ER: Equivariance Regularizer for Knowledge Graph Completion

@inproceedings{Cao2022ERER,
  title={ER: Equivariance Regularizer for Knowledge Graph Completion},
  author={Zongsheng Cao and Qianqian Xu and Zhiyong Yang and Qingming Huang},
  booktitle={AAAI Conference on Artificial Intelligence},
  year={2022}
}
Tensor factorization and distanced based models play important roles in knowledge graph completion (KGC). However, the relational matrices in KGC methods often induce a high model complexity, bearing a high risk of overfitting. As a remedy, researchers propose a variety of different regularizers such as the tensor nuclear norm regularizer. Our motivation is based on the observation that the previous work only focuses on the “size” of the parametric space, while leaving the implicit semantic… 
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Temporal-structural importance weighted graph convolutional network for temporal knowledge graph completion

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