Inductive autoencoder for efficiently compressing RDF graphs

The flexible paradigm of the Resource Description Framework (RDF) has accelerated the rate at which raw data is published on the web. Therefore, the volume of generated RDF data has increased impressively in the last decade, which promotes the use of compression to manage and reduce the size of RDF datasets. Furthermore, researchers have recently tried to reconstruct convolution and pooling procedures to better suit the structure of graphs and make convolutional neural networks (CNNs) more applicable to RDF graph data. In this study, we propose the Multi Kernel Inductive RDF Graph Convolution Network (MKIR-GCN), which, rather than compressing nodes/edges independently, uses similarities between nodes and the structure of graphs to reduce the size of all nodes and edges simultaneously to efficiently compress the RDF graph. By considering the topology and similarity of a network's nodes, our proposed attention based on similarity for RDF graph pooling (ASGPool) picks the most informative and representative nodes. In dynamic graphs, our proposed MKIR-GCN layer may learn more generic node representations by focusing on diverse characteristics. Through extensive experimentation, we can conclude that the proposed approach significantly improves compression over the existing graph representation learning schemes and RDF graph compression schemes.