Multilabel event prediction refers to the prediction of whether multiple associated events will occur in the future,which requires the simultaneous prediction of multiple target events and comparing it with the conventional single-label event prediction.Because the issue of multi-label event contexts in various fields is yet to be addressed and studies regarding multi-label event prediction are few,this paper proposes a Multi-Label Event Prediction(MLEP)model based on Event-Evolution Graph(EEG).First,an EEG is constructed based on event chains.Subsequently,problem transformation is performed on the multi-label event-prediction problem to transform it into a single-label problem,followed by obtaining vector representations of all events using event-representation learning methods to encode multi-label events.Finally,a multi-label event prediction model is constructed using the Gated Graph Neural Network(GGNN)framework.The optimal subsequent events are matched based on their similarity to predict multi-label events.Experimental results on real datasets show that the proposed MLEP model can effectively predict multi-labeled events with a prediction accuracy of 65.58％,thus outperforming most existing benchmark models with an improvement level exceeding 4.94％.Results of ablation experiments show that better event-representation learning methods provide better event representations and multi-label event predictions.
multi labelEvent-Evolution Graph(EEG)event representation learningGated Graph Neural Network(GGNN)event-prediction
维普
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