Summary of Zero-shot Relational Learning For Multimodal Knowledge Graphs, by Rui Cai et al.
Zero-Shot Relational Learning for Multimodal Knowledge Graphs
by Rui Cai, Shichao Pei, Xiangliang Zhang
First submitted to arxiv on: 9 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Multimedia (cs.MM)
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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 novel end-to-end framework is proposed for zero-shot relational learning in multimodal knowledge graph completion (KGC), which leverages diverse multimodal information and graph structures to facilitate inference on newly discovered relations without training data. The framework consists of three components: a multimodal learner, structure consolidator, and relation embedding generator. This approach outperforms existing methods on three multimodal KGC benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way is found to learn relationships between things in big datasets that have different types of information like text, images, or audio. Right now, there isn’t a good way to do this without any practice data. The new method uses multiple types of information and the structure of the dataset to make predictions about new relationships. |
Keywords
» Artificial intelligence » Embedding » Inference » Knowledge graph » Zero shot
