Knowledge graph embedding (KGE) aims to map entities and relations of a
knowledge graph (KG) into a low-dimensional and dense vector space via
contrasting the positive and negative triples. In the training process of KGEs,
negative sampling is essential to find high-quality negative triples since KGs
only contain positive triples. Most existing negative sampling methods assume
that non-existent triples with high scores are high-quality negative triples.
However, negative triples sampled by these methods are likely to contain noise.
Specifically, they ignore that non-existent triples with high scores might also
be true facts due to the incompleteness of KGs, which are usually called false
negative triples. To alleviate the above issue, we propose an easily pluggable
denoising mixup method called DeMix, which generates high-quality triples by
refining sampled negative triples in a self-supervised manner. Given a sampled
unlabeled triple, DeMix firstly classifies it into a marginal pseudo-negative
triple or a negative triple based on the judgment of the KGE model itself.
Secondly, it selects an appropriate mixup partner for the current triple to
synthesize a partially positive or a harder negative triple. Experimental
results on the knowledge graph completion task show that the proposed DeMix is
superior to other negative sampling techniques, ensuring corresponding KGEs a
faster convergence and better link prediction results.

通过提出一种名为 DeMix 的易于嵌入的去噪混合方法，将负样本三元组进行改进，使之成为高质量的三元组，从而保证了知识图谱嵌入模型的更快收敛和更好的链接预测结果。