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Identifying Academic Expertise of Researchers Based on Iceberg Model |
Song Peiyan,Long Chenxiang(),Li Yiran,Ni Xuening |
Management School, Tianjin Normal University, Tianjin 300382, China |
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Abstract [Objective] This paper aims to automatically identify the academic expertise of researchers, which improves the research project evaluation and talent assessment. [Methods] Firstly, we adopted the Iceberg Model to describe the academic expertise of researchers. The visible part of the “iceberg” reveals the researchers’ areas of expertise and specialization, which identify their core competencies and main research directions. The lower part of the “iceberg” indicates the “comparative advantages” of researchers’ expertise. Then, we used labels to represent researchers’ expertise and utilized machine learning techniques such as LDA and BERT to extract, cluster, and generate matrices of academic labels. Finally, we proposed the self-focus and the peer-relative indexes to identify the researchers’ main areas and relative position in the scientific community. [Results] Using a sample of 20 researchers, we generated 8,985 sets of label words and their weights and described researchers’ expertise at a fine-grained level. And then, the “Self-Focus Index” and the “Peer-relative Index” were calculated based on the domain-researcher matrix (40×20). We found the proposed method can accurately reflect researchers’ expertise in specific research areas and relative positions within the scientific community. [Limitations] Future work should consider incorporating the temporal factor to capture the temporal evolution characteristics of researchers’ academic expertise. [Conclusions] The advantages of the proposed method are twofold. Firstly, the iceberg model effectively explains what researchers do and how well they do it. The model provides a theoretical basis for label extraction, index design, and enhancing interpretability. Secondly, in addition to quantifiable comparative expertise index calculations, the method achieves fine-grained, precise, and dynamic talent expertise profiling.
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Received: 28 May 2022
Published: 09 August 2023
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Fund:National Social Science Fund of China(21BTQ061) |
Corresponding Authors:
Long Chenxiang,ORCID:0000-0002-1549-6542,E-mail:l1769069529@126.com。
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[1] |
熊回香, 叶佳鑫, 丁玲, 等. 基于改进的h指数的学者评价研究[J]. 情报学报, 2019, 38(10): 1022-1029.
|
[1] |
(Xiong Huixiang, Ye Jiaxin, Ding Ling, et al. Scholar Evaluation Research Based on an Improved h-Index[J]. Journal of the China Society for Scientific and Technical Information, 2019, 38(10): 1022-1029.)
|
[2] |
王林, 潘陈益, 朱文静. 基于h指数、g指数和p指数的微博影响力评价对比研究[J]. 现代情报, 2018, 38(6): 11-18, 61.
doi: 10.3969/j.issn.1008-0821.2018.06.002
|
[2] |
(Wang Lin, Pan Chenyi, Zhu Wenjing. Comparative Research on the Evaluation of Microblogs’ Impact Based on h-Index, g-Index and p-Index[J]. Journal of Modern Information, 2018, 38(6): 11-18, 61.)
doi: 10.3969/j.issn.1008-0821.2018.06.002
|
[3] |
隋桂玲. p指数和h指数学术影响力评价对比的理论和实证研究[J]. 情报杂志, 2020, 39(4): 153-160.
|
[3] |
(Sui Guiling. Theoretical and Empirical Research on the Comparison of Academic Influence Evaluation of p-Index and h-Index[J]. Journal of Intelligence, 2020, 39(4): 153-160.)
|
[4] |
宋培彦, 程志强. 肿瘤领域专家学术影响力评价方法及其实证研究[J]. 情报工程, 2018, 4(3): 48-57.
|
[4] |
(Song Peiyan, Cheng Zhiqiang. Method of Experts Academic Influence Evaluation in the Field of Oncology: An Empirical Study[J]. Technology Intelligence Engineering, 2018, 4(3): 48-57.)
|
[5] |
袁国华, 寇晶晶, 张建勇, 等. 基于开放同行评议的学者影响力评价研究——以F1000为例[J]. 图书情报工作, 2018, 62(13): 37-44.
doi: 10.13266/j.issn.0252-3116.2018.13.006
|
[5] |
(Yuan Guohua, Kou Jingjing, Zhang Jianyong, et al. The Research of Scholar Influence Evaluation Based on Open Peer Review: Take the F1000 as an Example[J]. Library and Information Service, 2018, 62(13): 37-44.)
doi: 10.13266/j.issn.0252-3116.2018.13.006
|
[6] |
王炎, 魏瑞斌. 基于多数据源的专家学术网络构建研究[J]. 情报杂志, 2016, 35(12): 121-126, 138.
|
[6] |
(Wang Yan, Wei Ruibin. To Build the Expert Academic Network Based on the Multi-data Source[J]. Journal of Intelligence, 2016, 35(12): 121-126, 138.)
|
[7] |
McClelland D C. Testing for Competence Rather Than for Intelligence.[J]. American Psychologist, 1973, 28(1): 1-14.
doi: 10.1037/h0034092
pmid: 4684069
|
[8] |
徐曾旭林, 谢靖, 于倩倩. 人才多元评价模型设计方法研究[J]. 数据分析与知识发现, 2021, 5(8): 122-131.
|
[8] |
(Xu Zengxulin, Xie Jing, Yu Qianqian. Research on Design Method of Multi-Evaluation Model for Talents[J]. Data Analysis and Knowledge Discovery, 2021, 5(8): 122-131.)
|
[9] |
宋雪雁, 李溪萌, 邓君. 数字时代档案文献编纂人员胜任力模型研究[J]. 图书情报工作, 2020, 64(3): 32-41.
doi: 10.13266/j.issn.0252-3116.2020.03.004
|
[9] |
(Song Xueyan, Li Ximeng, Deng Jun. Research on Competency Model of Archival Document Compilers in the Digital Age[J]. Library and Information Service, 2020, 64(3): 32-41.)
doi: 10.13266/j.issn.0252-3116.2020.03.004
|
[10] |
邹凯, 徐萍萍, 郭一航, 等. 大数据背景下高校信息管理类人才胜任力素质模型构建[J]. 情报理论与实践, 2021, 44(12): 55-64, 18.
|
[10] |
(Zou Kai, Xu Pingping, Guo Yihang, et al. Construction of Competency Model of Information Management Talents in Universities under the Background of Big Data[J]. Information Studies: Theory & Application, 2021, 44(12): 55-64, 18.)
|
[11] |
宋新平, 李慧, 熊强, 等. 大数据下企业竞争情报人员胜任力模型研究[J]. 现代情报, 2020, 40(5): 88-95.
doi: 10.3969/j.issn.1008-0821.2020.05.011
|
[11] |
(Song Xinping, Li Hui, Xiong Qiang, et al. Research on Competency Model of Enterprise Competitive Intelligence Personnel under Big Data[J]. Journal of Modern Information, 2020, 40(5): 88-95.)
doi: 10.3969/j.issn.1008-0821.2020.05.011
|
[12] |
Hu W, Ding K, Gu L, et al. Research on the Competency Model of Chancellors in Charge of Scientific Research in Chinese Research-Oriented Universities[J]. Journal of Scientometric Research, 2014, 3(3): 104-110.
doi: 10.4103/2320-0057.153552
|
[13] |
Klendauer R, Berkovich M, Gelvin R, et al. Towards a Competency Model for Requirements Analysts[J]. Information Systems Journal, 2012, 22(6): 475-503.
doi: 10.1111/isj.2012.22.issue-6
|
[14] |
庆海涛, 陈媛媛, 关琳, 等. 智库专家胜任力模型构建[J]. 图书馆论坛, 2016, 36(5): 34-39.
|
[14] |
(Qing Haitao, Chen Yuanyuan, Guan Lin, et al. Competency Model of Think-Tank Experts[J]. Library Tribune, 2016, 36(5): 34-39.)
|
[15] |
万健, 罗园晶, 茆意宏. 图书馆员知识咨询胜任力模型构建[J]. 图书情报工作, 2016, 60(20): 27-35.
doi: 10.13266/j.issn.0252-3116.2016.20.004
|
[15] |
(Wan Jian, Luo Yuanjing, Mao Yihong. The Construction of the Competency Model of Librarians’ Knowledge Consultation[J]. Library and Information Service, 2016, 60(20): 27-35.)
doi: 10.13266/j.issn.0252-3116.2016.20.004
|
[16] |
中华人民共和国国家质量监督检验检疫总局, 中国国家标准化管理委员会. 科技人才元数据元素集: GB/T 35397—2017[S]. 北京: 中国标准出版社, 2017.
|
[16] |
(General Administration of Quality Supervision, Inspection and Quarantine of the People’s Republic of China, Standardization Administration of the People’s Republic of China. Research and Development Talent Metadata Element Set: GB/T 35397—2017[S]. Beijing: Standards Press of China, 2017.)
|
[17] |
贾君枝, 崔西燕. 人物本体词表之间的互操作及分类体系构建[J]. 情报学报, 2019, 38(7): 731-741.
|
[17] |
(Jia Junzhi, Cui Xiyan. Interoperability Between Ontological Word Lists of Persons and Construction of Classification Systems[J]. Journal of the China Society for Scientific and Technical Information, 2019, 38(7): 731-741.)
|
[18] |
陆伟, 刘杰, 秦喜艳. 基于专长词表的图情领域专家检索与评价[J]. 中国图书馆学报, 2010, 36(2): 70-76.
|
[18] |
(Lu Wei, Liu Jie, Qin Xiyan. Expert Search and Evaluation Based on Expertise Vocabulary in the Field of Library and Information Science[J]. Journal of Library Science in China, 2010, 36(2): 70-76.)
|
[19] |
胡月红, 刘萍. 基于本体概念的专长表示研究[J]. 图书情报工作, 2012, 56(4): 17-21, 40.
|
[19] |
(Hu Yuehong, Liu Ping. An Ontology Based Approach for Expertise Representation[J]. Library and Information Service, 2012, 56(4): 17-21, 40.)
|
[20] |
陈翀, 李楠, 梁冰, 等. 基于成果特征的学者学术专长识别方法[J]. 图书情报工作, 2019, 63(20): 96-103.
doi: 10.13266/j.issn.0252-3116.2019.20.011
|
[20] |
(Chen Chong, Li Nan, Liang Bing, et al. Identifying Expertise Tags of Scholars by Multiple Features of Academic Publications[J]. Library and Information Service, 2019, 63(20): 96-103.)
doi: 10.13266/j.issn.0252-3116.2019.20.011
|
[21] |
刘萍, 周梦欢. 基于共词网络的专家专长挖掘[J]. 情报科学, 2012, 30(12): 1815-1819.
|
[21] |
(Liu Ping, Zhou Menghuan. Expertise Identification Based on Co-word Network[J]. Information Science, 2012, 30(12): 1815-1819.)
|
[22] |
刘晓豫, 朱东华, 汪雪锋, 等. 多专长专家识别方法研究——以大数据领域为例[J]. 图书情报工作, 2018, 62(3): 55-63.
doi: 10.13266/j.issn.0252-3116.2018.03.007
|
[22] |
(Liu Xiaoyu, Zhu Donghua, Wang Xuefeng, et al. Multi-expertise Researcher Identification: A Case Study of the Big Data[J]. Library and Information Service, 2018, 62(3): 55-63.)
doi: 10.13266/j.issn.0252-3116.2018.03.007
|
[23] |
张晓娟, 陆伟, 程齐凯. PLSA在图情领域专家专长识别中的应用[J]. 现代图书情报技术, 2012(2): 76-81.
|
[23] |
(Zhang Xiaojuan, Lu Wei, Cheng Qikai. Application of PLSA on Expertise Identifying in the Field of Library and Information Science[J]. New Technology of Library and Information Service, 2012(2): 76-81.)
|
[24] |
赵辉, 化柏林, 何鸿魏. 科技情报用户画像标签生成与推荐[J]. 情报学报, 2020, 39(11): 1214-1222.
|
[24] |
(Zhao Hui, Hua Bolin, He Hongwei. User Profile Tag Generation and Information Recommendations for Science and Tencnology Intelligence[J]. Journal of the China Society for Scientific and Technical Information, 2020, 39(11): 1214-1222.)
|
[25] |
聂卉. 结合词向量和词图算法的用户兴趣建模研究[J]. 数据分析与知识发现, 2019, 3(12): 30-40.
|
[25] |
(Nie Hui. Modeling Users with Word Vector and Term-Graph Algorithm[J]. Data Analysis and Knowledge Discovery, 2019, 3(12): 30-40.)
|
[26] |
夏立新, 曾杰妍, 毕崇武, 等. 基于LDA主题模型的用户兴趣层级演化研究[J]. 数据分析与知识发现, 2019, 3(7): 1-13.
|
[26] |
(Xia Lixin, Zeng Jieyan, Bi Chongwu, et al. Identifying Hierarchy Evolution of User Interests with LDA Topic Model[J]. Data Analysis and Knowledge Discovery, 2019, 3(7): 1-13.)
|
[27] |
范晓玉, 窦永香, 赵捧未, 等. 融合多源数据的科研人员画像构建方法研究[J]. 图书情报工作, 2018, 62(15): 31-40.
doi: 10.13266/j.issn.0252-3116.2018.15.004
|
[27] |
(Fan Xiaoyu, Dou Yongxiang, Zhao Pengwei, et al. Study for the Construction Method of Scientist Profile with Multi-Source Data Fusion[J]. Library and Information Service, 2018, 62(15): 31-40.)
doi: 10.13266/j.issn.0252-3116.2018.15.004
|
[28] |
Jeong Y S, Lee S H, Gweon G. Discovery of Research Interests of Authors over Time Using a Topic Model[C]// Proceedings of the International Conference on Big Data and Smart Computing. 2016: 24-31.
|
[29] |
Kang S, Cheng N C. Internet-Based Researcher Interest Mining[C]// Proceedings of the 6th International Conference on Dependable Systems and Their Applications. 2020: 1-12.
|
[30] |
Daud A. Using Time Topic Modeling for Semantics-Based Dynamic Research Interest Finding[J]. Knowledge-Based Systems, 2012, 26: 154-163.
doi: 10.1016/j.knosys.2011.07.015
|
[31] |
Dehghan M, Biabani M, Abin A A. Temporal Expert Profiling: With an Application to T-shaped Expert Finding[J]. Information Processing & Management, 2019, 56(3): 1067-1079.
doi: 10.1016/j.ipm.2019.02.017
|
[32] |
de Campos L M, Fernández -Luna J M, Huete J F, et al. LDA-Based Term Profiles for Expert Finding in a Political Setting[J]. Journal of Intelligent Information Systems, 2021, 56(3): 529-559.
doi: 10.1007/s10844-021-00636-x
|
[33] |
Jr Spencer L M, Spencer S M. Competence at Work: Models for Superior Performance[M]. New York: Wiley, 1993.
|
[34] |
闫淑敏, 杨小丽. 基于扎根理论的高校科研人员创新动力研究[J]. 科技管理研究, 2019, 39(1): 39-45.
|
[34] |
(Yan Shumin, Yang Xiaoli. Research on Innovation Motivation of Scientific Research Personnel in Colleges and Universities Based on Grounded Theory[J]. Science and Technology Management Research, 2019, 39(1): 39-45.)
|
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