Merging element fuzzy cognitive maps

ABSTRACT

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.

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