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Hybrid Multi-Agent GraphRAG for E-Government: Towards a Trustworthy AI Assistant

A modular framework integrating standard RAG, embedding-based retrieval, and LLM-generated structured graphs for e-government question answering.

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Hybrid Multi-Agent GraphRAG for E-Government: Towards a Trustworthy AI Assistant

By George Papageorgiou, Vangelis Sarlis, Manolis Μaragoudakis, Christos TjortjisApplied Sciences
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This paper introduces a hybrid multi-agent graph retrieval-augmented generation (GraphRAG) framework designed to enhance policy-focused question answering in e-government settings.

The framework integrates standard RAG, embedding-based retrieval, real-time web search, and LLM-generated structured Graphs to optimize knowledge discovery from public e-government data.

This approach aims to provide an overview of a hybrid architecture for operational deployment in e-government settings.

Abstract

This paper introduces a hybrid multi-agent graph retrieval-augmented generation (GraphRAG) framework designed to enhance policy-focused question answering in e-government settings. The framework integrates standard RAG, embedding-based retrieval, real-time web search, and LLM-generated structured Graphs to optimize knowledge discovery from public e-government data. This approach aims to provide an overview of a hybrid architecture for operational deployment in e-government settings.

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graphrage-governmentexplainable aiquestion answeringhybrid frameworkKnowledge GraphsStructured ContentAI AgentsLarge Language Models
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