HyCubE: Efficient Knowledge Hypergraph 3D Circular Convolutional Embedding

Understand

Knowledge hypergraph embedding models are usually computationally expensive due to the inherent complex semantic information.

• However, existing works mainly focus on improving the effectiveness of knowledge hypergraph embedding, making the model architecture more complex and redundant.
• It is desirable and challenging for knowledge hypergraph embedding to reach a trade-off between model effectiveness and efficiency.
• In this paper, we propose an end-to-end efficient knowledge hypergraph embedding model, HyCubE, which designs a novel 3D circular convolutional neural network and the alternate mask stack strategy to enhance the interaction and extraction of feature information comprehensively.