ResE: A Fast and Efficient Neural Network-Based Method for Link Prediction

In this study, we present a novel embedding model, named ResE, for predicting links in knowledge graphs. ResE employs depth-separable convolution and residual blocks, integrated with channel attention mechanisms. surpasses previously published models, including the closely related TransE by achieving satisfactory mean rank (MR) excellent Hits@10 scores on both WN18RR FB15K-237 benchmarks. is promising model graph completion tasks, potential further investigation extension to new applications such as user-oriented relationship modeling. Although comparatively shallow compared computer vision convolutional architectures, future work may explore deeper models. exhibits remarkable performance outperforms existing approaches, thus setting benchmark completion. The outcomes of our study illustrate effectiveness incorporating accompanied mechanisms, modeling These findings highlight ResE’s push boundaries cutting-edge domain.