摘要
认知图(Cognitive Map,CM)是一种新型知识管理方法,具有直观的知识表达能力、强大的基于数字矩阵的推理机制等优点.但是,要充分展示CM的这些优点,首先必须获得正确的CM图.传统的获取CM图的方法(如问卷法、头脑风暴法、样本学习法等)常常借助专家经验,因其过分强调主观因素而忽视客观数据资源,容易导致信息丢失现象.为此,该文提出了一种基于客观数据资源来挖掘CM图的新方法,它主要由数据库初始化技术、权重系数优化方法、CM图的简化策略等组成.实验结果表明:该方法能挖掘出构成CM图的所有节点间的所有关系,并可针对这些关系间的重要程度对CM图作适当简化;与传统的方法相比,该方法所挖掘出的CM图具有更为丰富的信息.
Cognitive Map （CM） is a new kind of method of knowledge management, which has many advantages such as. it is relative easy to use for representing structured knowledge, the inference mechanism can be computed by numeric matrix operation, etc. However, in order to exhibit these advantages about CM, the first step is that the corrected CMs must be obtained. Traditional approaches for obtaining the CMs, including questionnaire method, brainstorming method, and sample learning method, always rely on experience of domain experts. Because these methods put much emphasis on the subjective factors and neglect the objective data resources, they always lose some information. Therefore, this paper proposes a new methodology of mining the CMs based on data resources, which mainly includes database preprocessing technology, optimization algorithm for weight coefficients, and simplification strategy for CMs. The experimental results show that. the new method can mine all possible relationships among all nodes to form the CMs, and can also simplify it according to the significant degree of those relationships; the CMs mined by the new method has more information than the CMs obtained by traditional approaches.
出处
《计算机学报》
EI
CSCD
北大核心
2007年第8期1446-1454,共9页
Chinese Journal of Computers
基金
重庆市自然科学基金(2005BB2083)资助~~
关键词
认知图
数据挖掘
神经元网络
数据库
学习算法
cognitive map
data mining
neural network
database
learning arithmetic
