An entity-weights-based convolutional neural network for large-sale complex knowledge embedding

•In terms of complex relationships and multi-relational circle structures, we demonstrate that they are ubiquitous and inevitable.•The measures selected for baseline methods are not optimal by comparison experiments, and we develop two models based on matrix factorization, called sMFE and tMFE to improve embedding results.•We first use the complete incidence matrix to construct ComInE model, which contain the most comprehensive topological properties of graphs.•Use entity weights calculated by PageRank to extend TransE and apply them in Deep Learning, which reduce the cost of adding extra information while ensuring the performance.•A convolutional neural network with three hidden layers based on node weights, CNNe, is proposed for knowledge embedding.