Knowledge graph link prediction by fusing semantic space mapping and convolution neural networks

In research on knowledge graph representation learning, most of the existing methods use shallow linear models when extracting explicit features, while deep nonlinear models are preferred when extracting implicit features. Although the shallow linear model can learn explicit features, the effect of implicit feature extraction is limited, while the deep nonlinear model can learn implicit features, but it is easy to lead to problems such as too many parameters, overfitting, and loss of explicit features. Aiming at the above problems, this paper proposes a new feature extraction frame-work--- JointMC, which highlights entity-related features through semantic spatial mapping and combines one- and two-dimensional convolutional networks to extract explicit and implicit features in the knowledge graph. JointMC adopts a semantic spatial mapping model to learn the semantic information of the entity, filters irrelevant features, and highlights the features that are closely related to the entity. It then combines the semantic spatial representation with 1D and 2D convolutional networks to extract explicit and implicit features in the knowledge graph. Spatial mapping is combined with 1D and 2D convolutional networks to extract implicit and explicit features. Experimental com-parisons with several models confirm the good performance of JointMC in the link prediction task.