对比多种检索-重排管道，提升电商知识图谱的生成准确性。
Comparative Analysis of Neural Retriever-Reranker Pipelines for Retrieval-Augmented Generation over Knowledge Graphs in E-commerce Applications
- 设计多组检索-重排流水线，适配电商知识图谱的自然语言查询。
- 在STaRK数据集上实现Hit@1提升20.4%，MRR提升14.5%。
- 为生产级RAG系统部署提供可落地的结构化知识集成方案。
大型语言模型（LLMs）的进步推动了自然语言处理的发展，使复杂的信息检索与生成任务成为可能。检索增强生成（RAG）通过融合外部知识源与生成模型，显著提升了事实准确性和上下文相关性。尽管RAG在非结构化文本中表现优异，但在结构化知识图谱上的应用仍面临挑战：如何在连接的图结构中高效检索，并在生成过程中保持上下文关系。交叉编码器虽能提升检索精度，但其在结构化数据中的整合尚不充分。本研究针对电商场景，基于生产级半结构化知识库STaRK，设计并对比评估了多种检索-重排流水线。实验结果表明，所提方法相比已有基准显著提升性能，在Hit@1上提高20.4%，在平均倒数排名（MRR）上提高14.5%。研究为领域特定知识库集成至生成系统提供了实用框架，其成果不仅适用于电商，还可推广至需从结构化知识库中检索信息的其他领域。
原文摘要 · Abstract (English)
Recent advancements in Large Language Models (LLMs) have transformed Natural Language Processing (NLP), enabling complex information retrieval and generation tasks. Retrieval-Augmented Generation (RAG) has emerged as a key innovation, enhancing factual accuracy and contextual grounding by integrating external knowledge sources with generative models. Although RAG demonstrates strong performance on unstructured text, its application to structured knowledge graphs presents challenges: scaling retrieval across connected graphs and preserving contextual relationships during response generation. Cross-encoders refine retrieval precision, yet their integration with structured data remains underexplored. Addressing these challenges is crucial for developing domain-specific assistants that operate in production environments. This study presents the design and comparative evaluation of multiple Retriever-Reranker pipelines for knowledge graph natural language queries in e-Commerce contexts. Using the STaRK Semi-structured Knowledge Base (SKB), a production-scale e-Commerce dataset, we evaluate multiple RAG pipeline configurations optimized for language queries. Experimental results demonstrate substantial improvements over published benchmarks, achieving 20.4% higher Hit@1 and 14.5% higher Mean Reciprocal Rank (MRR). These findings establish a practical framework for integrating domain-specific SKBs into generative systems. Our contributions provide actionable insights for the deployment of production-ready RAG systems, with implications that extend beyond e-Commerce to other domains that require information retrieval from structured knowledge bases.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。