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数据分析与知识发现  2020, Vol. 4 Issue (2/3): 48-59     https://doi.org/10.11925/infotech.2096-3467.2019.0644
  专辑 本期目录 | 过刊浏览 | 高级检索 |
基于网络表示学习的作者重名消歧研究*
余传明1(),钟韵辞1,林奥琛1,安璐2
1中南财经政法大学信息与安全工程学院 武汉 430073
2武汉大学信息管理学院 武汉 430072
Author Name Disambiguation with Network Embedding
Yu Chuanming1(),Zhong Yunci1,Lin Aochen1,An Lu2
1School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan 430073, China
2School of Information Management, Wuhan University, Wuhan 430072, China
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摘要 

【目的】 消除文献系统中的作者重名歧义,以解决其导致的文献错误聚合问题。【方法】 通过结构化文献数据建立作者网络、文献网络以及作者-文献网络,融合不同网络表示学习方法获得文献节点表示,并采用无监督学习方法,将文献节点表示作为特征,使用层次凝聚聚类按照真实作者对文献进行正确划分。【结果】 在ArnetMiner、CiteSeerX和DBLP三组数据集上进行实证研究,本文方法在网络稀疏的情况下仍然具有较好的效果,Macro-F1值在次优模型基础上最高提升6%。【局限】 仅研究英文情境下的作者重名消歧。【结论】 基于网络表示学习的方法能够有效解决作者重名消歧问题,实验结果对于改进科研合作推荐、引文推荐以及知识网络相关研究具有重要意义。

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余传明
钟韵辞
林奥琛
安璐
关键词 网络表示学习异构网络作者重名消歧无监督学习    
Abstract

[Objective] The paper tries to eliminate the ambiguity of author names in the document system, aiming to solve the problem of incorrect document aggregation.[Methods] First, we constructed three types of networks for authors, documents and author-documents, with structured document data. Then we combined different network embedding methods to obtain the representation of document nodes. Finally, we employed the unsupervised learning model and the hierarchical agglomerative clustering to process the documents.[Results] We conducted empirical studies on datasets from ArnetMiner, CiteSeerX and DBLP. Our method performed well on sparse networks and the macro-F1 value increased by 6%.[Limitations] We only explored author name disambiguation in English.[Conclusions] The proposed method could effectively reduce the ambiguity of author names. It is of great significance for scientific collaboration and citation recommendation, as well as knowledge network related research.

Key wordsNetwork Embedding    Heterogeneous Network    Author Name Disambiguation    Unsupervised Learning
收稿日期: 2019-06-11      出版日期: 2020-04-26
ZTFLH:  TP391  
基金资助:*本文系教育部人文社会科学研究一般项目“多语言情境下基于深度表示与对齐的观点摘要研究”(19YJC870029);国家自然科学基金面上项目“大数据环境下基于领域知识获取与对齐的观点检索研究”的研究成果之一(71373286)
通讯作者: 余传明     E-mail: yuchuanming2003@126.com
引用本文:   
余传明,钟韵辞,林奥琛,安璐. 基于网络表示学习的作者重名消歧研究*[J]. 数据分析与知识发现, 2020, 4(2/3): 48-59.
Yu Chuanming,Zhong Yunci,Lin Aochen,An Lu. Author Name Disambiguation with Network Embedding. Data Analysis and Knowledge Discovery, 2020, 4(2/3): 48-59.
链接本文:  
https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2019.0644      或      https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2020/V4/I2/3/48
Fig.1  基于网络表示学习的作者重名消歧框架
Fig.2  作者与文献的网络表示模型
XML标签 元数据
<title> Trust Mechanism in Distributed Access Control Model of P2P Networks
<authors> Lei Wang,Yanqin Zhu,Lanfang Jin,Xizhao Luo
<label> 0
<id> 4944
<jconf> ACIS-ICIS
<year> 2008
<organization> null
Table 1  实验数据样例
Macro-F1_
arnetminer
AuthorList AuthorList-NMF NDNE ADNE 本文方法
Lei Wang 23.09 20.04 76.97 28.39 78.64
Jing Zhang 24.58 25.37 73.48 49.56 77.04
Yu Zhang 27.98 17.51 60.28 19.24 55.86
Bin Li 25.82 19.86 80.34 42.11 78.30
Yang Wang 19.01 18.70 53.06 21.07 54.42
Hao Wang 17.23 9.15 54.81 30.67 50.49
Wei Xu 24.57 18.81 66.46 25.05 72.58
Bo Liu 19.24 25.66 86.71 19.05 79.65
Gang Chen 25.79 9.77 63.07 28.09 67.99
Lei Chen 21.13 11.77 60.37 28.96 60.67
Table 2  在ArnetMiner数据集上的作者重名消歧结果
Macro-F1_citeseerx AuthorList AuthorList-NMF NDNE ADNE 本文方法
J Lee 6.41 6.25 42.58 6.62 21.12
S Lee 4.94 4.93 39.79 6.02 33.45
Y Chen 9.45 7.20 47.52 10.07 26.98
C Chen 11.20 4.92 35.63 7.89 18.03
J Smith 9.75 8.51 35.81 9.02 24.47
A Gupta 3.93 5.20 41.14 5.73 23.63
J Martin 17.17 13.83 53.98 22.58 41.05
D Johnson 12.91 15.23 28.55 17.85 24.07
A Kumar 25.67 21.78 35.74 17.96 14.33
M Brown 17.80 19.48 46.11 29.04 24.68
Table 3  在CiteSeerX数据集上的作者重名消歧结果
Macro-F1_dblp AuthorList AuthorList-NMF NDNE ADNE 本文
方法
Wei Wang 12.94 2.37 70.30 12.73 29.56
Yi Zhang 24.91 10.89 34.68 39.31 31.98
Jian Zhang 30.43 13.46 33.83 33.13 23.52
Jing Wang 16.67 11.92 77.00 58.33 67.71
Lei Zhang 5.94 9.68 50.54 8.96 19.28
Wei Li 18.94 4.67 42.52 31.45 32.03
Yang Wang 16.07 12.52 39.98 30.68 47.67
Minsoo Kim 17.73 21.08 43.24 33.85 52.34
Rui Wang 32.16 11.04 50.55 25.38 55.83
Jun Sun 17.42 16.97 58.04 24.57 40.63
Table 4  在DBLP数据集上的作者重名消歧结果
网络特征 ArnetMiner CiteSeerX DBLP
平均文献条目数 197.9 733.0 141.7
平均真实作者数 61.4 43.2 13.7
平均节点数(作者网络) 323.5 681.0 160.0
平均边数(作者网络) 600.5 1763.4 426.9
平均节点度数(作者网络) 3.7 4.7 5.0
平均边数(文献网络) 783.7 36541.7 2338.4
平均节点度数(文献网络) 8.1 83.1 19.4
Table 5  三组数据集的网络统计特征
Fig.3  学习迭代轮次对模型效果的影响
Fig.4  表示向量维数对模型效果的影响
Fig.5  LINE相似度选择对模型效果的影响
Fig.6  DeepWalk与LINE交换训练对象的影响
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