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基于知识图的可解释人工智能:系统性审查

Enayat Rajabi1, Kobra Etminani2

  • 1Shannon School of Business, Cape Breton University, Canada.

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PubMed
概括
此摘要是机器生成的。

知识图 (KG) 通过提取特征和关系来增强可解释的AI (XAI),特别是在医疗保健中. 本审查将XAI系统中的KG应用分类.

关键词:
知识图表知识图表人工智能的人工智能是人工智能.可以解释的人工智能AI系统性审查 系统性审查

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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
  • 信息科学 信息科学 信息科学

背景情况:

  • 知识图 (KGs) 通过层次结构提供数据的语义表示.
  • 智能化工具有助于整合各种信息来源.
  • 可解释和可解释的人工智能 (AI) 系统从结构化的知识表示中受益.

研究的目的:

  • 系统地审查最近关于KG在可解释AI (XAI) 中应用的文献.
  • 在XAI模型的不同阶段 (模型前,模型内,模型后) 中对KG的利用进行分类.
  • 确定使用KG用于AI可解释性的普遍领域和方法.

主要方法:

  • 对最近的出版物进行了系统的审查.
  • 设计了一个框架,将KG的使用分类为特征提取,关系提取,KG构建和KG推理.
  • 在XAI中,每个类别的应用阶段 (模型前,模型内,模型后) 都被确定.

主要成果:

  • 在预模型XAI中,KG主要用于特征和关系提取.
  • 在后模型XAI应用中,KG推理和推理具有重要意义.
  • 在医疗保健领域,KG在解释XAI模型方面得到了显著的应用.

结论:

  • 知识图是增强人工智能系统可解释性的有价值工具.
  • 特定的KG应用,如特征提取和推理,是提高AI可解释性的关键.
  • 未来的研究可以进一步探索KG在XAI的所有阶段的整合,特别是在医疗保健等专业领域.