The link prediction task aims to predict missing entities or relations in the knowledge graph and is essential for the downstream application. Existing well-known models deal with this task by mainly focusing on representing knowledge graph triplets in the distance space or semantic space. However, they can not fully capture the information of head and tail entities, nor even make good use of hierarchical level information. Thus, in this paper, we propose a novel knowledge graph embedding model for the link prediction task, namely, HIE, which models each triplet (\textit{h}, \textit{r}, \textit{t}) into distance measurement space and semantic measurement space, simultaneously. Moreover, HIE is introduced into hierarchical-aware space to leverage rich hierarchical information of entities and relations for better representation learning. Specifically, we apply distance transformation operation on the head entity in distance space to obtain the tail entity instead of translation-based or rotation-based approaches. Experimental results of HIE on four real-world datasets show that HIE outperforms several existing state-of-the-art knowledge graph embedding methods on the link prediction task and deals with complex relations accurately.

翻译：链接预测任务旨在预测知识图谱中缺失的实体或关系，对于下游应用至关重要。现有知名模型主要通过将知识图谱三元组表示在距离空间或语义空间中来处理此任务。然而，它们无法充分捕捉头尾实体的信息，甚至不能有效利用层次级别信息。因此，本文提出一种用于链接预测任务的新型知识图谱嵌入模型，即HIE，该模型将每个三元组（\textit{h}，\textit{r}，\textit{t}）同时建模到距离测量空间和语义测量空间中。此外，HIE引入层次感知空间，以利用实体和关系的丰富层次信息实现更好的表示学习。具体而言，我们在距离空间中对头实体应用距离变换操作以获取尾实体，而非采用基于平移或旋转的方法。HIE在四个真实世界数据集上的实验结果表明，该模型在链接预测任务上优于几种现有最先进的知识图谱嵌入方法，并能准确处理复杂关系。