# Sudden Event Prediction Based on Event Knowledge Graph
## 1 Introduction  
Event prediction is a knowledge inference problem that predicts the consequences or effects of an event based on existing information. The predecessor of the event prediction task was a kind of evaluation method proposed by [PERSON] and [PERSON] [1], which selected subsequent events from a pool of candidate events based on known event information. A series of subsequent studies on event prediction extended [PERSON] work [2, 3, 4, 5, 6]. One drawback of the studies that follows [PERSON] method, however, is that they relied on a long chain of events in order to make predictions. This chain structure performs well in predicting a single event but is not suitable for predicting multiple concurrent subsequent events. Furthermore, event prediction based on the chain structure ignores the effect of some features of the event itself on the event prediction. For example, subsequent events, including the injuries and deaths of passengers and rescue events, can be predicted based on the analysis of the features of traffic accidents. In recent years, more and more attention has been paid to the application of event prediction. Studies have been designed to solve event prediction difficulties in a specific application area or in a real-world situation. For example, estimating medications a patient will take next time based on their medical history [7], predicting civil unrest in 10 Latin American nations [8], and other fields [9, 10]. These studies, similar to [PERSON]', also have a clear event context. However, we have observed that some sudden events, such as chemical plant explosions, traffic accidents, and other accidents, frequently take placewithout evidence. Usually, the context of these accidents is implicit. Once these events happen, people can reduce the loss by predicting subsequent events and taking some actions. In this paper, we study a subsequent events prediction method for sudden events. Subsequent events prediction for sudden events usually faces the following challenges:  
1. The prediction result of a sudden event is often related to the features of the event itself, including the actions and objects involved, the condition of the event, and the scene (place and circumstances) of the event, therefore to classify a sudden event, such as natural disaster or terrorist attack, it is quite challenging to obtain effective feature information for the sudden event. 2. An effective event prediction method requires predicting all possible subsequent events of an initial event, and the features of an initial event determine whether it has one or several concurrent subsequent events. For example, the subsequent events of \"drowning\" may include \"first aid treatment\" and \"call for police\". If the person in the drowning event has been \"dead\", then the subsequent event will only be \"call for police\". In this paper, we provide a heterogeneous graph event prediction model (HGEP) based on an event knowledge graph to address the aforementioned challenges. An event knowledge graph can provide a priori knowledge for events prediction. For one thing, it can provide feature information for the analysis of initial events. Furthermore, it can provide scenario knowledge for the prediction of events. In other words, based on an event knowledge graph, the event type of the initial sudden event can be identified, and then the scenario model in the event knowledge graph can be used to predict the probability of its subsequent events. In the event knowledge graph, triples are used to describe the semantic relation between events and the relation between events and event arguments (action, object, place, time, and so on). Triples can also define the type (event class) of each event instance. On this basis, an event knowledge graph in a specific domain provided event scenario models to represent occurrence patterns of different events, which are described by a set of subevents and logical relations (including cause, sequence, concurrence, and so on) between them.