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Data Analysis and Knowledge Discovery  2022, Vol. 6 Issue (5): 10-19    DOI: 10.11925/infotech.2096-3467.2021.0189
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Review of Studies on Incremental Name Disambiguation
Cao Simeng,Li Chunwang()
National Science Library, Chinese Academy of Sciences, Beijing 100190, China; Department of Library Information and Archives Management, University of Chinese Academy of Sciences, Beijing 100190, China
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Abstract  

[Objective] This paper analyzes the research on name incremental disambiguation for authors, aiming to provide reference for future studies. [Coverage] We used “author” and “name disambiguation” as keywords to search Google Scholar, ACM, IEEE, Elsevier, Springer, CNKI and VIP databases. After manually screening and extending citation search based on seed documents, a total of 58 articles were retrieved, which included 30 papers directly discussing incremental disambiguation, and 28 other related research. [Methods] We discussed the developments, technical frameworks, and basic principles of incremental disambiguation. We also analyzed the development of incremental disambiguation on similarity comparison strategies, author assignment methods, and other issues.[Results] Popular areas include feature selection and representation, similarity calculation and author assignment methods. However, fragment merging, multi-topic recognition of the same author, and error-correction needs to be strengthened.[Limitations] There were limited studies on direct incremental disambiguation of author names, which could not fully support our results. [Conclusions] The research on incremental disambiguation should be strengthened. Combining traditional feature engineering methods with deep learning and a.pngicial intelligence technology could address more practical issues.
Key words： Author Name Disambiguation      Incremental Disambiguation      Similarity     
Received: 01 March 2021      Published: 21 June 2022
ZTFLH:  G250  
Fund:Literature and Information Capacity Building Project of Chinese Academy of Sciences(Y929090401)
Corresponding Authors: Li Chunwang,ORCID：0000-0002-6313-6576     E-mail: licw@mail.las.ac.cn
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Simeng Cao
Chunwang Li

Cite this article:

Cao Simeng, Li Chunwang. Review of Studies on Incremental Name Disambiguation. Data Analysis and Knowledge Discovery, 2022, 6(5): 10-19.

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https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2021.0189     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2022/V6/I5/10

作者分配方法 相似度比较策略 特征表示方式 主要方法或作者 发表年份
基于规则法 全记录 代数模型 INDi[8-9]、Tang等[10]、Han等[11] 2011/2020、2011、2017
集合论模型+代数模型 Protasiewicz等[12]、昌宁等[13] 2016、2020
作者模型 集合论模型 CAND[14-15] 2018/2019
分类法 全记录 代数模型 SLAND[16]、翟晓瑞等[17]、涂世文[18] 2012、2019、2020
概率模型 Zhang等[19⇓⇓-22] 2016/2017/2017/2019
部分记录 代数模型 CONNA[23] 2019
作者模型 代数模型 吴梓明[24] 2020
概率模型 Katsurai等[25]、Zhao等[26] 2016、2017
聚类法 全记录 代数模型 周杰等[27-28]、Zhang等[29] 2016/2016/2016、2018
概率模型 INC[30-31] 2015/2017
部分记录 代数模型 Khabsa[32]、Treeratpituk[33]、MINDi[34] 2012/2015、2014
作者模型 概率模型 IncAD[35] 2015
基于图的方法 全记录 图模型 Qiao等[36] 2019
作者模型 图模型 李娜[37] 2020
The Comparison of Principal Disambiguation Techniques
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