Multi-Label Event-Prediction Model Based on Event-Evolution Graph

doi:10.19678/j.issn.1000-3428.0067498

Abstract

Abstract:

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.

Key words: multi label, Event-Evolution Graph(EEG), event representation learning, Gated Graph Neural Network(GGNN), event-prediction

摘要：

多标记事件预测是指预测多个相关联的事件是否会在未来发生, 相比传统单标记事件预测, 需要同时预测多个目标事件。现有的事件预测研究忽略各领域存在的多标记事件情境, 且对多标记事件预测研究较少。提出一种基于事件演化图的多标记事件预测模型(MLEP), 以实现基于事件演化图(EEG)的多标记事件预测研究模式。首先基于事件链构建事件演化图; 然后对多标记事件预测问题进行问题转换, 将多标记问题转化为单标记问题, 利用事件表示学习方法获取所有事件的向量表示, 对多标记事件进行编码; 最后采用门控图神经网络(GGNN)框架构建多标记事件预测模型, 根据相似度匹配出最优的后续事件, 实现多标记事件的预测。在真实数据集上的实验结果表明, MLEP模型可以有效地预测出多标记事件, 预测准确率达到了65.58%, 性能优于大多现有的基准模型, 提升幅度达到了4.94%以上。通过消融实验也证明了更好的事件表示学习方法对事件具有较好的表示效果, 提升多标记事件预测的性能。

关键词: 多标记, 事件演化图, 事件表示学习, 门控图神经网络, 事件预测

Huazhen WANG, Ze XU, Yue SUN, Bin QIU, Jian CHEN, Qiangbin QIU. Multi-Label Event-Prediction Model Based on Event-Evolution Graph[J]. Computer Engineering, 2024, 50(4): 132-140.

王华珍, 许泽, 孙悦, 丘斌, 陈坚, 邱强斌. 基于事件演化图的多标记事件预测模型[J]. 计算机工程, 2024, 50(4): 132-140.

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Figures/Tables 11

Fig.1 Framework of MLEP model

Fig.2 Example of event evolution graph

Fig.3 Accuracy comparison of different models

Fig.4 Ablation experiment results

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