  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 n-ary 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. Furthermore, our proposed model achieves a better trade-off
between effectiveness and efficiency by adaptively adjusting the 3D circular
convolutional layer structure to handle different arity knowledge hypergraphs
with fewer parameters. In addition, we use 1-N multilinear scoring based on the
entity mask mechanism to further accelerate the model training efficiency.
Finally, extensive experimental results on all datasets demonstrate that our
proposed model consistently outperforms state-of-the-art baselines, with an
average improvement of 7.30%-9.53% and a maximum improvement of 33.82% across
all metrics. Meanwhile, HyCubE is 4.12x faster, GPU memory usage is 52.19%
lower, and the number of parameters is reduced by 85.21% compared with the
average metric of the latest state-of-the-art baselines.
