Semantic Web link analysis to discover social relationships in academic communities
Semantic Web link analysis to discover social relationships in academic communities
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DOI：
10.1109/saint.2005.58
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发表时间：
2005-01
期刊：
影响因子：
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通讯作者：
T. Miki;Saeko Nomura;T. Ishida
中科院分区：
文献类型：
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作者：
T. Miki;Saeko Nomura;T. Ishida
为了探究研究人员网站之间的链接如何精确地反映学术团体的联系，本研究进行了网络引用分析。对3878个计算机科学家的网站及其8811篇文章进行了评估。对其中被引用最频繁的200名研究人员进行了网络作者共引分析，并与文献数据进行了比较。结果表明，网络上的一组研究人员成为文献研究人员的一个超集，即一个代表更大研究领域的团体。然后我们开发了网络链接本体，它定义了研究人员之间的各种关系，以便对200个网站之间的链接应用元数据标注。研究结果如下：1) 由于知识联系分析将社会认知联系区分开来，它能更好地近似文献作者共引；2) 由于社会认知联系分析不仅可以利用合著者网络，还能利用各种链接，因此可以发现多种社会联系；3) 融合知识联系和社会认知联系分析可以发现连接社会网络上不同研究领域的把关人。
To discover how precisely links among researchers' Web sites capture the ties of academic communities, this study conducts Web citation analysis. 3,878 Web sites of computer scientists and their 8,811 articles are assessed. 200 most frequently cited researchers from them are subjected to Web author cocitation analysis and compared to bibliographical data. As a result, a cluster of researchers on the Web becomes a superset of bibliographical one, that is, a community representing a larger research field. We then develop Web link ontology that defines various relationships among researchers to apply metadata annotation to the links among 200 sites. The findings are 1) since intellectual ties analysis separates off sociocognitive ties, it results into a better approximation of bibliographical author cocitation, 2) since sociocognitive ties analysis can utilize various links not only coauthor networks, diverse kinds of social connections can be found, 3) merging intellectual ties and sociocognitive ties analyses detect gatekeepers who bridges research fields on social networks.