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Improving similarity measures of relatedness proximity: Toward augmented concept maps

机译:改善相关性接近度的相似性度量:向增强概念图

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摘要

Decision makers relying on web search engines in concept mapping for decision support are confronted with limitations inherent in similarity measures of relatedness proximity between concept pairs. To cope with this challenge, this paper presents research model for augmenting concept maps on the basis of a novel method of co-word analysis that utilizes webometrics web counts for improving similarity measures. Technology assessment serves as a use case to demonstrate and validate our approach for a spectrum of information technologies. Results show that the yielded technology assessments are highly correlated with subjective expert assessments (n = 136; r > 0.879), suggesting that it is safe to generalize the research model to other applications. The contribution of this work is emphasized by the current growing attention to big data. (C) 2015 Elsevier Ltd. All rights reserved.
机译:在概念映射中依靠网络搜索引擎提供决策支持的决策者面临着概念对之间相似性接近度的相似性度量固有的局限性。为了应对这一挑战,本文提出了一种基于概念的新词增强模型的研究模型,该词利用Webometrics Web计数来改进相似性度量,是一种新颖的词分析方法。技术评估可作为一个用例,以展示和验证我们针对各种信息技术的方法。结果表明,所产生的技术评估与主观专家评估高度相关(n = 136; r> 0.879),这表明将研究模型推广到其他应用是安全的。当前对大数据的日益关注强调了这项工作的贡献。 (C)2015 Elsevier Ltd.保留所有权利。

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