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.

翻译：知识图谱（Knowledge Graph，KG）在各种人工智能系统中正发挥着日益重要的作用。对于电子商务而言，高效且低成本的自动化知识图谱构建方法是支撑各类成功下游应用的基础。本文提出了一种从原始产品图像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）和大语言模型（LLM）的最新进展，实现了流程的完全自动化，并支持知识图谱的及时更新。我们还提出了一个用于知识图谱构建中产品属性提取任务评估的人工标注电子商务产品数据集。我们的方法在所有评估指标和属性上均优于基线模型，证明了其有效性和广阔的应用前景。