Multi-Filter soft shrinkage network for knowledge graph embedding

Abstract

Incompleteness is a prominent issue pervasive in real-world knowledge graphs, and link prediction techniques, which utilize known facts to forecast missing or unknown links, have emerged as a critical technology for knowledge graph completion. Recently, convolutional neural network-based knowledge graph embedding (KGE) models have achieved breakthroughs in the field of link prediction by increasing the number of interactions between entities and relations. However, current models mainly emphasize stacking complex architectures to capture more interaction features, ignoring the fact that, as the number of features increases significantly, irrelevant features might overshadow important ones, thus impacting the performance of link prediction. To tackle this problem, this paper introduces a novel KGE model that is based on a multi-filter soft shrinkage network (MFSSN). First, this model adaptively constructs filters based on entity embedding and relationship embedding, thereby increasing the interactions between entities and relationships. Secondly, the soft shrinkage function is innovatively introduced into the model, and a specialized soft shrinkage sub-network is innovatively designed to effectively eliminate noise features and improve attention to important features. Finally, the attention mechanism is added to the network to enhance important features and suppress useless features, improving the ability to utilize features, and thereby enhancing model performance. Through extensive experiments on five benchmark datasets of different sizes, the strong performance and generalization ability of the proposed model are proven. Compared with contrasting methods, it achieves performance leadership in almost all indicators.