Facilitating Message Passing with Potential Links for Knowledge Graph Completion

Knowledge graph completion (KGC) aims at inferring missing links between two entities. Most previous models focus on learning representations for entities and relations via graph neural networks. In this formalism, representations heavily rely on structural information. However, it is common for Knowledge graphs (KGs) to be unconnected due to their inherent incompleteness, resulting in a significant loss of vital structural information. To overcome this issue, this paper proposes to increase the connectedness of KGs to facilitate message passing between entities. Specifically, we first augment KGs with a series of auxiliary triplets derived from outer nodes. Subsequently, a dynamically weighted graph convolutional layer is employed to unequally aggregate the representations of neighboring entities and dynamically combine this aggregated information with information from themselves. Finally, ConvE is utilized to calculate scores for all triplets. Extensive experiments on two benchmark datasets demonstrate the superiority of our method compared to strong baseline models.