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A comparison of self-organizing maps and pathfinder networks for the mapping of co-cited authors.

机译:比较自组织地图和探路者网络以映射被引作者。

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摘要

Author co-citation analysis (ACA) is a methodology for determining the relationships between author pairs as specified by patterns of repeated co-citation. These patterns are visualized through computerized mapping techniques that now include self-organized maps or SOMs (also known as Kohonen feature maps) and Pathfinder networks (PFNETs).; In this research, ACA maps of authors in the humanities were produced as both SOMs and PFNETs using an experimental Web-based system called AuthorLink based on author co-citation data from the Arts & Humanities Citation Index for the years 1988–1997. The intent was to test how well each map type corresponded to a set of mental maps of experts, the “gold standard,” for the same authors. The mental maps were elicited from 20 experts in the humanities with card sorts.; The first research question was: How well do SOMs and PFNETs based on one famous author—here, Plato—correspond to the experts' combined mental map for that author? A Pearson correlation of the SOM with the mental map was compared to a Pearson correlation of the PFNET with the mental map. While both correlations proved highly significant (p 0.001), the SOM's (r = 0.968) was significantly higher (p 0.001) than the PFNET's (r = 0.783).; The second research question was: How well do SOMs and PFNETs based on personally chosen authors correspond to the experts' individual mental maps of these authors? Here, SOMs and PFNETS for 20 different authors were compared with the mental maps of those authors so as to produce difference scores for the two mapping techniques. These were analyzed through a paired-sample t-test. The 20 individual SOMs corresponded to the experts' mental maps better than the 20 individual PFNETs (p = 0.002).; The third research question was: Is one type of map, SOM or PFNET, preferred by experts? The 20 experts were statistically equal, 11 for the PFNET to 9 for the SOM. However, during interviews with the experts it was suggested that the two types are complementary and that both are needed to do a thorough exploration.
机译:作者共同引文分析(ACA)是一种确定作者对之间关系的方法,该方法由重复共同引文的模式指定。这些模式通过计算机制图技术可视化,现在包括自组织图或SOM(也称为Kohonen特征图)和Pathfinder网络(PFNET)。在这项研究中,基于1988-1997年艺术与人文科学引文索引的作者同被引证数据,使用名为AuthorLink的基于实验的基于Web的系统,将人文科学领域的作者ACA地图制作为SOM和PFNET。目的是测试每种地图类型与同一作者的一组心理专家地图(“黄金标准”)的对应程度。心理图谱是由20位人文科学专家以卡片形式绘制的。第一个研究问题是:基于一位著名作者(这里是柏拉图)的SOM和PFNET与该作者的专家组合心理图相对应吗?将SOM与心理图的Pearson相关与PFNET与心理图的Pearson相关进行了比较。虽然两者的相关性都具有高度显着性(p <0.001),但SOM的相关性(r = 0.968)显着高于PFNET的相关性(p <0.001)(r = 0.783)。第二个研究问题是:基于个人选择作者的SOM和PFNET与这些作者的专家个人心理图的对应程度如何?在此,将20位不同作者的SOM和PFNETS与这些作者的思维导图进行比较,以得出两种映射技术的得分差异。通过配对样本t检验分析了这些数据。 ; 20个单独的SOM与专家的心理图相对应的效果要好于20个单独的PFNET(p = 0.002)。第三个研究问题是:专家是否喜欢一种地图,即SOM或PFNET?在统计学上,这20名专家是平等的,PFNET的11名,SOM的9名。但是,在与专家的访谈中,有人建议这两种类型是互补的,并且需要进行彻底的探索。

著录项

  • 作者

    Buzydlowski, Jan William.;

  • 作者单位

    Drexel University.;

  • 授予单位 Drexel University.;
  • 学科 Library Science.
  • 学位 Ph.D.
  • 年度 2003
  • 页码 217 p.
  • 总页数 217
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 图书馆学、图书馆事业;
  • 关键词

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