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Data Analysis and Knowledge Discovery  2021, Vol. 5 Issue (4): 90-102    DOI: 10.11925/infotech.2096-3467.2020.0532
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Disambiguation of Chinese Author Names with Multiple Features
Lin Kerou,Wang Hao(),Gong Lijuan,Zhang Baolong
School of Information Management, Nanjing University, Nanjing 210023, China
Jiangsu Key Laboratory of Data Engineering and Knowledge Service, Nanjing 210023, China
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Abstract  

[Objective] This paper aims to address the issues facing document management systems due to Chinese authors with the same names. [Methods] We built author entities with “author name + institution name” based on bibliographic data. Then, we used the attributes of author entities to construct six similarity features from three aspects. Third, we merged these features by principal component analysis or direct weight assignment. Finally, we evaluated the performance of the proposed method. [Results] Our methods significantly reduced processing time. Their F1 values on the LIS dataset were 70.74% and 70.42%, while their F1 values on the economics dataset were 81.90% and 80.93%. [Limitations] The attributes used in this research were only retrieved from metadata of the papers. [Conclusions] The proposed method could improve weight setting of multiple features.
Key words： Feature Fusion      Author Name Disambiguation      PCA      Chinese Papers     
Received: 08 June 2020      Published: 10 October 2020
ZTFLH:  分类号: TP393  
Fund:“Six Talent Peaks” Project in Jiangsu Province(JY-001);Jiangsu Young Talents in Social Sciences, the Tang Scholar of Nanjing University
Corresponding Authors: Wang Hao     E-mail: ywhaowang@nju.edu.cn
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Kerou Lin
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Cite this article:

Lin Kerou,Wang Hao,Gong Lijuan,Zhang Baolong. Disambiguation of Chinese Author Names with Multiple Features. Data Analysis and Knowledge Discovery, 2021, 5(4): 90-102.

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https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2020.0532     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2021/V5/I4/90

Research Framework
AU IN CA CI IA TI KW
陈婧 湖南师范大学 王知津/张收棉/张素芳/严贝妮/周贺来 南开大学/国家图书馆 蔡骐/陈婧/陈君莲/陈万忠/党洪莉/党美锦… 企业竞争情报作战室运行准备机制研究/政府公共决策支持系统的构建 企业竞争情报/竞争情报作战室/运行准备/公共决策/决策支持/信息系统
An Example of the Description Structure of the Entity to be Disambiguated
机构信息方面特征
（SIIN）		 合作信息方面特征（SCIN） 主题信息方面特征（SSIN）
机构作者相似度（SIA）
机构名相似度（SIN）		 合作者相似度（SCA）
合作机构相似度（SCI）		 标题相似度（STI）
关键词相似度（SKW）
Summary of All Features
测试集 序号 AU IN1 IN2 SIA SIN SCA SCI STI SKW
LIS测试集 1 安新颖 中国医学科学院 中国医学科学院医学信息研究所 0.845 1.000 0.612 0.000 0.204 0.000
2 安新颖 中国科学院文献情报中心 中国医学科学院 0.442 0.000 0.250 0.707 0.226 0.120
3 安新颖 黑龙江大学 中国科学院文献情报中心 0.331 0.000 0.408 0.000 0.000 0.000
… … … … … … … … … …
1281 张梅 南京大学 西安文理学院图书馆 0.175 0.000 0.000 0.000 0.000 0.000
1282 张梅 解放军医学图书馆 南京大学 0.298 0.000 0.000 0.000 0.000 0.000
1283 张梅 福建师范大学 河北大学 0.214 0.000 0.000 0.000 0.000 0.000
ECO测试集 1 张巍 吉林财经大学 上海理工大学 0.203 0.000 0.000 0.000 0.086 0.000
2 张巍 吉林财经大学 财政部企业司 0.308 0.000 0.000 0.000 0.052 0.000
3 张巍 吉林财经大学 华信惠悦中国投资咨询部 0.335 0.000 0.000 0.000 0.000 0.000
… … … … … … … … … …
1736 姜松 重庆理工大学经济金融学院 重庆理工大学 0.431 1.000 0.500 0.000 0.234 0.000
1737 姜松 重庆理工大学经济金融学院 西南大学经济管理学院 0.235 0.236 0.267 0.577 0.243 0.102
1738 姜松 重庆理工大学 西南大学经济管理学院 0.191 0.000 0.267 0.000 0.178 0.102
The Similarity Calculation Results of the Entities to be Disambiguated in Test Dataset
测试集 序号 AU IN1 IN2 judge FS
LIS测试集 1 张静 互联网实验室 浙江传媒学院互联网与社会研究中心 1 4.490
2 王斌 河南工业大学 河南工业大学管理学院 1 4.456
3 张静 东北林业大学 东北林业大学图书馆 1 4.099
4 白献阳 河北大学管理学院 中国人民大学 1 3.926
5 刘冬梅 天津理工大学 天津理工大学图书馆 1 3.604
6 刘华 上海大学 上海大学图书馆 1 3.419
7 安新颖 中国医学科学院 中国医学科学院医学信息研究所 1 3.383
8 鄢小燕 中国科学院 中国科学院国家科学图书馆成都分馆 1 3.353
9 张静 西安交通大学图书馆采编部 西安交通大学图书馆副研究馆员 1 3.253
10 李广建 北京大学 北京大学信息管理系 1 3.112
ECO测试集 1 李建军 新疆大学新疆创新管理研究中心 新疆大学经济与管理学院 1 8.708
2 丁慧 南京大学商学院 南京大学商学院经济学系 1 5.328
3 严良 中国地质大学[武汉]经济管理学院 中国地质大学（武汉）经济管理学院 1 4.562
4 刘杨 天水师范学院经管学院 天水师范学院经济与社会管理学院 1 4.325
5 李军 中铝财务有限责任公司 对外经济贸易大学国际商学院 1 4.286
6 贾康 财政部财政科研所 财政部财科所 1 4.243
7 姜松 重庆理工大学经济与贸易学院 重庆理工大学经济金融学院 1 4.142
8 刘伟 北京大学 北京大学经济学院 1 3.793
9 侯鹏 北京林业大学经济管理学院 北京林业大学 1 3.767
10 张云 南开大学经济学院经济系 南开大学经济学院 1 3.693
The Top 10 Results of Sorted FS
The Evaluation Results of Fusing All Features by PCA on LIS Test Dataset
The Evaluation Results of Fusing All Features by PCA on ECO Test Dataset
测试集 序号 去除的方面 WZSIA WZSIN WZSCA WZSCI WZSTI WZSKW R_threshold P/% R/% F1/%
LIS测试集 1 无 0.23 0.27 0.12 0.02 0.16 0.20 0.50 81.93 50.00 62.10
2 SIIN 0.00 0.00 0.18 0.10 0.36 0.36 0.65 52.07 64.71 57.70
3 SCIN 0.40 0.38 0.00 0.00 0.08 0.14 0.40 81.82 39.71 53.47
4 SSIN 0.38 0.39 0.18 0.06 0.00 0.00 0.45 89.71 44.85 59.80
ECO测试集 1 无 0.08 0.15 0.23 0.15 0.18 0.20 0.65 87.10 65.32 74.65
2 SIIN 0.00 0.00 0.29 0.21 0.24 0.26 0.50 82.67 50.00 62.31
3 SCIN 0.12 0.25 0.00 0.00 0.31 0.32 0.65 69.23 65.32 67.22
4 SSIN 0.16 0.24 0.34 0.27 0.00 0.00 0.70 92.55 70.16 79.82
The Highest F1 Value of Fusing Features by PCA after Removing One Single Aspect Features
测试集 序号 去除的特征 WZSIA WZSIN WZSCA WZSCI WZSTI WZSKW R_threshold P/% R/% F1/%
LIS测试集 1 无 0.23 0.27 0.12 0.02 0.16 0.20 0.50 81.93 50.00 62.10
2 SIA 0.00 0.27 0.14 0.04 0.26 0.29 0.65 61.81 65.44 63.57
3 SIN 0.14 0.00 0.15 0.08 0.31 0.32 0.65 53.01 64.71 58.28
4 SCA 0.04 0.10 0.00 0.26 0.32 0.29 0.65 52.69 64.71 58.09
5 SCI 0.32 0.30 0.17 0.00 0.08 0.13 0.45 91.04 44.85 60.10
6 STI 0.29 0.33 0.14 0.02 0.00 0.21 0.50 83.95 50.00 62.67
7 SKW 0.31 0.33 0.16 0.05 0.16 0.00 0.50 82.93 50.00 62.39
ECO测试集 1 无 0.08 0.15 0.23 0.15 0.18 0.20 0.65 87.10 65.32 74.65
2 SIA 0.00 0.17 0.25 0.17 0.20 0.22 0.65 84.38 65.32 73.64
3 SIN 0.10 0.00 0.27 0.19 0.21 0.23 0.50 84.93 50.00 62.94
4 SCA 0.10 0.21 0.00 0.17 0.26 0.26 0.70 70.73 70.16 70.45
5 SCI 0.10 0.19 0.25 0.00 0.21 0.24 0.70 79.09 70.16 74.36
6 STI 0.11 0.19 0.28 0.19 0.00 0.22 0.70 90.63 70.16 79.09
7 SKW 0.11 0.20 0.28 0.21 0.20 0.00 0.70 87.88 70.16 78.03
The Highest F1 Value of Fusing Features by PCA after Removing One Single Feature
The Effects of Direct Weight Assignment Using One Aspect Features as a Unit Compared with Using One Feature as a Unit
测试集 序号 WSIIN WSCIN WSSIN R_threshold P/% R/% F1/%
LIS测试集 1 0.05 0.85 0.10 0.55 97.40 55.15 70.42
2 0.10 0.70 0.20 0.55 97.40 55.15 70.42
ECO测试集 1 0.05 0.95 0.00 0.70 95.60 70.16 80.93
2 0.10 0.90 0.00 0.70 95.60 70.16 80.93
3 0.15 0.85 0.00 0.70 95.60 70.16 80.93
The Evaluation Results When Fusing Features by Direct Weight Assignment Using One Aspect Features as a Unit and Getting the Highest F1 Value
The Average Value of Each Weight When Fusing Features by Direct Weight Assignment Using One Single Feature as a Unit and Getting the Highest F1 Value
The F1 Value of the Mixed Method Compared with That of Using PCA Only on LIS Test Dataset
The F1 Value of the Mixed Method Compared with That of Using PCA Only on ECO Test Dataset
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