Convolutional 3D Embedding for Knowledge Graph Completion

Abstract

Link prediction is to predict missing relations between entities for Knowledge Graph Completion (KGC). Convolution neural network has been used in much previous work on link prediction to capture fundamental data pattern of knowledge graph. However, because these models use low-dimensional convolution operation, which limits their performance, they learn fewer expressive features. Further more, they do not have the the capability of keeping the translation property of knowledge triplet, which is an important property for knowledge reasoning. Focusing on these problems, we propose Conv3D (3D Convolution Embedding), a neural network model for link prediction that uses 3D convolution. To capture deeper feature interactions in the knowledge graph, we employ 3D convolution instead of shallow 1D or 2D convolution for generating triplet scores. We conduct link prediction experiments on four general datasets (WN18, WN18RR, FB15k, FB15k-237) and get state-of-the-art (SOTA) results on WN18 and WN18RR. We also explore the influence of convolution parameters (reshaping dimension, number of filters, kernel size) on FB15k-237 and obtain quantitative analytical findings.