• Corpus ID: 2768038

Embedding Entities and Relations for Learning and Inference in Knowledge Bases

@article{Yang2014EmbeddingEA,
  title={Embedding Entities and Relations for Learning and Inference in Knowledge Bases},
  author={Bishan Yang and Wen-tau Yih and Xiaodong He and Jianfeng Gao and Li Deng},
  journal={CoRR},
  year={2014},
  volume={abs/1412.6575}
}
Abstract: We consider learning representations of entities and relations in KBs using the neural-embedding approach. [] Key Method Under this framework, we compare a variety of embedding models on the link prediction task. We show that a simple bilinear formulation achieves new state-of-the-art results for the task (achieving a top-10 accuracy of 73.2% vs. 54.7% by TransE on Freebase).
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