  Knowledge graph (KG) embedding is widely used in many downstream applications
using KGs. Generally, since KGs contain only ground truth triples, it is
necessary to construct arbitrary negative samples for representation learning
of KGs. Recently, various methods for sampling high-quality negatives have been
studied because the quality of negative triples has great effect on KG
embedding. In this paper, we propose a novel method called Entity Aware
Negative Sampling (EANS), which is able to sample negative entities resemble to
positive one by adopting Gaussian distribution to the aligned entity index
space. Additionally, we introduce auxiliary loss for false negative prediction
that can alleviate the impact of the sampled false negative triples. The
proposed method can generate high-quality negative samples regardless of
negative sample size and effectively mitigate the influence of false negative
samples. The experimental results on standard benchmarks show that our EANS
outperforms existing the state-of-the-art methods of negative sampling on
several knowledge graph embedding models. Moreover, the proposed method
achieves competitive performance even when the number of negative samples is
limited to only one.
