用视觉语言模型自动构建电商知识图谱，支持高效更新。
Hierarchical Knowledge Graph Construction from Images for Scalable E-Commerce
- 结合视觉语言模型与大语言模型，自动化构建商品知识图谱。
- 在多个评估指标上优于基线，显著提升属性提取准确率。
- 适用于需要快速更新商品信息的电商平台和研究者。
知识图谱（KG）在各类AI系统中作用日益重要。对于电商领域，高效且低成本的自动化知识图谱构建方法是实现众多下游应用的基础。本文提出一种从原始商品图像中构建结构化产品知识图谱的新方法。该方法协同利用视觉语言模型（VLM）与大语言模型（LLM），完全自动化流程，支持及时更新。同时，我们构建了一个人工标注的电商商品数据集，用于知识图谱构建中的属性提取基准测试。实验结果表明，该方法在所有评估指标和属性上均优于基线，验证了其有效性和广阔的应用前景。
原文摘要 · Abstract (English)
Knowledge Graph (KG) is playing an increasingly important role in various AI systems. For e-commerce, an efficient and low-cost automated knowledge graph construction method is the foundation of enabling various successful downstream applications. In this paper, we propose a novel method for constructing structured product knowledge graphs from raw product images. The method cooperatively leverages recent advances in the vision-language model (VLM) and large language model (LLM), fully automating the process and allowing timely graph updates. We also present a human-annotated e-commerce product dataset for benchmarking product property extraction in knowledge graph construction. Our method outperforms our baseline in all metrics and evaluated properties, demonstrating its effectiveness and bright usage potential.
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