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

Medical knowledge graphs have emerged as essential tools for representing complex relationships among medical entities. However, existing methods for learning embeddings from medical knowledge graphs, such as DistMult, RotatE, ConvE, InteractE, JointE, and ConvKB, may not adequately capture the unique challenges posed by the domain, including the heterogeneity of medical entities, rich hierarchical structures, large-scale, high-dimensionality, and noisy and incomplete data. In this study, we propose an Adaptive Hierarchical Transformer with Memory (AHTM) model, coupled with a teacher–student model compression approach, to effectively address these challenges and learn embeddings from a rich medical knowledge dataset containing diverse entities and relationship sets. We evaluate the AHTM model on this newly constructed “Med-Dis” dataset and demonstrate its superiority over baseline methods. The AHTM model achieves substantial improvements in Mean Rank (MR) and Hits@10 values, with the highest MR value increasing by nearly 56% and Hits@10 increasing by 39%. Furthermore, we observe similar performance enhancements on the “FB15K-237” and “WN18RR” datasets. Our model compression approach, incorporating knowledge distillation and weight quantization, effectively reduces the model’s storage and computational requirements, making it suitable for resource-constrained environments. Overall, the proposed AHTM model and compression techniques offer a novel and effective solution for learning embeddings from medical knowledge graphs and enhancing our understanding of complex relationships among medical entities, while addressing the inadequacies of existing approaches.