论文标题
实体意识到负面抽样，辅助损失因知识图嵌入的虚假负面预测
Entity Aware Negative Sampling with Auxiliary Loss of False Negative Prediction for Knowledge Graph Embedding
论文作者
论文摘要
知识图（kg）嵌入被广泛用于使用kgs的许多下游应用中。通常，由于KGS仅包含地面真相三元，因此有必要构建任意负面样本来表示KGS。最近，已经研究了对高质量负面负面负面质量的各种方法，因为负三元组的质量对KG嵌入具有很大的影响。在本文中，我们提出了一种称为“实体意识到负面采样（EAN）的新方法”，该方法能够通过将高斯分布在对齐实体指数空间中采用高斯分布来样本与正实体相似。此外，我们引入了假阴性预测的辅助损失，这可以减轻采样的假阴性三元组的影响。所提出的方法可以产生高质量的负样本，而不论负样本量如何，并有效地减轻了假阴性样品的影响。标准基准测试的实验结果表明，我们的EAN在几种知识图嵌入模型上的负面采样方法优于现有的最新方法。此外，即使否定样本的数量仅限于一个，提出的方法也可以达到竞争性能。
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.
