Abstract: ConvE applies convolutional neural network (CNN) to link prediction tasks,and its outstanding performance has attracted significant attention in academia.However,CNN-based models like ConvE still inadequately extract graph structural information and fail to consider the multi-feature attributes of relations in knowledge graphs.To fully leverage graph structural features and the multi-feature properties of relations,this paper proposes a novel link prediction model——ComConvR,which extracts the multi-feature representations of relations and incorporates dense convolutional blocks into the CNN.This enhancement strengthens the networks feature extraction capability and enables multi-feature fusion for link prediction.Experiments on four benchmark datasets demonstrate the effectiveness of ComConvR,supported by ablation studies and key parameter analyses that validate the efficiency and contribution of the dense convolutional blocks.

Key words: link prediction, neural network, multi-feature perception, dense convolution