SGKGE:通过互补的潜伏表示方式嵌入语义引导的知识图
IEEE transactions on neural networks and learning systems
|April 23, 2025
概括
本研究介绍了语义指导知识图嵌入 (SGKGE) 以通过捕捉各种语义来改善实体表示. SGKGE提高了知识图完成准确性和下游任务性能.
科学领域:
- 人工智能的人工智能
- 数据科学数据科学数据科学
- 计算机科学 计算机科学
背景情况:
- 完成知识图 (KG) 对于从复杂的数据集中提取信息至关重要.
- 现有的方法与实体多重性斗争,导致不准确的表示.
- 这种限制阻碍了KG完成任务的有效性.
研究的目的:
- 提出一种新的方法,即语义引导的KG嵌入 (SGKGE),用于在KG中准确地表示实体.
- 通过捕捉细粒度的语义来解决实体多重性的挑战.
- 通过改进的KG嵌入来提高下游应用程序的性能.
主要方法:
- SGKGE使用可学习曲率的超标多元体来实现整体实体语义.
- 一个几何注意力融合模块有助于有效推理.
- 卡尔特产品里曼的多元体和语义交互模块捕捉了细粒度的语义.
主要成果:
- SGKGE在六个KG完成基准上取得了最先进的表现.
- 该模型在表示各种实体语义方面表现出卓越的准确性.
- 改进的实体表示导致下游应用程序性能提高.
结论:
- SGKGE有效地解决了知识图中的实体多态.
- 拟议的方法在KG完成方面提供了显著的进步.
- SGKGE为各种应用提供了更准确的实体语义.
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