Graph-Enhanced Retrieval-Augmented Question Answering for E-Commerce Customer Support
This work addresses the need for quick and accurate responses in e-commerce customer support, though it appears incremental by building on existing RAG and knowledge graph methods.
The paper tackled the problem of improving answer relevance and factual grounding in e-commerce customer support by developing a retrieval-augmented generation framework that integrates knowledge graphs with text documents. It achieved a 23% improvement in factual accuracy and 89% user satisfaction in e-commerce QA scenarios.
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.