Hierarchical Knowledge Graph Construction from Images for Scalable E-Commerce
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866912090198900736 |
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| author | Yang, Zhantao Zhang, Han Chen, Fangyi Bolimera, Anudeepsekhar Savvides, Marios |
| author_facet | Yang, Zhantao Zhang, Han Chen, Fangyi Bolimera, Anudeepsekhar Savvides, Marios |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_21237 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Hierarchical Knowledge Graph Construction from Images for Scalable E-Commerce Yang, Zhantao Zhang, Han Chen, Fangyi Bolimera, Anudeepsekhar Savvides, Marios Artificial Intelligence 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. |
| title | Hierarchical Knowledge Graph Construction from Images for Scalable E-Commerce |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2410.21237 |