Abstract
Importance degree and difference degree of keywords in different topics have been measured by the associated weights in Element Fuzzy Cognitive Maps (E-FCMs) which can represent textual knowledge effectively . Logic “ and” operation is introduced to roughly evaluate the similarities between the mass E-FCMs in order to form the similar sets of textual knowledge. Based on the associated weight measuring and the logic 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 set. A formula obtained through F-measure 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 ual knowledge that can be used in understanding-based automatic text classification and clustering, as well as relevant knowledge aggregation and integration. The proposed algorithm will ha ve very good application prospects in future .
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