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.

翻译：知识超图嵌入模型通常因固有的复杂语义信息而计算成本高昂。然而，现有工作主要集中于提升知识超图嵌入的效果，导致模型架构更为复杂冗余。在模型效果与效率之间取得平衡，是知识超图嵌入领域既具需求又充满挑战的目标。本文提出一种端到端的高效n元知识超图嵌入模型HyCubE，该模型设计了新颖的三维循环卷积神经网络与交替掩码堆叠策略，以全面增强特征信息的交互与提取。此外，通过自适应调整三维循环卷积层结构以处理不同元数的知识超图并减少参数量，我们提出的模型实现了效果与效率间更优的权衡。同时，我们采用基于实体掩码机制的1-N多线性评分方法，进一步加速了模型训练效率。最终，在所有数据集上的大量实验结果表明，我们提出的模型在各项指标上均持续优于现有最先进的基线模型，平均提升幅度达7.30%-9.53%，最大提升幅度达33.82%。与此同时，与最新最先进基线模型的平均指标相比，HyCubE的训练速度提升了4.12倍，GPU内存使用降低了52.19%，参数量减少了85.21%。