Pred-ID: Future event prediction based on event type schema mining by graph induction and deduction

Abstract

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.