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多模式知识图通过交叉模式交互完成,增强相似性和拥抱差异.

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    此摘要是机器生成的。

    本研究引入了一个新的多式联网知识图完成 (MMKGC) 框架,有效地利用数据类型之间的相似性和差异. 通过CISEDE方法,在知识图表增强方面取得了最先进的结果.

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    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:

    • 多模式知识图完成 (MMKGC) 整合了各种数据,用于增强知识图.
    • 现有的方法往往忽略了模式之间的互补特征或相似之处.
    • 在MMKGC中,有效地关联异构的模式仍然是一个重大挑战.

    研究的目的:

    • 提出一个新的MMKGC框架,利用多式联运实体之间的相似性和差异.
    • 引入一个设计用于有效MMKGC的跨模式交互机制.
    • 通过多式联运数据集成,提高知识图表应用的精度和广度.

    主要方法:

    • 开发了一种跨模式交互,具有增强相似性和拥抱差异的框架 (CISEDE).
    • 采用了利用多头注意力的交叉模式交互机制.
    • 集成关系引导的融合来解码和合并MMKGC的 modal triples.

    主要成果:

    • 在CISEDE框架中,在基准数据集 (FB15k-237,WN9,WN18RR) 上表现出了最先进的性能.
    • 拟议的方法有效地利用不同数据模式的共享和独特信息.
    • 在知识图表完成准确度方面取得了显著的改进.

    结论:

    • 通过拥抱多式联运数据异质性,CISEDE框架为MMKGC提供了一种强有力的方法.
    • 交叉模式交互机制是提高MMKGC性能的关键.
    • 这项工作通过提供一个更全面的方法来完成多式联运知识图,从而推动了该领域的发展.