期刊名称：International Journal of Software Engineering and Its Applications
印刷版ISSN：1738-9984
出版年度：2016
卷号：10
期号：4
页码：73-82
DOI：10.14257/ijseia.2016.10.4.08
出版社：SERSC
摘要：When using search engine services to search for scholarly articles, obtaining quick and accurate search results from a huge set of scholarly information is always important. However, most of the domestic and foreign search engine services for scholarly articles present a broad range of the results that correspond to the query of the researcher's name. Such results contribute in lowering the search precision and require users to spend time and effort to verify the results and find the necessary information. Such a problem is called "author ambiguity", while solving this problem is called "author disambiguation." An author disambiguation method classifies the authors with the same name into an actual person. By resolving author ambiguity, better search results can be obtained; this increases the recall rate and accuracy when searching for scholarly articles. In order to resolve author ambiguity in this paper, we shall expand the co-author network and identify the author using the co-author network information and basic bibliographic information as the features for machine learning Support Vector Machine. To examine the effectiveness of the proposed method, we test the author disambiguation method by targeting 92,100 IT-related scholarly data generated in Korea. Author disambiguation results through the expansion of co-author network are shown to have an F-1 measure of 94.79%. The result confirms that the author disambiguation method through the implementation of the co-author network is effective.
关键词：Author disambiguation; Co-author network; Support Vector Machine; ; Classification
