Translation-based embedding models have gained significant attention in link prediction tasks for knowledge graphs. TransE is the primary model among translation-based embeddings and is well-known for its low complexity and high efficiency. Therefore, most of the earlier works have modified the score function of the TransE approach in order to improve the performance of link prediction tasks. Nevertheless, proven theoretically and experimentally, the performance of TransE strongly depends on the loss function. Margin Ranking Loss (MRL) has been one of the earlier loss functions which is widely used for training TransE. However, the scores of positive triples are not necessarily enforced to be sufficiently small to fulfill the translation from head to tail by using relation vector (original assumption of TransE). To tackle this problem, several loss functions have been proposed recently by adding upper bounds and lower bounds to the scores of positive and negative samples. Although highly effective, previously developed models suffer from an expansion in search space for a selection of the hyperparameters (in particular the upper and lower bounds of scores) on which the performance of the translation-based models is highly dependent. In this paper, we propose a new loss function dubbed Adaptive Margin Loss (AML) for training translation-based embedding models. The formulation of the proposed loss function enables an adaptive and automated adjustment of the margin during the learning process. Therefore, instead of obtaining two values (upper bound and lower bound), only the center of a margin needs to be determined. During learning, the margin is expanded automatically until it converges. In our experiments on a set of standard benchmark datasets including Freebase and WordNet, the effectiveness of AML is confirmed for training TransE on link prediction tasks.
翻译:以翻译为基础的嵌入模型在知识图表的链接预测任务中得到了极大关注。 TransE是基于翻译的嵌入中的主要模型,因其复杂性低,效率高而广为人知。因此,大多数早期著作都修改了TransE方法的评分功能,以改进链接预测任务的绩效。然而,在理论上和实验上,TransE的性能在很大程度上取决于损失函数。Mrincin 分级损失(MRL)是以前广泛用于培训TransE的损失函数之一。然而,正三联体的分数不一定小到足以通过使用关系矢量(TransE的初始假设)实现从头到尾的翻译。为了解决这一问题,最近提出了几项损失函数,增加了上限,降低了正数和负数样本的分数。尽管以前开发的模型由于选择超参数(特别是分数的上限和下限)的搜索空间扩大而受到影响,但基于翻译模型的性能高度依赖它。在这个纸张上,我们提议在升级的模型的自动流流值中,包括升级的模型的升级、升级的模型的升级的升级、升级、升级的升级的升级的升级、升级的升级的升级的升级的模型的升级的升级的升级的计算、升级的升级的计算、升级的升级的升级的升级的计算、升级的升级的升级的计算、升级的升级的升级的计算、升级的升级的计算、升级的升级的计算。