• Title/Summary/Keyword: Author Disambiguation Features

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Disambiguation of Author Names Using Co-citation (동시인용정보를 이용한 동명이인 저자의 중의성 해소)

  • Kang, In-Su
    • Journal of Information Management
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    • v.42 no.3
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    • pp.167-186
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    • 2011
  • Co-citation means that two or more studies are cited together by a later study. This paper deals with the relationship between co-citation and author disambiguation. Author disambiguation is to cluster same-name author instances into real-world individuals. Co-citation may influence author disambiguation in terms that two or more related research works performed by the same person may be co-cited by some later studies. This article describes automated steps to gather co-citation information from Google scholar, and proposes a new clustering algorithm to effectively integrate co-citation information with other author disambiguation features. Experiments showed that co-citation helps to improve the performance of author disambiguation.

Application of Machine Learning Techniques for Resolving Korean Author Names (한글 저자명 중의성 해소를 위한 기계학습기법의 적용)

  • Kang, In-Su
    • Journal of the Korean Society for information Management
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    • v.25 no.3
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    • pp.27-39
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    • 2008
  • 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.