt instructions as illustrated in Table A3 in Appendix A, presents a structured response that isolates objectives, frameworks, and implementations as distinct yet interconnected components, aligning with key EU strategic goals.  
Structurally, the response excels in highlighting entity-based groupings, such as policy goals, regulatory frameworks, and specific national support schemes, without over-relying on the original textual flow. The use of precise, modular headings (e.g., \"State Aid Framework and Consultation\", \"Approved Support Schemes\", \"Related Initiatives\") reflects the pipeline's strength in extracting high-confidence triples and thematically clustering them. This approach supports improved clarity when tracing how individual measures (e.g., Spanish hydrogen auctions or Czech clean technology grants), for our example use case, contribute to overarching deal objectives. It also surfaces relevant metadata, such as timelines, funding volumes, and consultation windows with greater consistency.  
The combination of graph-based facts, represented as structured triples (entity, relation, and entity), with additions of raw textual content retrieved alongside them, can significantly enhance the overall quality of the Q&A response. This hybrid approach ensures that factual precision and contextual richness are maintained simultaneously. While the knowledge graph structures help isolate and surface relevant policy components, such as the linkage between a funding mechanism and its target technology, the accompanying raw content, linked to the searched entity retrieved, provides the necessary semantic context and interpretive framing. This dual-layered output addresses a known limitation of purely symbolic systems, which often lack descriptive depth, and overcomes the abstraction limits of embedding-only models, which may miss discrete factual associations.  
In our example use case, this approach allows the system not only to identify that Spain's hydrogen auction contributes to CISAF objectives but also to explain how this contribution unfolds through direct funding, regulatory compliance, and strategic alignment with EU directives. This enriched pairing of structured and unstructured elements creates a more informative, interpretable, and versatile response that serves both high-level summaries and detailed policy tracing.  
Finally, the graph pipeline is particularly well-suited for users seeking clarity and fact-based information grounded on relational background generally and on our use case example, on how policy instruments fit within the Clean Industrial Deal's broader architecture. It enhances interpretability, supports analytical queries, and aligns tasks requiring transparency in how e-government-related information can be interpreted. As such, this pipeline provides significant value in technical briefings, regulatory monitoring, and multi-policy comparisons within e-goverance contexts.