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:链接预测任务的目标是在知识图谱中预测丢失实体或关系,这对于下游应用非常重要。现有的众所周知的模型主要通过在距离空间或语义空间中表示知识图谱三元组来处理此任务。然而,它们无法充分捕捉头尾实体的信息,甚至无法很好地利用层次信息。因此,在本文中,我们提出了一种新颖的用于链接预测任务的知识图谱嵌入模型,即HIE,它同时将每个三元组(h,r,t)建模为距离测量空间和语义测量空间。此外,将HIE引入层次感知空间中,以利用实体和关系的丰富层次信息进行更好的表示学习。具体地,在距离空间中对头实体应用距离变换操作以获得尾实体,而不是使用基于平移或基于旋转的方法。在四个真实世界的数据集上,HIE的实验结果表明,在链接预测任务中,HIE优于几种现有的最先进的知识图谱嵌入方法,并能够准确处理复杂的关系。