论文标题
知识图表示中负面抽样的全面分析学习
Comprehensive Analysis of Negative Sampling in Knowledge Graph Representation Learning
论文作者
论文摘要
负抽样（NS）损失在学习知识图嵌入（KGE）中起着重要的作用，以处理大量实体。但是，适当选择了KGE降解没有超参数的降解，例如NS损失中的余量和负样本的数量。目前，经验超参数调整以计算时间为代价解决了这个问题。为了解决这个问题，我们理论上分析了NS损失，以帮助高参数调整，并了解NS损失在KGE学习中的更好使用。我们的理论分析表明，具有限制值范围的评分方法，例如transe和旋转，需要适当调整边缘项或与没有限制值范围（例如重新，复杂，复杂和疏远）的负相同的负相同数量。我们还提出了专门从理论方面研究的KGE中NS损失的子采样方法。我们对FB15K-237，WN18RR和Yago3-10数据集的经验分析表明，实际训练的模型的结果与我们的理论发现一致。
Negative sampling (NS) loss plays an important role in learning knowledge graph embedding (KGE) to handle a huge number of entities. However, the performance of KGE degrades without hyperparameters such as the margin term and number of negative samples in NS loss being appropriately selected. Currently, empirical hyperparameter tuning addresses this problem at the cost of computational time. To solve this problem, we theoretically analyzed NS loss to assist hyperparameter tuning and understand the better use of the NS loss in KGE learning. Our theoretical analysis showed that scoring methods with restricted value ranges, such as TransE and RotatE, require appropriate adjustment of the margin term or the number of negative samples different from those without restricted value ranges, such as RESCAL, ComplEx, and DistMult. We also propose subsampling methods specialized for the NS loss in KGE studied from a theoretical aspect. Our empirical analysis on the FB15k-237, WN18RR, and YAGO3-10 datasets showed that the results of actually trained models agree with our theoretical findings.
