Summary of Noise-powered Multi-modal Knowledge Graph Representation Framework, by Zhuo Chen et al.
Noise-powered Multi-modal Knowledge Graph Representation Framework
by Zhuo Chen, Yin Fang, Yichi Zhang, Lingbing Guo, Jiaoyan Chen, Jeff Z. Pan, Huajun Chen, Wen Zhang
First submitted to arxiv on: 11 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 A unified Multi-Modal Knowledge Graph (MMKG) representation learning framework is crucial for effectively embedding structured knowledge into multi-modal Large Language Models, addressing issues like knowledge misconceptions and multi-modal hallucinations. This paper explores the efficacy of models in accurately embedding entities within MMKGs through two tasks: Multi-modal Knowledge Graph Completion (MKGC) and Multi-modal Entity Alignment (MMEA). The proposed SNAG method uses a Transformer-based architecture with modality-level noise masking to robustly integrate multi-modal entity features in KGs. By incorporating specific training objectives for both MKGC and MMEA, the approach achieves state-of-the-art performance across ten datasets, demonstrating its versatility. Additionally, SNAG can enhance other existing methods, providing stable performance improvements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating a way to represent knowledge from different sources (like images, videos, and text) in a single framework. This is important because it helps prevent mistakes when combining information from these different sources. The researchers tested their method on two main tasks: completing gaps in knowledge graphs and aligning entities across different sources. They created a new model called SNAG that uses special noise-masking techniques to combine information from different sources. Their approach performed better than others on ten different datasets, showing it’s reliable. Plus, SNAG can help improve other methods too. |
Keywords
» Artificial intelligence » Alignment » Embedding » Knowledge graph » Multi modal » Representation learning » Transformer
