2017
DOI: 10.1017/s0269888917000182
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A survey of author name disambiguation techniques: 2010–2016

Abstract: Digital libraries content and quality of services are badly affected by the author name ambiguity problem in the citations and it is considered as one of the hardest problems faced by the digital library researchers. Several techniques have been proposed in the literature for the author name ambiguity problem. In this paper, we reviewed some recently presented author name disambiguation techniques and give some challenges and future research directions. We analyze the recent advancements in this field and clas… Show more

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Cited by 57 publications
(44 citation statements)
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“…They introduce a taxonomy that distinguishes among others between author grouping (resolving synonymy) and author assignment (homonymy). In a more recent comparison, Hussain and Ashger [8] discuss AND methods since 2010 and try to pinpoint strengths and weaknesses of the selected approaches. They use a different taxonomy focusing on the technology used (i.e., machine learning, graph-based, etc.…”
Section: Related Workmentioning
confidence: 99%
“…They introduce a taxonomy that distinguishes among others between author grouping (resolving synonymy) and author assignment (homonymy). In a more recent comparison, Hussain and Ashger [8] discuss AND methods since 2010 and try to pinpoint strengths and weaknesses of the selected approaches. They use a different taxonomy focusing on the technology used (i.e., machine learning, graph-based, etc.…”
Section: Related Workmentioning
confidence: 99%
“…Current AND methods can be categorized into two groups: the first are the methods that resolve whole citations in BDs on new insertions called batch AND and the second are those methods which disambiguate only newly inserted citations called incremental AND. Similarly, Hussain et al in [6] proposed a taxonomy for existing AND methods and divided all methods into supervised [3,[11][12][13], unsupervised [14][15][16][17][18][19], semisupervised [20][21][22], graph-oriented approaches using graph models or social networks [1,[23][24][25], and string processing or heuristicbased methods [26][27][28]. In this section, we only overview the incremental AND methods.…”
Section: Related Workmentioning
confidence: 99%
“…In this section, we only overview the incremental AND methods. For detailed discussions about AND methods and AND data sets, interested readers are referred to our recent survey of these techniques presented in [6] and [29], respectively.…”
Section: Related Workmentioning
confidence: 99%
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“…But unfortunately, AND is not an elementary problem because distinct authors may share the same name, which is quite common for Asians, especially Chinese researchers [9], since different Chinese names will be the same when mapped to English (e.g., and share the same English name Wei Wang). The problem of disambiguating who is who dates back at least few decades, and it is typically viewed as a clustering problem and solved by various clustering models, such models have to answer two questions inevitably, that is how to quantify the similarity and how to determine cluster size [8]. Many existing literatures mainly focus on answering the first question, such as feature-based methods [12,13] and graph-based methods [3,16,20].…”
Section: Introductionmentioning
confidence: 99%