A Continual Learning Framework for Event Prediction with Temporal Knowledge Graphs

Events such as crises, public opinion issues, and social hotspots usually follow certain patterns. From a large amount of historical data, we can extract these patterns to predict future events. This valuable task can be viewed as the Temporal Knowledge Graph (TKG) inference problem, as TKGs are widely employed to sketch ongoing events. However, most of the traditional TKG inference methods mainly focus only on entity prediction and do not take into consideration the variable length of information summarized from events at different periods. To address these challenges, we propose a new collaborative entity- relation prediction method called Predicting the Future Without Forgetting (PFWF). PFWF introduces historical representation to deal with the issue posed by the variability of information length. We also treat the TKG prediction task as a continual learning problem that prevents training new models from scratch when new data are added, as real-world knowledge graphs are constantly evolving. We validated the effectiveness of PFWF on four public TKG datasets related to crisis events in offline and online continual learning settings.