双视图转换器网络用于多跳转的知识图推理
Congcong Sun1, Jianrui Chen2, Zhongshi Shao1
1School of Computer Science, Shaanxi Normal University, Xi'an, 710119, China.
概括
双视图转换器网络 (DV4KGR) 通过共同学习结构化和串行视图来增强多跳的知识图推理. 这种方法提高了从复杂数据集中发现缺失信息的效率和准确性.
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
- 知识表示 知识表示
背景情况:
- 知识图 (KG) 经常存在不完整性,需要多跳式推理来推断缺少的信息.
- 现有的多跳式推理方法面临的挑战是培训效率低下 (强化学习) 或结构化知识丧失 (基于序列的方法).
研究的目的:
- 提出一种新的方法,双视图转换器网络 (DV4KGR),用于有效的多节点知识图推理.
- 为了提高推理能力,使结构化和序列化的观点能够共同学习.
主要方法:
- DV4KGR使用结构化视图来表示全局关系,并使用序列化视图来推理语义.
- 采用监督的对比学习来有效地表示一对多的关系.
- 将结构化知识与规则诱导相结合,用于行动平滑,以减轻过度拟合.
主要成果:
- 与最先进的基线相比,DV4KGR在四个基准数据集中表现出更好的表现.
- 联合学习方法提高了捕捉和表现复杂关系的能力.
结论:
- DV4KGR提供了一种更有效,更易于解释的方法,用于多跳的知识图推理.
- 双视图策略有效地解决了先前方法的局限性,改善了整体知识图的完成和理解.
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