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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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知识图:机遇和挑战

Ciyuan Peng1, Feng Xia2, Mehdi Naseriparsa3

  • 1Institute of Innovation, Science and Sustainability, Federation University Australia, Ballarat, 3353 VIC Australia.

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概括
此摘要是机器生成的。

知识图为人工智能 (AI) 系统组织真实世界的信息. 本文调查了知识图表开发中的AI机会和技术挑战.

关键词:
人工智能的人工智能是人工智能.图形嵌入式嵌入式图表学习学习图表学习知识工程知识工程知识工程知识图是知识图.

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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
  • 知识表示 知识表示

背景情况:

  • 人工智能 (AI) 和大数据的快速扩张需要有效的知识组织.
  • 知识图,作为图形数据的一种形式,对于表示现实世界的信息和复杂的关系至关重要.
  • 由于知识图的表示力,学术界和工业界对知识图的兴趣越来越大.

研究的目的:

  • 为知识领域提供系统的概述.
  • 探索知识图所带来的机会,特别是在人工智能系统和各种应用领域.
  • 识别和讨论知识图的研究和开发中的重大技术挑战.

主要方法:

  • 系统的文献审查,专注于知识图表.
  • 对机遇的分析,分为AI系统集成和应用领域.
  • 讨论关键的技术挑战,包括嵌入,收购,完成,合并和推理.

主要成果:

  • 知识图为构建先进的人工智能系统提供了重大机会.
  • 对知识图表技术而言,各种各样的应用领域正在出现.
  • 几个关键的技术挑战阻碍了充分实现知识图的潜力.

结论:

  • 在AI时代,知识图对于管理和利用大量信息至关重要.
  • 解决知识图嵌入,获取,完成,融合和推理方面的挑战对于未来的进步至关重要.
  • 本次调查旨在指导未来在知识图的动态领域的研究和开发.