NativE: Multi-modal Knowledge Graph Completion in the Wild
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- NativE: Multi-modal Knowledge Graph Completion in the Wild
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AbstractLarge-scale knowledge graphs are usually incomplete. Knowledge graph embedding has achieved encouraging performance in alleviating the incompleteness of knowledge graphs. There are approaches to leverage the multi-modal content, such as text ...
Highlights- A novel multi-modal fusion model for knowledge graph embedding is proposed.
- The model can fuses multi-modal information to learn more effective embeddings.
- The model can be used to fuse more types of data with specified decoders.
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- General Chairs:
- Grace Hui Yang,
- Hongning Wang,
- Sam Han,
- Program Chairs:
- Claudia Hauff,
- Guido Zuccon,
- Yi Zhang
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Association for Computing Machinery
New York, NY, United States
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- Research-article
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- Zhejiang Provincial Natural Science Foundation of China
- Ningbo Natural Science Foundation
- Fundamental Research Funds for the Central Universities
- National Natural Science Foundation of China
- Yongjiang Talent Introduction Programme
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