HyCubE: Efficient Knowledge Hypergraph 3D Circular Convolutional Embedding
CoRR(2024)
摘要
Existing knowledge hypergraph embedding methods mainly focused on improving
model performance, but their model structures are becoming more complex and
redundant. Furthermore, due to the inherent complex semantic knowledge, the
computation of knowledge hypergraph embedding models is often very expensive,
leading to low efficiency. In this paper, we propose a feature interaction and
extraction-enhanced 3D circular convolutional embedding model, HyCubE, which
designs a novel 3D circular convolutional neural network and introduces the
alternate mask stack strategy to achieve efficient n-ary knowledge hypergraph
embedding. By adaptively adjusting the 3D circular convolution kernel size and
uniformly embedding the entity position information, HyCubE improves the model
performance with fewer parameters and reaches a better trade-off between model
performance and efficiency. 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 HyCubE consistently outperforms state-of-the-art baselines, with an
average improvement of 4.08
all metrics. Commendably, HyCubE speeds up by an average of 7.55x and reduces
memory usage by an average of 77.02
baselines.
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