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Embedding Multimodal Relational Data for Knowledge Base Completion (D18-1)

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Challenge: Existing approaches focus on a finite set of entities, ignoring the variety of data types used in knowledge bases.
Approach: They propose multimodal knowledge base embeddings that use different neural encoders for observed data and different neural decoders to learn embedded entities and multimodal data.
Outcome: The proposed models outperform existing methods with 5-7% accuracy over existing methods.

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