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.
@article{arxiv.2410.21237,
title = {Hierarchical Knowledge Graph Construction from Images for Scalable E-Commerce},
author = {Zhantao Yang and Han Zhang and Fangyi Chen and Anudeepsekhar Bolimera and Marios Savvides},
journal= {arXiv preprint arXiv:2410.21237},
year = {2024}
}