ConvD: Attention Enhanced Dynamic Convolutional Embeddings for Knowledge
  Graph Completion
Wenbin Guo Zhao Li Xin Wang Zirui Chen
Knowledge graphs generally suffer from incompleteness, which can be
alleviated by completing the missing information. Deep knowledge convolutional
embedding models based on neural networks are currently popular methods for
knowledge graph completion. However, most existing methods use external
convolution kernels and traditional plain convolution processes, which limits
the feature interaction capability of the model. In this paper, we propose a
novel dynamic convolutional embedding model ConvD for knowledge graph
completion, which directly reshapes the relation embeddings into multiple
internal convolution kernels to improve the external convolution kernels of the
traditional convolutional embedding model. The internal convolution kernels can
effectively augment the feature interaction between the relation embeddings and
entity embeddings, thus enhancing the model embedding performance. Moreover, we
design a priori knowledge-optimized attention mechanism, which can assign
different contribution weight coefficients to multiple relation convolution
kernels for dynamic convolution to improve the expressiveness of the model
further. Extensive experiments on various datasets show that our proposed model
consistently outperforms the state-of-the-art baseline methods, with average
improvements ranging from 11.30\% to 16.92\% across all model evaluation
metrics. Ablation experiments verify the effectiveness of each component module
of the ConvD model.