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Normalized Similarity Index: An adjusted index to prioritize article citations

机译:归一化相似度指数:一种调整后的指数,用于对文章引用进行优先排序

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摘要

One of the main applications of citation is to find articles that are relevant to a particular article. However, not all citations are equally relevant to the target article. This paper presents an approach to identify the most relevant citation(s). To this end, the Normalized Similarity Index (NS1) is proposed to quantify the similarity between the source and target of a citation base on the co-citations and references shared by them. To validate the method, NSI was calculated for five citation networks and was compared with the peer review grades for the relevancy between the source and the target articles. The results showed a significant correlation between the NSI ranks and those of peer review. Also, combined linkage (CL) and weighted direct citation (WDC) were calculated from the same data. According to the results of comparison between the NSI with other similarity measures, in most cases, NSI did better than others at reproducing the peer rankings. Our principal conclusion is that the NSI can be used to prioritize the citations of given highly cited article, and represent knowledge flow from the target article.
机译:引用的主要应用之一是查找与特定文章相关的文章。但是,并非所有引用都与目标文章同等相关。本文提出了一种识别最相关引用的方法。为此,提出了归一化相似度指数(NS1),以基于共同引用和共同引用来量化引用来源与目标之间的相似性。为了验证该方法,计算了五个引文网络的NSI,并将其与同行评审等级的来源和目标文章之间的相关性进行了比较。结果显示NSI等级与同行评审的等级之间存在显着相关性。同样,从相同的数据中计算出了联合连锁(CL)和加权直接引文(WDC)。根据NSI与其他相似性度量之间的比较结果,在大多数情况下,NSI在复制同行排名方面的表现要好于其他。我们的主要结论是,NSI可以用于对给定被高引用文章的引用进行优先排序,并代表来自目标文章的知识流。

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