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GraphRAG-Enhanced Dialogue Engine for Domain-Specific Question Answering: A Case Study on the Civil IoT Taiwan Platform

Author

Listed:
  • Hui-Hung Yu

    (National Center for High-Performance Computing, National Institute of Applied Research, Hsinchu 300092, Taiwan)

  • Wei-Tsun Lin

    (Geographic Information Systems Research Center, Feng Chia University, Taichung 407102, Taiwan)

  • Chih-Wei Kuan

    (Geographic Information Systems Research Center, Feng Chia University, Taichung 407102, Taiwan)

  • Chao-Chi Yang

    (Geographic Information Systems Research Center, Feng Chia University, Taichung 407102, Taiwan)

  • Kuan-Min Liao

    (Geographic Information Systems Research Center, Feng Chia University, Taichung 407102, Taiwan)

Abstract

The proliferation of sensor technology has led to an explosion in data volume, making the retrieval of specific information from large repositories increasingly challenging. While Retrieval-Augmented Generation (RAG) can enhance Large Language Models (LLMs), they often lack precision in specialized domains. Taking the Civil IoT Taiwan Data Service Platform as a case study, this study addresses this gap by developing a dialogue engine enhanced with a GraphRAG framework, aiming to provide accurate, context-aware responses to user queries. Our method involves constructing a domain-specific knowledge graph by extracting entities (e.g., ‘Dataset’, ‘Agency’) and their relationships from the platform’s documentation. For query processing, the system interprets natural language inputs, identifies corresponding paths within the knowledge graph, and employs a recursive self-reflection mechanism to ensure the final answer aligns with the user’s intent. The final answer transformed into natural language by utilizing the TAIDE (Trustworthy AI Dialogue Engine) model. The implemented framework successfully translates complex, multi-constraint questions into executable graph queries, moving beyond keyword matching to navigate semantic pathways. This results in highly accurate and verifiable answers grounded in the source data. In conclusion, this research validates that applying a GraphRAG-enhanced engine is a robust solution for building intelligent dialogue systems for specialized data platforms, significantly improving the precision and usability of information retrieval and offering a replicable model for other knowledge-intensive domains.

Suggested Citation

  • Hui-Hung Yu & Wei-Tsun Lin & Chih-Wei Kuan & Chao-Chi Yang & Kuan-Min Liao, 2025. "GraphRAG-Enhanced Dialogue Engine for Domain-Specific Question Answering: A Case Study on the Civil IoT Taiwan Platform," Future Internet, MDPI, vol. 17(9), pages 1-22, September.
    • Handle: RePEc:gam:jftint:v:17:y:2025:i:9:p:414-:d:1746455
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    References listed on IDEAS

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    1. Sebastian Farquhar & Jannik Kossen & Lorenz Kuhn & Yarin Gal, 2024. "Detecting hallucinations in large language models using semantic entropy," Nature, Nature, vol. 630(8017), pages 625-630, June.
    2. Yu-Fang Lin & Tzu-Yin Chang & Wen-Ray Su & Rong-Kang Shang, 2021. "IoT for Environmental Management and Security Governance: An Integrated Project in Taiwan," Sustainability, MDPI, vol. 14(1), pages 1-12, December.
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