基于合作作者与隶属机构信息的同名排歧方法

doi:10.11896／j.issn.1002-137X.2018.11.034

摘要/Abstract

摘要： 同名排歧是实体分辨领域的重要研究内容之一,其旨在分辨出相同姓名对应的不同人。针对传统同名排歧方法需要丰富的信息以及无法解决信息缺乏时的排歧问题,提出了一种基于合作作者和隶属机构信息的同名排歧方法。根据作者间的合作关系以及作者与机构间的隶属关系构造实体关系图,采用广度优先搜索策略搜索图中两两同名作者间的有效路径;根据有效路径长度、数目及路径上边的类型,计算两个同名作者间的连接强度,并将其与阈值进行比较,实现同名排歧。实验结果表明,所提方法比当前最好的方法具有更好的同名排歧效果,且能够实现单一作者的同名排歧。

关键词: 连接强度, 实体分辨, 数据质量, 同名排歧, 有效路径

Abstract: Name disambiguation is one of the most challenging issues in entity resolution domain,and it aims at solving the problem that the same name is shared by different people.However,most of the conventional approaches rely heavily on sufficient information of entities,and fail to realize the name identification with insufficient information.This paper proposesd a novel name disambiguation approach based on co-authors and authors’affiliates.Specifically,entity relationship diagram is constructed based on co-authorship and authors’affiliates,and the breadth-first search scheme is utilized to search the effective path between each pair of authors with the exactly same name in the constructed entity relationship diagram.A unique metric connection strength between authors is calculated according to the length of effective path,the number of effective path and the type of edge on path.And it is compared with the threshold to achieve name disambiguation.Experimental results show that the proposed approach is better than the state-of-the-art approaches,and it is able to disambiguate the authors sharing the same name without co-authorship.

Key words: Connection strength, Data quality, Effective path, Entity resolution, Name disambiguation

中图分类号:

TP311

尚玉玲, 曹建军, 李红梅, 郑奇斌. 基于合作作者与隶属机构信息的同名排歧方法[J]. 计算机科学, 2018, 45(11): 220-225. https://doi.org/10.11896／j.issn.1002-137X.2018.11.034

SHANG Yu-ling, CAO Jian-jun, LI Hong-mei, ZHENG Qi-bin. Co-author and Affiliate Based Name Disambiguation Approach[J]. Computer Science, 2018, 45(11): 220-225. https://doi.org/10.11896／j.issn.1002-137X.2018.11.034

参考文献

[1]TAN M C,DIAO X C,CAO J J,et al.Relationship Type Based Connection Strength Model for Relationship-based Entity Resolution[J].Journal of Computational Information Systems,2015,11(16):5947-5957.
[2]ANDERSON A F,VELOSO A,MARCOS A G,et al.Self-trai- ning Author Name Disambiguation for Information Scarce Scenarios[J].Journal of the American Society for Information Scien-ce & Technology,2014,65(6):1257-1278.
[3]EMILIA A S,ANDERSON A F,MARCOS A G.Combining Classifiers and User Feedback for Disambiguating Author Names[C]∥Proceedings of JCDL’16.Knoxville,Tennessee,USA,2015:259-260.
[4]COTA R G,ANDERSON A F,MARCOS A G,et al.An Unsupervised Heuristic-based Hierarchical Method for Name Disambiguation in Bibliographic Citations[J].Journal of the American Society for Information Science & Technology,2010,61(9):1853-1870.
[5]FAKHRI M,PHILIPP M.Using Co-authorship Networks for Author Name Disambiguation[C]∥2016 IEEE/ACM Joint Conference on Digital Libraries(JCDL).2016:261-262.
[6]CARVALHO A P,ANDERSON A F,ALBERTO H F,et al.Incremental Unsupervised Name Disambiguation in Cleaned Digi-tal Libraries[J].Journal of Information and Data Management,2011,2(3):289-304.
[7]FAN X M,WANG J Y,PU X,et al.On Graph-based Name Di- sambiguation[J].ACM Journal of Data and Information Quality,2011,2(2):1-23.
[8]MADIAN K,PUCKTADA T,LEE C G.Online Person Name Disambiguation with Constraints[C]∥ACM／IEEE-CS Joint Conference on Digital Libraries.2015:37-46.
[9]KIM K,KHABSA M,GILES C L.Inventor Name Disambigua- tion for a Patent Database Using a Random Forest and DBSCAN[C]∥Proceedings of the 16th ACM/IEE-CS on Joint Conference on Digital Libraries.2016:269-270.
[10]ZHENG C S,JI D,CAI D F.The Method of Expert Name Di- sambiguation Based on System Combination[J].Journal of Shen-yang Aerospace University,2014,31(2):74-78.(in Chinese)
郑才松,季铎,蔡东风.基于系统融合的专家同名区分方法[J].沈阳航空航天大学学报,2014,31(2):74-78.
[11]CHEN W L.Name Disambiguation Based on the Coauthorship Association Graph of Scholar Papers[D].Hangzhou:Hangzhou Dianzi University,2017.(in Chinese)
陈未路.基于科研论文合作者关系图的同名排歧方法研究[D].杭州:杭州电子科技大学,2017.
[12]THIAGO A G,RICARD S T,ARIADNE M B,et al.A Relevancd Feedback Approach for the Author Name Disambiguation Problem[C]∥Proceedings of ACM/IEEE Joint Conference on Digital Libraries’13 Indianapolis.Indiana,USA,2013:209-218.
[13]YIN X X,HAN J W,PHILIP S Y.Object Distinction:Distinguishing Objects with Identical Names[C]∥Proceedings ofInternational Conference on Data Engineering(ICDE).2007:1242-1246.
[14]XU R F,GUI L,LU Q,et al.Incorporating Multi-kernel function and Internet Verification for Chinese Person Name Disambiguation[J].Frontiers of Computer Science,2016,10(6):1-13.
[15]HIEN T N,TRU H C.Named Entity Disambiguation:A Hybird Statistical and Rule-Based Incremental Approach[C]∥Procee-dings of the Semantic Web:the 3th Asian Semantic Web Confe-rence(ASWC).2008:420-433.
[16]FU J L,QIU J,GUO Y L,et al.Entity Linking and Name Disam- biguation Using SVM in CHINESE Micro-blogs[C]∥Proceedings of International Conference on Natural Computation.IEEE,2016:468-472.
[17]LI Y P.Bibliometric Analysis and Name Disambiguation Research Based on Knowledge Clustering[D].Nanjing:Nanjing University of Posts and Telecommunications,2016.(in Chinese)
李永萍.基于知识聚类的文献统计与重名消歧机制的研究[D].南京:南京邮电大学,2016.
[18]MIN S,ERIN H K,HA J K.Exploring author name disambigua- tion on PubMed-scale[J].Journal of Informetrics,2015,9(4):924-941.
[19]MU L M.Research of the Nature & Operation of Finite MultiSet[J].Journal of Neijiang Normal University,2009,24(4):5-8.(in Chinese)
牟廉明.有限多重集的运算及性质[J].内江师范学院学报,2009,24(4):5-8.
[20]TRAVERS J,MILGRAM S.An Experimental Study of the Small World Problem[J].Sociometry,1969,32(4):425-443.
[21]MONGLI L,TOK W L,WAI L L.Intelliclean:A Knowledge-Based Intelligent Data Cleaner[C]∥ACM Sigkdd International Conference on Knowledge Discovery & Data Mining.2000:290-294.

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