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A fast method based on multiple clustering for name disambiguation in bibliographic citations
Author(s) -
Liu Yu,
Li Weijia,
Huang Zhen,
Fang Qiang
Publication year - 2015
Publication title -
journal of the association for information science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.903
H-Index - 145
eISSN - 2330-1643
pISSN - 2330-1635
DOI - 10.1002/asi.23183
Subject(s) - computer science , cluster analysis , pairwise comparison , ambiguity , context (archaeology) , information retrieval , set (abstract data type) , citation , dependency (uml) , digital library , data mining , artificial intelligence , world wide web , art , paleontology , literature , poetry , biology , programming language
Name ambiguity in the context of bibliographic citation affects the quality of services in digital libraries. Previous methods are not widely applied in practice because of their high computational complexity and their strong dependency on excessive attributes, such as institutional affiliation, research area, address, etc., which are difficult to obtain in practice. To solve this problem, we propose a novel coarse‐to‐fine framework for name disambiguation which sequentially employs 3 common and easily accessible attributes (i.e., coauthor name, article title, and publication venue). Our proposed framework is based on multiple clustering and consists of 3 steps: (a) clustering articles by coauthorship and obtaining rough clusters, that is fragments; (b) clustering fragments obtained in step 1 by title information and getting bigger fragments; (c) and clustering fragments obtained in step 2 by the latent relations among venues. Experimental results on a D igital B ibliography and L ibrary P roject ( DBLP ) data set show that our method outperforms the existing state‐of‐the‐art methods by 2.4% to 22.7% on the average pairwise F 1 score and is 10 to 100 times faster in terms of execution time.