Event evolutionary graph (EEG) reflects sequential and causal relations between events, which is of great value for event prediction. However, lacking event context in the EEG raises the problems of direction uncertainty and low accuracy when making predictions. In this article, we propose a conditional event evolutionary graph (CEEG) to deal with these problems. CEEG extends EEG with an additional four types of event context, including state, cause, sub‐type, and object. We first extract event context by matching the input with self‐adaptive semantic templates and generalize the context for each event. To identify the evolution direction, we treat it as a binary classification problem and calculate the event transition probability for each direction given the generalized context. Experimental results show that CEEG has a strong ability to generate better event evolutionary paths compared with NAR, EEM, and other non‐context‐based methods.

中文翻译：

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不确定的未来：使用条件事件演化图预测事件

事件进化图（EEG）反映了事件之间的顺序和因果关系，这对于事件预测具有重要的价值。然而，在进行脑电图预测时，脑电图缺乏事件背景会引起方向不确定性和准确性低的问题。在本文中，我们提出了一个条件事件演化图（CEEG）来解决这些问题。CEEG用其他四种事件上下文扩展了EEG，包括状态，原因，子类型和对象。我们首先通过将输入与自适应语义模板进行匹配来提取事件上下文，并概括每个事件的上下文。为了确定演化方向，我们将其视为二元分类问题，并在给定广义上下文的情况下针对每个方向计算事件转移概率。