用AI代理自动构建电商产品知识图谱，提升数据可用性。
AI Agent-Driven Framework for Automated Product Knowledge Graph Construction in E-Commerce
- 设计三阶段代理流程，自动生成与优化产品知识图谱
- 在空调产品数据上实现超97%属性覆盖率，冗余极低
- 无需预设模板，适合大规模电商知识结构化
电商平台快速扩张产生了海量非结构化商品数据，给信息检索、推荐系统和数据分析带来挑战。知识图谱（KG）能以结构化方式组织此类数据，但构建特定产品知识图谱仍依赖人工且复杂。本文提出一种完全自动化的AI代理驱动框架，直接从非结构化商品描述中构建产品知识图谱。该方法利用大语言模型（LLMs），通过三个专用代理完成：本体创建与扩展、本体优化、知识图谱填充。该代理式方法确保语义一致性、可扩展性和高质量输出，无需预定义模式或手工规则。我们在真实空调商品描述数据集上评估系统，验证了其在本体生成和知识图谱填充方面的优异表现。框架实现超过97%的属性覆盖率和极低冗余，证明其有效性和实际应用价值。本工作展示了大语言模型在零售领域自动化结构化知识提取的潜力，为智能商品数据集成与利用提供可扩展路径。
原文摘要 · Abstract (English)
The rapid expansion of e-commerce platforms generates vast amounts of unstructured product data, creating significant challenges for information retrieval, recommendation systems, and data analytics. Knowledge Graphs (KGs) offer a structured, interpretable format to organize such data, yet constructing product-specific KGs remains a complex and manual process. This paper introduces a fully automated, AI agent-driven framework for constructing product knowledge graphs directly from unstructured product descriptions. Leveraging Large Language Models (LLMs), our method operates in three stages using dedicated agents: ontology creation and expansion, ontology refinement, and knowledge graph population. This agent-based approach ensures semantic coherence, scalability, and high-quality output without relying on predefined schemas or handcrafted extraction rules. We evaluate the system on a real-world dataset of air conditioner product descriptions, demonstrating strong performance in both ontology generation and KG population. The framework achieves over 97\% property coverage and minimal redundancy, validating its effectiveness and practical applicability. Our work highlights the potential of LLMs to automate structured knowledge extraction in retail, providing a scalable path toward intelligent product data integration and utilization.
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