The evolution and development of breaking news events usually present regular patterns, leading to the happening of sequential events. Therefore, the analysis of such evolutionary patterns among events and prediction to breaking news events from free text is a valuable capability for decision support systems. Traditional systems tend to focus on contents distribution information but ignore the inherent regularity of evolutionary events. We introduce evolutionary event ontology knowledge (EEOK) structuring the evolutionary patterns in five different event domains, namely Explosion, Conflagration, Geological Hazard, Traffic Accident, Personal Injury. Based on EEOK which provides a representing general-purpose ontology knowledge, we also explore a framework with a pipeline semantic analysis procedure of event extraction, evolutionary event recognition, and event prediction. Since the evolutionary event under each event domain has different evolution patterns, our proposed event prediction model combines the event types to capture the inherent regulation of evolutionary events. Comparative analyses are presented to show the effectiveness of the proposed prediction model compared to other alternative methods.

中文翻译：

---

基于进化事件本体知识的事件预测

突发新闻事件的演变和发展通常呈现出规律性的规律，从而导致连续事件的发生。因此，分析事件之间的这种演化模式以及从自由文本中预测突发新闻事件对于决策支持系统来说是一项有价值的功能。传统系统往往关注内容分布信息而忽视演化事件的内在规律性。我们引入进化事件本体知识（EEOK），构建五个不同事件领域的进化模式，即爆炸、火灾、地质灾害、交通事故、人身伤害。基于EEOK提供的具有代表性的通用本体知识，我们还探索了一个具有事件提取、进化事件识别和事件预测的管道语义分析过程的框架。由于每个事件域下的进化事件具有不同的进化模式，我们提出的事件预测模型结合事件类型来捕捉进化事件的内在规律。比较分析显示了所提出的预测模型与其他替代方法相比的有效性。