在开放域库中基于图形的事件模式诱导
Keyu Yan1,2, Wei Liu1,2, Shaorong Xie1
1School of Computer Engineering and Science, Shanghai University, Shanghai, China.
PeerJ. Computer science
|December 16, 2024
概括
本研究介绍了一个基于图形的事件方案诱导模型,通过结合结构图的特征来改进事件集群和方案生成. 新方法提高了聚类的有效性,并产生了高度可接受的事件方案.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 知识表示 知识表示
背景情况:
- 传统的事件模式诱导方法严重依赖文本特征,限制了它们的集群功能.
- 用于表示事件和世界知识的正式语言对于人工智能应用至关重要.
研究的目的:
- 提出一个新的基于图形的事件模式诱导模型.
- 通过从构造图中提取结构特征来增强事件聚类.
- 创建事件方案,使用灵感来自于上下文学习的方法.
主要方法:
- 构建了一个图表来提取事件模式诱导的结构特征.
- 开发了一种以上下文学习为灵感的方法,用于概念化用于模式生成的集群.
- 使用调整的兰德指数 (ARI),规范化的相互信息 (NMI),准确性 (ACC) 和BCubed-F1指标评估集群性能.
主要成果:
- 与现有方法相比,基于图形的事件模式诱导模型显示了较好的集群效率.
- 生成的事件方案实现了高度可接受的比率,表明了实际的实用性.
- 该模型成功地将结构图的特征集成到事件模式诱导过程中.
结论:
- 拟议的基于图形的事件模式诱导模型在该领域取得了重大进展.
- 从图表中整合结构信息可以提高事件模式诱导的能力.
- 该方法为知识表示和事件建模提供了一个有前途的方法.
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