一项关于知识图的调查,对图形类型的推理:静态,动态和多模式
概括
本调查提供了第一个关于知识图推理 (KGR) 模型的全面概述,涵盖静态,时间和多模式方法. 它根据图形类型和技术对模型进行分类,总结性能和数据集,以指导未来的研究.
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:
- 知识图推理 (KGR) 使用逻辑规则从现有数据中推断出新的事实.
- KGR 增强了人工智能应用程序,如回答问题和推系统.
- 现有的KGR模型被分为静态,时间和多模式类型.
研究的目的:
- 提供第一个关于知识图推理 (KGR) 模型的全面调查.
- 系统地审查KGR模型从静态到时间和多模式的方法.
- 解决KGR.中缺乏全面的摘要和开源存储库的问题.
主要方法:
- 基于双层分类学审查的模型:图形类型 (静态,时间,多模式) 和基层 (技术,场景).
- 总结模型性能和相关数据集.
- 识别KGR.GR当前的挑战和未来的研究机会.
主要成果:
- 将KGR模型分为静态,时间和多模式的分类.
- 详细审查每个类别内的技术和场景.
- 性能基准和数据集的呈现,用于比较分析.
结论:
- 该调查提供了对KGR景观的结构化概述,将静态,时间和多式模式连接起来.
- 它通过总结现有工作,数据集和绩效指标,为研究人员提供了宝贵的资源.
- 该研究强调了关键的挑战和机遇,为知识图推理的进步铺平了道路.
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