# Sudden Event Prediction Based on Event Knowledge Graph
## 2 Related Work  
Most existing event prediction studies were originally designed to deal with specific application domains, such as predicting adverse drug reaction events in the medical field [17]. Classical machine learning algorithms were used in the early stages of these studies. In recent years, graph-based algorithms have frequently been used to solve the problem of event prediction. Traditional event prediction methods use machine learning methods such as classification, clustering, and frequent-pattern mining to predict whether an event will occur based on statistical data. [PERSON] et al. [18] designed a frequent-episode mining framework to discover a continuous sequence of events in an event stream based on a discrete hidden Markov model. Furthermore, based on this, the framework was extended to support multiple sequences in a dataset [19]. [PERSON] et al. [20] further ranked the prediction results by confidence and output the top k events as the final result. [PERSON] et al. [21] used logistic regression and a classification tree model to predict customer churn events. [PERSON] et al. [22] proposed a novel logit leaf model to predict customer churn events by fusing a logistic regression and decision tree models. [PERSON] et al. [23] fused data sources such as Twitter, blogs, and news, using logistic regression models with Lasso to select sparse features in order to predict the probability of occurrence of civil unrest events. [PERSON] et al. [24] used the HAC hierarchical clustering algorithm to aggregate similar events in an abstract event-centric manner and extract the generalized causal relation between two events to predict events in a rule-generating manner. [PERSON] et al. [2] predicted events in the form of event chains, using a composite neural network that predicted whether two events would appear in the same event chain by learning a vector representation of event predicates and parameter nouns. [PERSON] et al. [25] proposed a script learning model using LSTM, where a script was a sequence of prototypical events represented by multiple predicates as a way to predict future events. [PERSON] et al. [6] also used LSTM for scriped event learning, modeling pairs of events based on the relation between the event sequence information and event pairs. Compared with [PERSON], [PERSON]'s method had better performance on the event-pair model. [PERSON] et al. [8] collected data in numerous media for their analysis and used logistic regression and LASSO to select sparse feature sets and built separate models for different countries to predict their probability of civil unrest. [PERSON] et al. [26] utilized a Weibull distribution to model volcano eruption events. With the growing popularity of graph neural network [27] in recent years, there have been many studies on constructing event graphs from events or event elements and then using graph-based algorithms and techniques to solve event prediction problems. [PERSON] et al. proposed a scaled graph neural network (SGNN) [4] to predict script events. [PERSON] used triples <subject, predicate, object> to represent events and constructed a narrative-based event evolution graph using causal chains of events to model the event prediction as choosing the correct answer among one correct event and four random events. [PERSON] et al. proposed a narrative graph neural network [5] to predict script events. Differing from [PERSON], [PERSON] used a combination of information from event chains, event graphs, and event pairs, and proposed a prediction task to predict intermediate events based on previous and subsequent events. [PERSON] et al. [28] proposed a method for predicting events with an autoregressive graph generation model, which constructed event instance graphs based on a time axis, where each event instance graph contained a complex event involving all entities and predicting the event type and possible event parameters using pattern induction. [PERSON] et al.