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<p>With the development of event graphs, events, entities, and relationships are represented as interconnected nodes and edges, revealing the complex connections between events. Most existing event prediction methods are based on event pairs or event chains, and there is still room for improvement in handling event and its argument information, as well as mining the overall development context of events. Therefore, we introduce a novel event prediction method that improves accuracy by aggregating event and argument information. The method involves edge-aware and bidirectional graph propagation to understand the overall event development distribution, followed by the use of attention mechanisms to mine the event context and match it with event development to generate events and arguments synchronously. To demonstrate the effectiveness of the model, we conducted experiments on three publicly available IED datasets and achieved improvements of 1.5, 1.5, and 0.9% in the Event Type Matching (F1) metric, respectively.</p>

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EDCM-EA: event prediction based on event development context mining considering event arguments

  • 期刊论文

  • 发表日期：

    2025年3月4日

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  • Zhiheng Gong,
  • Huan Rong,
  • Zhongfeng Chen,
  • Yixiang Tang,
  • Victor S. Sheng

摘要

With the development of event graphs, events, entities, and relationships are represented as interconnected nodes and edges, revealing the complex connections between events. Most existing event prediction methods are based on event pairs or event chains, and there is still room for improvement in handling event and its argument information, as well as mining the overall development context of events. Therefore, we introduce a novel event prediction method that improves accuracy by aggregating event and argument information. The method involves edge-aware and bidirectional graph propagation to understand the overall event development distribution, followed by the use of attention mechanisms to mine the event context and match it with event development to generate events and arguments synchronously. To demonstrate the effectiveness of the model, we conducted experiments on three publicly available IED datasets and achieved improvements of 1.5, 1.5, and 0.9% in the Event Type Matching (F1) metric, respectively.