The graph attention network in relational reasoning based on knowledge graph

The knowledge graph in the knowledge graph is sparse due to missing relationships and incomplete triples, which promotes research on knowledge graph completion (relationship prediction). However, the purpose of knowledge graph reasoning technology is to infer unknown knowledge or identify wrong knowledge based on the knowledge learned in the knowledge graph to improve the knowledge graph. The graph attention network (GAT) can improve the input constraints of RNN and convolution and introduce a self-attention architecture to perform classification of graph-structured data. However, the model still has two limitations: applying the model in the knowledge graph will ignore the relationship (edge) features; Currently popular models can only use the k-th hop output for multi-hop embedding learning, resulting in the loss of a lot of early embedding information (such as one hop) during the graph attention process. This paper proposes a new method to improve the quality of the model by embedding relational features.