# Sudden Event Prediction Based on Event Knowledge Graph
## 1 Introduction
In recent years, the research on reasoning based on knowledge graphs has become an important area of knowledge reasoning. It mainly revolves around relational reasoning; by understanding the characteristic information of entities, relations, and knowledge graph structures, unknown facts or relations are inferred based on the existing facts or relations of the knowledge graph. Reasoning based on the knowledge graph can primarily assist in reasoning about new facts, new relations, new rules, etc. Similarly, we use the constructed event knowledge graph, and with the help of the concept of knowledge graph reasoning, it is possible to reason about new events from known event features, that is, to predict the occurrence of new events. Since the research on event knowledge graphs has just started, reasoning and event prediction based on event knowledge graphs are still in their initial stages. Extending the reasoning research on knowledge graphs to event knowledge graphs is also the motivation of this paper. In previous studies, representation learning on knowledge graphs [11; 12; 13; 14] mapped sparse graphs into dense vector spaces such that entities and relations with a high semantic similarity had similar embedding vectors. The corresponding tail nodes were then inferred by understanding the semantic information of the head nodes and relations in the triplet. Then, theoretically, representation learning on the event knowledge graph could infer the corresponding subsequent events by understanding the semantic information of known events and relations. Therefore, we design a novel event representation learning method. Using this approach, the features of an initial event and its concurrent events (if they exist) can be learned from the event knowledge graph, and then its subsequent events can be predicted. We store the events that have occurred and their subsequent events in the event knowledge graph. The logic of subsequent events occurring is then captured by extracting the features of the initial event and its concurrent events. When a new event occurs, the machine quickly makes a reliable prediction based on past events with similar features. We use the method in [15] to construct a knowledge graph of events in the field of transportation for the experiments. An \"event\" is composed of a trigger and four types of arguments: time, place, object, and participants. Events and arguments are connected to each other by different argumentative relations. Through a concurrent relation, concurrent events are linked to one another. The concurrent events and their arguments form a heterogeneous graph. We learn the representation of each such heterogeneous graph to obtain event scenario representations. Specifically, we use a heterogeneous graph transformer (HGT) [16] to fuse the entity embedding of concurrent events in an inductive manner. It differs from the common graph attention network by adding a reverse edge to each directional edge in a graph, which facilitates the flow of information through the graph. We use a multilabel classification approach to project event scenario representations onto the result set to handle the presence of events in subsequent events. To determine whether a specific event type would show up in the results, we create scoring functions. We summarize our main contributions as follows:  
1. An event representation method based on a HGT is proposed, which efficiently obtains an embedding containing event semantic information from sparse heterogeneous graphs composed of event arguments. 2. An event prediction method based on the event knowledge graph is proposed. The method understands the semantic relation between events in the knowledge graph through representation learning and predicts subsequent events according to the logical relation of events in the event scenario model. 3. An event knowledge graph in the domain of transportation is constructed. The rest of the paper is organized as follows. Section 2 describes the related research on event prediction.