神经象征性人工智能对知识图进行推理:一项调查
概括
神经象征性人工智能 (AI) 将符号推理与深度学习相结合,用于知识图 (KG) 分析. 这项调查对KG神经符号推理方法进行了分类,提供了一个新的分类和未来的研究方向.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 知识表示 知识表示
背景情况:
- 神经象征性AI融合了象征性推理和深度学习,以提高能力.
- 知识图 (KG) 越来越多地用于复杂的数据表示.
- 现有的KG推理方法通常是基于规则或基于嵌入的,从而产生二分法.
研究的目的:
- 对知识图 (KG) 进行现有神经符号推理方法的调查.
- 提出一种新的分类学来分类这些神经符号KG方法.
- 确定局限性,并建议该领域的未来研究方向.
主要方法:
- 对KG应用的神经象征推理技术的文献综述.
- 开发一个新的分类分类法,分为三个类别:逻辑知情嵌入,具有逻辑约束的嵌入和规则学习方法.
- 方法的比较分析,包括表格概述和源代码链接.
主要成果:
- 关于神经符号KG推理方法的全面调查.
- 一个新的分类系统将方法分为三个主要类型.
- 确定当前神经符号KG方法的优点和弱点.
结论:
- 神经符号推理为KG提供了一条有希望的途径,可以弥合象征性和副象征性AI之间的差距.
- 拟议的分类学为理解和比较各种方法提供了一个结构化的框架.
- 需要进一步的研究才能充分实现神经符号AI在KG推理中的潜力,重点是可解释性和专家知识集成.
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