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

Hui-Hung Yu, Wei-Tsun Lin, Chih-Wei Kuan (), Chao-Chi Yang and Kuan-Min Liao
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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

Future Internet, 2025, vol. 17, issue 9, 1-22

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.

Keywords: graph retrieval-augmented generation (GraphRAG); knowledge graph (KG); large language model (LLM); semantics; SensorThings API (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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