Knowledge Graph Embedding Based on Graph Neural Network

The representation of semantic information pertaining to the real world has been active research for some time now. Among the available methods, knowledge graphs have emerged as a widely accepted approach. Meanwhile, graph neural networks (GNNs) have demonstrated excellent performance in embedding graph-based information. Given the natural graph structure of knowledge graphs, employing GNNs to embed them is expected to yield a more interpretable and trustworthy representation of the learned knowledge. In this paper, we propose three customized GNNs for different scenarios of knowledge graph representation, including traditional, multimodal, and uncertain knowledge graphs. In the traditional knowledge graph scenario, we present a graph self-supervised learning method, named deep relation graph infomax (DRGI), which incorporates both the complete graph structure information and semantic information. In the multimodal knowledge graph scenario, we introduce a novel network, named hyper-node relational graph attention network (HRGAT), which combines different modal information with graph structure information for a more precise representation of multimodal knowledge graphs. In the uncertain knowledge graph scenario, we define a novel message-passing paradigm with box embedding, named box graph neural network (BGNN). BGNN leverages both the graph structure information of uncertain knowledge graphs and the probabilistic semantics of box embedding. To validate the effectiveness of our proposed methods, we conduct a series of experiments and report the results. We also discuss possible future work in GNN-based knowledge graph embedding.