MMPGCN: Multi-hop Message Passing Graph Convolutional Network for Knowledge Graph Completion

Graph Convolutional Network (GCN) is extensively utilized in the domain of Knowledge Graph Completion (KGC) which is aiming to predict the absent entities or relationships within Knowledge Graph (KG). The majority of traditional GCN-based models employ the method of passing messages layer by layer to capture the characteristics of distant neighbors. However, this approach fails to effectively integrate the semantic feature information of multi-hop neighbors due to the significant complexity of relations when attempting to learn continuous vectors for entities. To overcome this limitation, this study introduces Multi-Hop Message Passing Graph Convolutional Network (MMPGCN), a novel framework for GCNs that effectively integrates feature information from indirect neighbors in a novel manner. Furthermore, the graph attention mechanism is utilized in the proposed model to differentiate the weights of various indirect neighbors. A robust and expressive balancing gate mechanism is designed to integrate the information from both direct and indirect neighbors to produce the ultimate representation. The approach is assessed using FB15k237 and WN18RR datasets, and it demonstrates superior performance in comparison to state-of-the-art methods for KGC task.