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›› 2013,Vol. 28 ›› Issue (4): 689-719.doi: 10.1007/s11390-013-1369-6
所属专题： 不能删除； Artificial Intelligence and Pattern Recognition
• Special Section on Selected Paper from NPC 2011 • 上一篇 下一篇
Liang-Jun Zang1,2 (臧良俊), Cong Cao1,2 (曹聪), Ya-Nan Cao3 (曹亚男), Yu-Ming Wu1 (吴昱明) and Cun-Gen Cao1 (曹存根), Member, CCF
Liang-Jun Zang1,2 (臧良俊), Cong Cao1,2 (曹聪), Ya-Nan Cao3 (曹亚男), Yu-Ming Wu1 (吴昱明) and Cun-Gen CAO1 (曹存根), Member, CCF
为实现机器常识推理而构建大规模常识知识库是人工智能领域的一项十分具有挑战性的工作.为了克服知识获取瓶颈,研究人员设计和开发了许多获取常识知识的方法和系统.虽然研究人员已经提出了一些具体的常识推理任务以便评价和比较常识知识获取系统,我们将从以下几方面更加全面系统地比较常识知识获取系统：知识获取任务(回答从什么知识源获取什么常识知识),知识获取技术(回答用什么技术获取常识知识),知识获取评价(回答如何评价常识知识库).首先,本文给出一个常识知识获取系统的分类以及该领域存在的挑战.然后,本文详细描述了各种常识知识获取系统并进行了比较.最后,总结了该领域当前的进展状况并给出了一些重要的研究问题.
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