KG2QA: Knowledge Graph-enhanced Retrieval-Augmented Generation for Communication Standards Question Answering

• URL: http://arxiv.org/abs/2506.07037v1
• Date: Sun, 08 Jun 2025 08:07:22 GMT
• Title: KG2QA: Knowledge Graph-enhanced Retrieval-Augmented Generation for Communication Standards Question Answering
• Authors: Zhongze Luo, Weixuan Wan, Qizhi Zheng, Yanhong Bai, Jingyun Sun, Jian Wang, Dan Wang,
• Abstract summary: This paper combines the fine-tuning of large language models with the construction of knowledge graphs.<n>Qwen2.5-7B-Instruct demonstrates outstanding professional capabilities in the field of communication standards.
• Score: 3.981162741154511
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: There are many types of standards in the field of communication. The traditional consulting model has a long cycle and relies on the knowledge and experience of experts, making it difficult to meet the rapidly developing technological demands. This paper combines the fine-tuning of large language models with the construction of knowledge graphs to implement an intelligent consultation and question-answering system for communication standards. The experimental results show that after LoRA tuning on the constructed dataset of 6,587 questions and answers in the field of communication standards, Qwen2.5-7B-Instruct demonstrates outstanding professional capabilities in the field of communication standards on the test set. BLEU-4 rose from 18.8564 to 66.8993, and evaluation indicators such as ROUGE also increased significantly, outperforming the fine-tuning effect of the comparison model Llama-3-8B-Instruct. Based on the ontology framework containing 6 entity attributes and 10 relation attributes, a knowledge graph of the communication standard domain containing 13,906 entities and 13,524 relations was constructed, showing a relatively good query accuracy rate. The intelligent consultation and question-answering system enables the fine-tuned model on the server side to access the locally constructed knowledge graph and conduct graphical retrieval of key information first, which is conducive to improving the question-answering effect. The evaluation using DeepSeek as the Judge on the test set shows that our RAG framework enables the fine-tuned model to improve the scores at all five angles, with an average score increase of 2.26%. And combined with web services and API interfaces, it has achieved very good results in terms of interaction experience and back-end access, and has very good practical application value.

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