Merging element fuzzy cognitive maps
Merging element fuzzy cognitive maps
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DOI：
10.1145/1516241.1516302
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发表时间：
2009-02
期刊：
影响因子：
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通讯作者：
Xiangfeng Luo;Yi Du-;Fangfang Liu;Zhian Yu;Weimin Xu
中科院分区：
文献类型：
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作者：
Xiangfeng Luo;Yi Du-;Fangfang Liu;Zhian Yu;Weimin Xu
通过元素模糊认知图（E-FCM）中的权重计算不同主题中关键词的重要度和差异度。通过引入逻辑“与”运算，对大量E-FCM之间的相似性进行粗略评估，形成相似的E-FCM社区。基于权值计算和逻辑“与”运算，提出了一种基于E-FCM的知识融合算法，以检测属于同一相似社区的原始E-FCM中隐藏的噪声和冗余信息。采用Shannon熵作为衡量融合过程中文本信息损失的指标。合并算法和指示器提供了文本知识的简洁表示，可用于基于理解的文本自动分类和聚类，以及相关的知识聚合和集成。该算法在e-Science知识网格和e-Learning等领域具有很好的应用前景。
Importance degree and difference degree of keywords in different topics have been computed by the weights in Element Fuzzy Cognitive Maps (E-FCMs). Logic "and" operation is introduced to roughly evaluate the similarities between mass E-FCMs in order to form similar communities of E-FCMs. Based on the weights computing and the logic "and" operation, an E-FCMs-based knowledge merging algorithm is proposed to inspect the noisy and the redundancy information hidden in the original E-FCMs belonging to one similar community. Shannon entropy is employed as an indicator to measure the loss of textual information during the merging process of E-FCMs. The merging algorithm and the indicator provide a concise representation of text knowledge that can be used in understanding-based text automatic classification and clustering, as well as relevant knowledge aggregation and integration. The proposed algorithm has very good application prospects in the fields of e-Science knowledge gird and e-Learning.