Knowledge graphs are useful for many AI tasks but often have missing facts. To populate the graphs, knowledge graph embedding models have been developed. TransE is one of such models and the first translation-based method. TransE is well known because the principle of TransE can effectively capture the rules of a knowledge graph although it seems very simple. However, TransE has problems with its regularization and an unchangeable ratio of negative sampling. In this paper, we generalize TransE to solve these problems by proposing knowledge graph embedding on a Lie group (KGLG) and the Weighted Negative Part (WNP) method for the objective function of translation-based models. KGLG is the novel translation-based method which embeds entities and relations of a knowledge graph on any Lie group. It allows us not to employ regularization during training of the model if we choose a compact lie group for the embedding space. The WNP method is for changing the ratio of negative sampling, which enhances translation-based models. Our approach outperforms other state-of-the-art approaches such as TransE, DistMult, and ComplEx on a standard link prediction task. We show that TorusE, KGLG on a torus, is scalable to large-size knowledge graphs and faster than the original TransE.

中文翻译：

---

基于广义翻译的知识图谱嵌入

知识图对许多 AI 任务很有用，但通常缺少事实。为了填充图，已经开发了知识图嵌入模型。TransE 是这样的模型之一，也是第一个基于翻译的方法。TransE 之所以广为人知，是因为 TransE 的原理虽然看起来很简单，但却可以有效地捕捉知识图谱的规则。然而，TransE 在其正则化和不可改变的负采样率方面存在问题。在本文中，我们通过提出知识图嵌入李群 (KGLG) 和加权负部分 (WNP) 方法来概括 TransE 来解决这些问题，用于基于翻译的模型的目标函数。KGLG 是一种新颖的基于翻译的方法，它将知识图谱的实体和关系嵌入到任何李群上。如果我们为嵌入空间选择一个紧凑的谎言群，它允许我们在模型训练期间不使用正则化。WNP 方法是为了改变负采样的比例，从而增强了基于翻译的模型。我们的方法在标准链接预测任务上优于其他最先进的方法，例如 TransE、DistMult 和 ComplEx。我们展示了 TorusE，环面上的 KGLG，可扩展到大型知识图谱，并且比原始 TransE 更快。