A novel multiple layers name disambiguation framework for digital libraries using dynamic clustering

Jia Zhu (), Xingcheng Wu, Xueqin Lin, Changqin Huang, Gabriel Pui Cheong Fung and Yong Tang
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Jia Zhu: South China Normal University
Xingcheng Wu: South China Normal University
Xueqin Lin: South China Normal University
Changqin Huang: South China Normal University
Gabriel Pui Cheong Fung: The Chinese University of Hong Kong
Yong Tang: South China Normal University

Scientometrics, 2018, vol. 114, issue 3, No 1, 794 pages

Abstract: Abstract In many types of databases, such as a science bibliography database, the name attribute is the most commonly used identifier to recognize entities. However, names are frequently ambiguous and not always unique, thereby causing problems in various fields. Name disambiguation is a data management task that aims to properly distinguish different entities that share the same name, particularly for large databases such as digital libraries, because the information that can be used to identify author’s name is limited. In digital libraries, the issue of ambiguous author names occurs due to the existence of multiple authors with the same name or different name variations for the same author. Most previous works conducted to solve this issue frequently used hierarchical clustering approaches based on information within citation records, e.g., co-authors and publication titles. In the present study, we propose a multiple layers name disambiguation framework that is not only applicable to digital libraries but can also be easily extended to other applications. Our framework adopts a dynamic clustering mechanism to minimize clustering errors. We evaluated our approach on real world corpora, and favorable experiment results indicated that our proposed framework was feasible.

Keywords: Name disambiguation; Dynamic clustering; Digital library (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (6)

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DOI: 10.1007/s11192-017-2611-8

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