Abstract: As the demand for efficient information retrieval in specialized domains continues to rise, vertical domain question-answering systems play an increasingly important role in addressing domain-specific knowledge needs. This paper proposes a retrieval-augmented generation method that integrates path search in knowledge graphs to enhance intelligent question-answering systems for professional information retrieval. The proposed approach leverages fine-tuned large language models to identify entities and extract relations from user queries, combining pruned marker method with a shortest path generation tree algorithm to efficiently retrieve relevant information. The retrieval results are then integrated with user queries using prompt engineering to generate precise and contextually relevant answers. To validate the practicality of the proposed method, this paper develops a knowledge graph encompassing policies, regulations, and social services within the household registration vertical domain. The experimental results within this vertical domain reveal that the proposed method significantly outperforms existing methods in terms of evaluation metrics such as BLEU, ROUGE, and METEOR, achieving improvements exceeding 3%. Furthermore, ablation experiments validate the importance of combining path search algorithms with fine-tuning techniques in enhancing the question-answering performance.

Jian Zhu, Huajun Zhang, Jianpeng Da, Hanbing Huang, Chongxin Luo and Xu Peng, “Knowledge Graph Path-Enhanced RAG for Intelligent Residency Q&A” International Journal of Advanced Computer Science and Applications(IJACSA), 16(2), 2025. http://dx.doi.org/10.14569/IJACSA.2025.01602125

@article{Zhu2025,
title = {Knowledge Graph Path-Enhanced RAG for Intelligent Residency Q&A},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.01602125},
url = {http://dx.doi.org/10.14569/IJACSA.2025.01602125},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {2},
author = {Jian Zhu and Huajun Zhang and Jianpeng Da and Hanbing Huang and Chongxin Luo and Xu Peng}
}