  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.
