In the field of information management, effective event intelligence management is crucial for its development. With the continuous evolution of events, predicting future events has become a key task in information management. Event Prediction aims to predict upcoming events based on given contextual information. This requires modeling events and their relationships in the context to infer the structure of future events. However, the existing event prediction methods ignore that the event graph schema based on core events can provide more knowledge about history and future for event prediction through induction and deduction, so as to achieve accurate event prediction. In addressing this issue, we directed our focus towards Event Schema Induction. Inspired by it, we propose the Pred-ID model, designed to build event evolutionary pattern through Inductive Event Graph Generation, Deductive Event Graph Expansion, and Graph Fusion for Event Prediction. Specifically, in the Inductive Event Graph Generation phase, Pred-ID extracts the event core subgraph and event developmental trends from the instance event graph, learning the global structure and uncovering the main processes of event development. Then, in the Deductive Event Graph Expansion phase, by expanding future event node and stretching the main processes of event development into future directions, Pred-ID obtains deductive results, so as to construct the event evolutionary pattern. Finally, in the Graph Fusion for Event Prediction phase, aligning and merging the event evolutionary pattern with the instance event graph enables collaborative prediction of future events. The experimental results indicate that our proposed Pred-ID achieves optimal performance in event evolutionary pattern generation and event prediction tasks.

中文翻译：

---

Pred-ID： 基于图归纳和演绎的事件类型模式挖掘的未来事件预测


在信息管理领域，有效的事件智能管理对其发展至关重要。随着事件的不断发展，预测未来事件已成为信息管理中的一项关键任务。事件预测 旨在根据给定的上下文信息预测即将发生的事件。这需要在上下文中对事件及其关系进行建模，以推断未来事件的结构。然而，现有的事件预测方法忽略了基于核心事件的事件图架构可以通过归纳和演绎为事件预测提供更多关于历史和未来的知识，从而实现准确的事件预测。为了解决这个问题，我们将重点放在了 Event Schema Induction 上。受此启发，我们提出了 Pred-ID 模型，旨在通过归纳事件图生成、演绎事件图扩展和用于事件预测的图融合来构建事件演化模式。具体来说，在归纳事件图生成阶段，Pred-ID 从实例事件图中提取事件核心子图和事件发展趋势，学习全局结构并揭示事件发展的主要过程。然后，在演绎事件图扩展阶段，通过扩展未来事件节点，将事件发展的主要过程延伸到未来方向，获得演绎结果，从而构建事件演化模式。最后，在 Graph Fusion for Event Prediction 阶段，将事件演变模式与实例事件图对齐和合并，可以协作预测未来事件。 实验结果表明，我们提出的 Pred-ID 在事件进化模式生成和事件预测任务中取得了最佳性能。