用知识图谱增强问答，让电商客服回复更准确可信。
Graph-Enhanced Retrieval-Augmented Question Answering for E-Commerce Customer Support
- 结合领域知识图谱与文本检索结果生成答案
- 事实准确性提升23%，用户满意度达89%
- 适合需要高准确性的电商客服系统
电商客服需快速准确地基于产品数据和历史支持案例提供回答。本文提出一种新型检索增强生成（RAG）框架，利用知识图谱（KG）提升答案相关性与事实依据。研究了基于大语言模型（LLM）的最新知识增强RAG与聊天机器人进展，包括Microsoft的GraphRAG和混合检索架构。在此基础上，提出一种新的答案合成算法，将领域知识图谱中的结构化子图与支持档案中检索到的文本文档相结合，生成更连贯且有据可依的回答。详细阐述了系统架构与知识流动机制，进行了全面实验评估，并在实时支持场景中验证了设计合理性。实现23%的事实准确性提升和89%的用户满意度，显著优于传统方法。
原文摘要 · Abstract (English)
E-Commerce customer support requires quick and accurate answers grounded in product data and past support cases. This paper develops a novel retrieval-augmented generation (RAG) framework that uses knowledge graphs (KGs) to improve the relevance of the answer and the factual grounding. We examine recent advances in knowledge-augmented RAG and chatbots based on large language models (LLM) in customer support, including Microsoft's GraphRAG and hybrid retrieval architectures. We then propose a new answer synthesis algorithm that combines structured subgraphs from a domain-specific KG with text documents retrieved from support archives, producing more coherent and grounded responses. We detail the architecture and knowledge flow of our system, provide comprehensive experimental evaluation, and justify its design in real-time support settings. Our implementation demonstrates 23\% improvement in factual accuracy and 89\% user satisfaction in e-Commerce QA scenarios.
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