Summary of Hierarchical Knowledge Graph Construction From Images For Scalable E-commerce, by Zhantao Yang et al.
Hierarchical Knowledge Graph Construction from Images for Scalable E-Commerce
by Zhantao Yang, Han Zhang, Fangyi Chen, Anudeepsekhar Bolimera, Marios Savvides
First submitted to arxiv on: 28 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed novel method constructs structured product knowledge graphs from raw product images by leveraging vision-language models (VLMs) and large language models (LLMs). The method automates the process, allowing for timely graph updates. A human-annotated e-commerce product dataset is also introduced for benchmarking product property extraction in knowledge graph construction. Compared to a baseline, the proposed method outperforms in all metrics and evaluated properties, demonstrating its effectiveness and potential applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to build a special kind of database called a knowledge graph. This graph helps e-commerce websites by organizing information about products in a structured way. The method uses computer vision and language models to create the graph automatically. A dataset was also created for testing how well this method works. Compared to other methods, the proposed approach does better at extracting useful information from product data, making it a promising solution. |
Keywords
» Artificial intelligence » Knowledge graph
