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数据分析与知识发现  2019, Vol. 3 Issue (1): 27-37     https://doi.org/10.11925/infotech.2096-3467.2018.1363
  专题 本期目录 | 过刊浏览 | 高级检索 |
科研实体名称规范的研究与实践*
张建勇1,2,钱力1,2,于倩倩1(),董智鹏1,黄永文3,刘建华4,郭舒5,王峰6
1中国科学院文献情报中心 北京 100190
2中国科学院大学图书情报与档案管理系 北京 100190
3中国农业科学院农业信息研究所 北京 100081
4上海科技大学图书馆 上海 201210
5国家互联网应急中心 北京 100029
6中国科学院自动化研究所 北京 100190
Constructing Name Authority for Research Entities
Jianyong Zhang1,2,Li Qian1,2,Qianqian Yu1(),Zhipeng Dong1,Yongwen Huang3,Jianhua Liu4,Shu Guo5,Feng Wang6
1National Science Library, Chinese Academy of Sciences, Beijing 100190, China
2Department of Library, Information and Archives Management, University of Chinese Academy of Sciences, Beijing 100190, China
3Institute of Agricultural Information, Chinese Academy of Agricultural Sciences, Beijing 100081, China
4Library of Shanghai Tech University , Shanghai 201210, China
5National Computer Network Emergency Response Technical Team/Coordination Center of China, Beijing 100029, China
6Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
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摘要 

【目的】建立机构规范、作者规范、期刊规范、基金规范, 为发现系统、科研实体分析评价等建立数据基础。【方法】以多源异构数据为基础, 对数据进行汇聚和融合, 形成具有唯一标识符的统一的结构化数据。依据名称规范元数据模型, 对科研实体及实体间的关系进行抽取。针对不同的科研实体可获取的文献特征, 制定不同的消歧规则集合, 结合传统字符串匹配方法和深度学习方法进行文本相似度计算。【结果】形成包含260多万条数据的机构规范库、2 300多万条数据的作者规范库、3万多条数据的期刊规范库和200多万条数据的基金规范库。以NSTL机构规范为例, 与InCites机构规范进行对比, 结果显示所遴选的美、英、中3个国家的6所高校, 对标吻合度平均值达到86.8%。【局限】所提出的消歧规则和算法在处理文献特征表达形式多样性方面有待进一步细化和提升;需对具体数据源数据情况进行分析,以选择合适的算法模型。【结论】本研究提出了多源异构数据汇聚融合方法, 设计了科研实体消歧规则和算法, 能够有效实现名称规范数据库建设的规范性和全面性。

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王峰
张建勇
钱力
于倩倩
董智鹏
黄永文
刘建华
郭舒
关键词 名称规范期刊规范机构规范基金规范作者规范    
Abstract

[Objective] This paper aims to construct name authority for authors, institutions, journals, and funding, etc. [Methods] First, we loaded, cleansed, transformed, integrated and merged names from multiple sources to create uniform structured data with unique identifiers. Then, we used the metadata model for name authority to extract research entities and relationships among them. Finally, we proposed disambiguation algorithms, such as Levenshtein Distance, Jaccard, word2vec and CNN, for different research entities. [Results] Our study created name authority databases for authors (23 million records), institutions (2.6 million records), journals (30,000 records), and funding (2 million records). We chose six institutions’ names from NSTL and compared them with those from Incites. We found the average precision reached 86.8%. [Limitations] The proposed disambiguation strategies and algorithms need to be further refined and improved in dealing with the diverse expressions of selected disambiguation feature. The analysis of data from different data sources are needed, in order to apply appropriate algorithms. [Conclusions] The proposed method and disambiguation strategies could improve the performance and comprehensiveness of databases for name authority.

Key wordsName Authority    Journal Authority    Institution Authority    Fund Authority    Author Authority
收稿日期: 2018-12-03      出版日期: 2019-03-04
基金资助:*本文系国家科技图书文献中心(NSTL)资助项目“名称规范数据库建设”(项目编号: 科1817)、中国科学院文献情报中心青年人才领域前沿项目“基于深度学习的名称规范方法研究”(项目编号: G180171001)和中国科学院文献情报中心重点任务专项“科研人员研究方向和研究重点分析”(项目编号: 院1643)的研究成果之一
引用本文:   
张建勇,钱力,于倩倩,董智鹏,黄永文,刘建华,郭舒,王峰. 科研实体名称规范的研究与实践*[J]. 数据分析与知识发现, 2019, 3(1): 27-37.
Jianyong Zhang,Li Qian,Qianqian Yu,Zhipeng Dong,Yongwen Huang,Jianhua Liu,Shu Guo,Feng Wang. Constructing Name Authority for Research Entities. Data Analysis and Knowledge Discovery, 2019, 3(1): 27-37.
链接本文:  
https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2018.1363      或      https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2019/V3/I1/27
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