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摘要翻译：
知识在人类和人工智能中起着核心作用。知识的关键特征之一是其结构化组织。知识可以而且应该以多层次、多视图的方式呈现，以满足人们不同粒度层次、不同视角的需求。本文从粒度计算的角度出发，通过粒度知识结构(GKS)给出了对知识的多层次、多视图的理解。研究了粒度知识结构的表示、构造粒度知识结构的操作以及如何使用粒度知识结构。作为说明，我们提供了一些例子，通过分析的结果，继续论文。结果表明，粒度知识结构可以帮助用户从集合理论、逻辑和可视化的角度更好地理解知识源。人们可以考虑使用它们来满足特定的需求或解决某些类型的问题。
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英文标题：
《On Granular Knowledge Structures》
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作者：
Yi Zeng, Ning Zhong
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最新提交年份：
2008
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Computer Science 计算机科学
二级分类：Digital Libraries 数字图书馆
分类描述：Covers all aspects of the digital library design and document and text creation. Note that there will be some overlap with Information Retrieval (which is a separate subject area). Roughly includes material in ACM Subject Classes H.3.5, H.3.6, H.3.7, I.7.
涵盖了数字图书馆设计和文献及文本创作的各个方面。注意，与信息检索（这是一个单独的主题领域）会有一些重叠。大致包括ACM课程H.3.5、H.3.6、H.3.7、I.7中的材料。
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英文摘要：
Knowledge plays a central role in human and artificial intelligence. One of the key characteristics of knowledge is its structured organization. Knowledge can be and should be presented in multiple levels and multiple views to meet people's needs in different levels of granularities and from different perspectives. In this paper, we stand on the view point of granular computing and provide our understanding on multi-level and multi-view of knowledge through granular knowledge structures (GKS). Representation of granular knowledge structures, operations for building granular knowledge structures and how to use them are investigated. As an illustration, we provide some examples through results from an analysis of proceeding papers. Results show that granular knowledge structures could help users get better understanding of the knowledge source from set theoretical, logical and visual point of views. One may consider using them to meet specific needs or solve certain kinds of problems.
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PDF链接：
https://arxiv.org/pdf/0810.4668
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