Applying link prediction (LP) methods over knowledge graphs (KG) for tasks
such as causal event prediction presents an exciting opportunity. However,
typical LP models are ill-suited for this task as they are incapable of
performing inductive link prediction for new, unseen event entities and they
require retraining as knowledge is added or changed in the underlying KG. We
introduce a case-based reasoning model, EvCBR, to predict properties about new
consequent events based on similar cause-effect events present in the KG. EvCBR
uses statistical measures to identify similar events and performs path-based
predictions, requiring no training step. To generalize our methods beyond the
domain of event prediction, we frame our task as a 2-hop LP task, where the
first hop is a causal relation connecting a cause event to a new effect event
and the second hop is a property about the new event which we wish to predict.
The effectiveness of our method is demonstrated using a novel dataset of
newsworthy events with causal relations curated from Wikidata, where EvCBR
outperforms baselines including translational-distance-based, GNN-based, and
rule-based LP models.

应用连结预测、知识图谱在因果事件预测上的机遇，引入基于案例推理的 EvCBR 模型来预测新事件的属性，无需训练步骤，且在新 sworthy 事件与因果关系的数据集中表现优于基线模型。