书目耦合
引用
引文分析
相似性(几何)
计算机科学
星团(航天器)
共引
生物医学
联轴节(管道)
连贯性(哲学赌博策略)
书目数据库
情报检索
分歧(语言学)
数据挖掘
数据科学
人工智能
统计
图书馆学
生物信息学
数学
语言学
图像(数学)
生物
程序设计语言
哲学
工程类
机械工程
作者
Kevin W. Boyack,Richard Klavans
摘要
Abstract In the past several years studies have started to appear comparing the accuracies of various science mapping approaches. These studies primarily compare the cluster solutions resulting from different similarity approaches, and give varying results. In this study we compare the accuracies of cluster solutions of a large corpus of 2,153,769 recent articles from the biomedical literature (2004–2008) using four similarity approaches: co‐citation analysis, bibliographic coupling, direct citation, and a bibliographic coupling‐based citation‐text hybrid approach. Each of the four approaches can be considered a way to represent the research front in biomedicine, and each is able to successfully cluster over 92% of the corpus. Accuracies are compared using two metrics—within‐cluster textual coherence as defined by the Jensen‐Shannon divergence, and a concentration measure based on the grant‐to‐article linkages indexed in MEDLINE. Of the three pure citation‐based approaches, bibliographic coupling slightly outperforms co‐citation analysis using both accuracy measures; direct citation is the least accurate mapping approach by far. The hybrid approach improves upon the bibliographic coupling results in all respects. We consider the results of this study to be robust given the very large size of the corpus, and the specificity of the accuracy measures used.
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