GCKG: Novel Gated Convolutional embedding model for Knowledge Graphs. (1st December 2022) - Yao, Shuanglong; Pi, Dechang; Chen, Junfu; Xu, Yue
Abstract: With the aim of constructing a low-dimensional representation space, knowledge graph embedding has gradually become a hot spot in various information retrieval and machine learning tasks. However, most existing knowledge graphs suffer from incompleteness due to the fact that it is unable to collect a complete world knowledge. For knowledge graph completion (KGC), this paper introduces a novel embedding method, named gated Convolution for Knowledge Graph (GCKG), to automatically predict missing links for large-scale Knowledge Graphs. The model GCKG advances the existing models by employing a gated mechanism on convolutional neural network, so that it can capture global relationships and attribute characteristics between entities and relations in knowledge graphs. First, three embedding vectors of each triple are combined into three different matrices. Then, the matrices are fed to three convolutional layers. Finally, the network generates three different maps which are controlled to transmit the information by a gated mechanism. By performing extensive and comprehensive experiments, we evaluate GCKG with two benchmark datasets, FB15k-237 and WN18RR. Experiments show that GCKG achieves substantial improvements against state-of-the-art baselines, and it is respectively improved about 37% in MRR and 4% in Hits@10 on FB15k-237 database compared with CapsE. Highlights: Gated Mechanism for KGs is proposed considering characteristic of a triple. Gated Mechanism is inspired by the chain rule and the Markov assumption. A Gated convolution Embedding for KGs. The model achieved a good performance on benchmark datasets.