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The link prediction task aims to predict missing entities or relations in theknowledge graph and is essential for the downstream application. Existingwell-known models deal with this task by mainly focusing on representingknowledge graph triplets in the distance space or semantic space. However, theycan not fully capture the information of head and tail entities, nor even makegood use of hierarchical level information. Thus, in this paper, we propose anovel knowledge graph embedding model for the link prediction task, namely,HIE, which models each triplet (\textit{h}, \textit{r}, \textit{t}) intodistance measurement space and semantic measurement space, simultaneously.Moreover, HIE is introduced into hierarchical-aware space to leverage richhierarchical information of entities and relations for better representationlearning. Specifically, we apply distance transformation operation on the headentity in distance space to obtain the tail entity instead of translation-basedor rotation-based approaches. Experimental results of HIE on four real-worlddatasets show that HIE outperforms several existing state-of-the-art knowledgegraph embedding methods on the link prediction task and deals with complexrelations accurately.

Joint embedding in Hierarchical distance and semantic representation learning for link prediction