Abstract: In dealing with practical issues, it is generally possible to obtain accurate understanding through the refinement of knowledge particles. On the contrary, the problem can be simplified by coarse knowledge particles. This paper introduces the concepts of knowledge resolution and information entropy to study the resolution ability of knowledge in lattice-valued information systems. By some properties, it can be proved that both of them gradually become lager with the refinement of knowledge particles, smaller with the coarsening of knowledge particles. An instance result shows that the knowledge resolution and information entropy are greater, the ability of knowledge resolution is stronger. Furthermore, the relationship between knowledge resolution and information entropy, which can be used to reflect the particles of knowledge and the degree of classification in lattice-valued information systems, states that they are the same. In addition, the influence of knowledge particles on knowledge is reflected from the side. These conclusions provide theoretical basis for the knowledge discovery of lattice-valued information systems.

Key words: lattice-valued information system, knowledge resolution, information entropy, knowledge particles

摘要： 在处理实际问题时，一般会尽可能通过细化知识颗粒获得精确的认识，相反地，经过粗化知识颗粒来使问题得以简单化。在格值信息系统中引入知识分辨度和信息熵的概念来研究知识的分辨能力。通过研究它们的有关性质，证明了二者随着知识颗粒的细化逐渐变大，粗化而逐渐变小的结论。通过对实例的研究得到知识的分辨度和信息熵越大表明知识的分辨能力越强，知识的分辨度和信息熵越小表明知识的分辨能力越弱。进一步通过它们之间的关系发现知识的分辨度与信息熵是相同的，因此它们都可以用来反映格值信息系统中知识的颗粒和分类程度，都从侧面反映了格值信息系统中知识颗粒对知识的影响。这些结论为格值信息系统的知识发现奠定了一定的理论基础。

关键词: 格值信息系统, 知识分辨度, 信息熵, 知识颗粒