SelectE: Multi-scale adaptive selection network for knowledge graph representation learning

Most knowledge graphs in the real world suffer from incompleteness which can be addressed through knowledge graph representation learning (KGRL) techniques that use known facts to infer missing links. In this paper, a novel multi-scale adaptive selection network for KGRL, namely SelectE, is developed to learn richer multi-scale interactive features and automatically select important features, thereby achieving promising KGRL performance. Specifically, first, the input feature matrix is redesigned to better cooperate with multi-scale convolution to improve the interaction of entities and relations. Second, a multi-scale learning module is designed to learn richer multi-scale features from the input matrix using multiple branches with different kernel sizes. Third, to automatically strengthen the important features and suppress the useless features, a multi-scale adaptive selection mechanism is proposed to dynamically allocate the weights of the obtained features based on their contained information. The core of SelectE is to maximize interactions while also considering how to better utilize features. Finally, the outstanding performance of SelectE is validated by a series of comparison experiments on seven benchmark datasets (FB15k-237, WN18RR, FB15k, WN18, YAGO3-10, KINSHIP, UMLS). The experimental results show that SelectE outperforms other state-of-the-art models, demonstrating its remarkable performance and generalization.