Tensor factorization and distanced based models play important roles in knowledge graph completion (KGC). However, the relational matrices in KGC methods often induce a high model complexity, bearing a high risk of overfitting. As a remedy, researchers propose a variety of different regularizers such as the tensor nuclear norm regularizer. Our motivation is based on the observation that the previous work only focuses on the "size" of the parametric space, while leaving the implicit semantic information widely untouched. To address this issue, we propose a new regularizer, namely, Equivariance Regularizer (ER), which can suppress overfitting by leveraging the implicit semantic information. Specifically, ER can enhance the generalization ability of the model by employing the semantic equivariance between the head and tail entities. Moreover, it is a generic solution for both distance based models and tensor factorization based models. The experimental results indicate a clear and substantial improvement over the state-of-the-art relation prediction methods.
翻译:然而,KGC方法中的关系矩阵往往产生高模型复杂性,具有高超适应风险。作为一种补救措施,研究人员提出多种不同的监管者,如高温核规范调节器。我们的动机是基于以下观察,即以前的工作只侧重于参数空间的“大小”,而没有广泛触及隐含的语义信息。为了解决这一问题,我们提议一种新的正规化器,即平等调和器(ER),它可以通过利用隐含的语义信息来抑制过度匹配。具体地说,ER可以通过使用顶部和尾部实体之间的语义等同法来提高模型的通用能力。此外,它是基于远程模型和基于电荷因子化模型的通用解决方案。实验结果表明,与最先进的关系预测方法相比,情况有了明显和实质性的改善。