Knowledge graph embedding based on dynamic adaptive atrous convolution and attention mechanism for link prediction

Abstract

Knowledge graph embedding (KGE) is essential for various applications, particularly in link prediction and other downstream tasks. While existing convolutional neural network (CNN)-based methods have been effective, they face challenges in comprehensively capturing local and global contextual information from triplets. To address this challenge, we propose a KGE method based on a dynamic adaptive atrous convolution and attention mechanism (DTAE). In this study, we innovatively construct adaptive convolutional kernels derived from relation representations. Employing a parallel strategy, we apply a multidimensional attention mechanism across kernel space. This configuration allows our kernels to dynamically adapt and learn from different entity representations, ensuring more nuanced feature extraction. Moreover, a dynamic adaptive atrous convolutional network has been developed. This network leverages atrous convolution, efficiently broadening the receptive field to encompass global information while preserving essential details during the aggregation process. In addition, we enhance feature information by integrating a channel attention mechanism, further refining our model’s ability to discern and process relevant data. Via comprehensive experiments on five standard link prediction benchmark datasets, DTAE demonstrates superior performance over existing models. Compared to the best recent state-of-the-art models over the last three years, we achieve improvements across various metrics ranging from 0.4% to 14.4%.