Mconvkgc: a novel multi-channel convolutional model for knowledge graph completion

The incompleteness of the knowledge graph limits its applications to various downstream tasks. To this end, numerous influential knowledge graph embedding models have been presented and have made great achievements in the domain of knowledge graph completion. However, most of these models only pay attention to the extraction of latent knowledge or translational features, and cannot comprehensively capture the surface semantics, latent interactions, and translational characteristics of triples. In this paper, a novel multi-channel convolutional model, MConvKGC, is presented for knowledge graph completion, which has three feature extraction channels and employs them to simultaneously extract shallow semantics, latent interactions, and translational characteristics, respectively. In addition, MConvKGC adopts an asymmetric convolutional block to comprehensively extract the latent interactions from triples, and process the generated feature maps with various attention mechanisms to further learn local dependencies between entities and relations. The results of the conducted link prediction experiments on FB15k-237, WN18RR, and UMLS indicate that our proposed MConvKGC shows excellent performance and outperforms previous state-of-the-art KGE models in the majority of cases.