GN-GCN: Grid neighborhood-based graph convolutional network for spatio-temporal knowledge graph reasoning

Abstract

Owing to the difficulty of utilizing hidden spatio-temporal information, spatio-temporal knowledge graph (KG) reasoning tasks in real geographic environments have issues of low accuracy and poor interpretability. This paper proposes a grid neighborhood-based graph convolutional network (GN-GCN) for spatio-temporal KG reasoning. Based on the discretized process of encoding spatio-temporal data through the GeoSOT global grid model, the GN-GCN consists of three parts: a static graph neural network, a neighborhood grid calculation, and a time evolution unit, which can learn semantic knowledge, spatial knowledge, and temporal knowledge, respectively. The GN-GCN can also improve the training accuracy and efficiency of the model through the multiscale aggregation characteristic of GeoSOT and can visualize different probabilities in a spatio-temporal intentional probabilistic grid map. Compared with other existing models (RE-GCN, CyGNet, RE-NET, etc.), the mean reciprocal rank (MRR) of GN-GCN reaches 48.33 and 54.06 in spatio-temporal entity and relation prediction tasks, increased by 6.32/18.16% and 6.64/15.67% respectively, which achieves state-of-the-art (SOTA) results in spatio-temporal reasoning. The source code of the project is available at https://doi.org/10.18170/DVN/UIS4VC.