AI 中文总结
针对现有时尚知识图谱的局限性，本文提出全领域知识图谱FashionEcoKG，并开发无需训练的PG-RAG框架，通过双粒度路径重排序模块提升时尚问答的检索与答案准确性，效果优于多种基线方法。
AI 中文摘要
时尚是一个知识密集型领域，有效决策依赖于整合多种类型的知识。尽管大型语言模型（LLMs）已在诸多领域实现变革，但其在时尚领域的应用仍受限于幻觉问题和较弱的领域专业性。基于知识图谱（KG）的检索增强生成（RAG）为向大型语言模型添加结构化知识提供了可行途径，但现有时尚知识图谱通常仅局限于产品级属性或物品关系，无法覆盖更广泛的时尚生态系统。为填补这些空白，我们提出FashionEcoKG，这是一个全面的、覆盖全领域的知识图谱，以专家级的精度和专业性构建而成。它通过三阶段智能体流水线构建，从权威教材中提取高保真知识核心，并通过跨领域增强和生成式扩展强化结构连通性。为利用该资源，我们进一步开发PG-RAG（剪枝- grounding RAG），这是一个无需训练的框架，旨在处理时尚查询的概念密度和语言噪声。具体而言，我们引入双粒度路径重排序（DGPR）模块，分为两个阶段：基于剪枝的语义排序（PSR）模块将每个查询提炼为骨架形式以提升检索召回率，而基于 grounding 的智能体重排序（GAR）则针对原始完整查询对候选路径进行逐点审查，以确保全局相关性。在精心整理的时尚问答数据集上的实验表明，PG-RAG 可有效利用 FashionEcoKG 提升检索和答案准确性，优于非 RAG 方法及现有 KG-RAG 基线。
英文摘要
Fashion is a knowledge-intensive domain in which effective decision-making depends on integrating multiple types of knowledge. Although Large Language Models (LLMs) have transformed many areas, their application in fashion remains limited by hallucinations and weak domain specialization. Knowledge Graph (KG)-based Retrieval-Augmented Generation (RAG) offers a promising way to add structured knowledge to LLMs. However, existing fashion KGs are typically restricted to product-level attributes or item relations, and fail to capture the broader fashion ecosystem. To bridge these gaps, we propose \textbf{FashionEcoKG}, a comprehensive, domain-wide knowledge graph built with expert-level precision and professionalism. It is constructed through a three-stage agentic pipeline that extracts high-fidelity knowledge cores from authoritative textbooks and strengthens structural connectivity through cross-domain augmentation and generative expansion. To leverage this resource, we further develop \textbf{PG-RAG} (Pruning-Grounding RAG), a training-free framework designed to handle the conceptual density and linguistic noise of fashion queries. Specifically, we introduce a Dual-Granularity Path Re-Ranking (DGPR) module of two stages. The Pruning-based Semantic Ranking (PSR) module distills each query into a skeleton form to improve retrieval recall, while the Grounding-based Agentic Ranking (GAR) performs point-wise scrutiny of candidate paths against the original full query to ensure global relevance. Experiments on a curated fashion QA dataset show that PG-RAG effectively leverages FashionEcoKG to improve retrieval and answer accuracy, outperforming both non-RAG and existing KG-RAG baselines.
