A Model of Text-Enhanced Knowledge Graph Representation Learning with Collaborative Attention
Abstract
This paper proposes a novel collaborative attention mechanism, to fully utilize the mutually reinforcing relationship among the knowledge graph representation learning procedure (i.e., structure representation) and textual relation representation learning procedure (i.e., text representation). Based on this collaborative attention mechanism, a text-enhanced knowledge graph (KG) representation model is proposed, which could utilize textual information to enhance the knowledge representations and make the multi-direction signals to be fully integrated to learn more accurate textual representations for further improving structure representation and vice versa. Experimental results demonstrate the efficiency of the proposed model on both link prediction task and triple classification task.