Relation-attention semantic-correlative knowledge graph embedding for inductive link prediction

Link prediction has increasingly been the focus of significant research interest, benefited from the explosion of machine learning and deep learning techniques. Graph embedding has been proven to be an effective method for predicting missing links in graph-based structure. In this work, we propose a novel relation-attention semantic-correlative graph embedding for inductive link prediction. Unlike existing embedding-based methods, we shift the node representation learning from a node’s perspective to a relational subgraph perspective. Our model has a better inductive bias to learn entity-independent relational semantics. We consider two kinds of relational subgraph topology for a given entity pair: relational correlation subgraph and relational path subgraph. Firstly, we capture the structure of neighboring relation-properties of semantic-missing entity by relational correlation subgraph. Secondly, we capture the set of relational paths between given entity pair by relational path subgraph. Finally, we organize the above two modules in a unified framework for relation prediction. Our ablation experiments show that two kinds of relational subgraph topology are important for relation prediction. Experimental results on six benchmark datasets demonstrate that our proposed graph embedding outperforms existing state-of-the-art models for link prediction tasks.