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Vol.25 No.3
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Application of Machine Learning Techniques for Resolving Korean Author Names

Journal of the Korean Society for Information Management / Journal of the Korean Society for Information Management, (P)1013-0799; (E)2586-2073
2008, v.25 no.3, pp.27-39
https://doi.org/10.3743/KOSIM.2008.25.3.027

(2008). Application of Machine Learning Techniques for Resolving Korean Author Names. Journal of the Korean Society for Information Management, 25(3), 27-39, https://doi.org/10.3743/KOSIM.2008.25.3.027
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Abstract

In bibliographic data, the use of personal names to indicate authors makes it difficult to specify a particular author since there are numerous authors whose personal names are the same. Resolving same-name author instances into different individuals is called author resolution, which consists of two steps: calculating author similarities and then clustering same-name author instances into different person groups. Author similarities are computed from similarities of author-related bibliographic features such as coauthors, titles of papers, publication information, using supervised or unsupervised methods. Supervised approaches employ machine learning techniques to automatically learn the author similarity function from author-resolved training samples. So far, however, a few machine learning methods have been investigated for author resolution. This paper provides a comparative evaluation of a variety of recent high-performing machine learning techniques on author disambiguation, and compares several methods of processing author disambiguation features such as coauthors and titles of papers.

keywords
저자 식별, 동명저자, 기계학습, author disambiguation, same-name authors, machine learning techniques, author disambiguation, same-name authors, machine learning techniques

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