知识组织

科研人员学术专长的细粒度描述模型及实证研究

  • 陈翀 ,
  • 王嘉怡 ,
  • 高欣妍 ,
  • 宣羽菲
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  • 北京师范大学政府管理学院 北京 100875
陈翀,教授,博士,E-mail:chenchong@bnu.edu.cn;王嘉怡,本科生;高欣妍,本科生;宣羽菲,本科生。

收稿日期: 2023-02-10

  修回日期: 2023-05-24

  网络出版日期: 2023-09-11

基金资助

本文系国家社会科学基金一般项目“面向科研人员定量评价的多维学术专长识别及属性度量研究”(项目编号:21BTQ065)研究成果之一。

Fine-Grained Description Model on Academic Expertise of Scientific Researchers with an Empirical Study

  • Chen Chong ,
  • Wang Jiayi ,
  • Gao Xinyan ,
  • Xuan Yufei
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  • School of Government, Beijing Normal University, Beijing 100875

Received date: 2023-02-10

  Revised date: 2023-05-24

  Online published: 2023-09-11

摘要

[目的/意义] 对科研人员学术专长的细粒度描述,有助于精准地利用他们的知识解决特定问题,改进科研人员画像、专家发现等应用。[方法/过程] 首先提出专长描述的维度、标识词粒度和属性度量问题,将学术专长描述建模为构建个体知识图谱;其次探讨学术专长描述和领域知识图谱之间的关系;最后选取特定领域的代表性个体,在知识实体层面进行细粒度定性定量描述,测试模型的可行性。[结果/结论] 科研人员的学术专长并不等同于研究兴趣,而是包括熟悉的问题域、擅长的方法等多个维度。应当选择有适当概念粒度的知识实体作专长标识词。各专长维度可以在个体内部及群体范畴进行属性的度量。在此提出的学术专长描述模型兼顾定性定量特征,具有结构上的灵活性;在专长维度和属性上细化以往学术画像研究中对科研人员学术特征的表达;从个体知识和领域知识互补的角度有助于扩展知识组织理论研究。

本文引用格式

陈翀 , 王嘉怡 , 高欣妍 , 宣羽菲 . 科研人员学术专长的细粒度描述模型及实证研究[J]. 图书情报工作, 2023 , 67(17) : 118 -128 . DOI: 10.13266/j.issn.0252-3116.2023.17.010

Abstract

[Purpose/Significance] Modeling the academic expertise of scientific researchers in a fine-grained way contributes to utilize their expertise knowledge exactly to solve problems, and enhance the precision of academic profiling or find expert. [Method/Process] Firstly, the issue of description aspects on academic expertise, the granularity of annotation, and the measure of attributes of expertise are proposed, and the academic expertise description is modeled as building the individual knowledge maps. Secondly, the relationship between the academic expertise description and the domain knowledge maps is discussed. Finally, in order to test the feasibility of the model, a representative individual of a specific domain is described qualitatively and quantitatively in a fine-grained way to the depth of knowledge entities. [Result/Conclusion] The academic expertise of scientific researchers is not equivalent to their research interests; rather, it includes multiple aspects such as the familiar problem domains, the adept methodologies, etc. The selected knowledge entities as the academic expertise description should be in appropriate conceptual granularity. Each aspect is measured with attributes within individuals or across peer researchers. The academic expertise description model proposed in this article takes into account both qualitative and quantitative characteristics so that the model is flexible in the structure. The academic characteristics of scientific researchers are refined comparing with those academic profile studies in terms of the academic aspects and attributes. Our study can help to extend the theoretical research on knowledge organization from the perspective of complementation of individual knowledge and domain knowledge.

参考文献

[1] 王东, 李青, 张志刚, 等. 科研人员画像构建方法研究[J]. 情报学报, 2022, 41(8):812-821. (WANG D, LI Q, ZHANG Z G, et al. Research on the researcher profile method[J]. Journal of the China Society for Scientific and Technical Information, 2022, 41(8):812-821.)
[2] 张亚楠, 黄晶丽, 王刚. 考虑全局和局部信息的科研人员科研行为立体精准画像构建方法[J]. 情报学报, 2019, 38(10):1012-1021. (ZHANG Y N, HUANG J L, WANG G. A method considering local and global information for constructing stereoscopic and accurate portraits of scientific researchers[J]. Journal of the China Society for Scientific and Technical Information, 2019, 38(10):1012-1021.)
[3] 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. (FAN X Y, DOU Y X, ZHAO P W, 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.)
[4] 王世奇, 刘智锋, 王继民. 学者画像研究综述[J]. 图书情报工作, 2022, 66(20):73-81. (WANG S Q, LIU Z F, WANG J M. A review of scholar profiling research[J]. Library and information service, 2022, 66(20):73-81.)
[5] 袁莎, 唐杰, 顾晓韬. 开放互联网中的学者画像技术综述[J]. 计算机研究与发展, 2018, 55(9):1903-1919. (YUAN S, TANG J, GU X T. A survey on scholar profiling techniques in the open internet[J]. Journal of computer research and development, 2018, 55(9):1903-1919.)
[6] 王梓森, 梁英, 刘政君, 等. 科研项目同行评议专家学术专长匹配方法[J]. 计算机应用, 2021, 41(8):2418-2426. (WANG Z S, LIANG Y, LIU Z J, et al. Matching method for academic expertise of research project peer review experts[J]. Journal of computer applications, 2021, 41(8):2418-2426.)
[7] 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. (CHEN C, LI N, LIANG B, et al. Identifying expertise tags of scholars by multiple features of academic publications[J]. Library and information service, 2019, 63(20):96-103.)
[8] 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. (ZHANG X J, LU W, CHENG Q K. 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.)
[9] 宋培彦, 龙晨翔, 倪雪宁, 等. 基于冰山模型的科研人员学术专长识别方法研究[J/OL]. 数据分析与知识发现:1-15[2023-07-05]. http://kns.cnki.net/kcms/detail/10.1478.G2.20221026.0906.002.html. (SONG P Y, LONG C X, NIX N, et al. A method of how to identify academic expertise of researchers based on iceberg model[J/OL]. Data analysis and knowledge discovery:1-15[2023-07-05]. http://kns.cnki.net/kcms/detail/10.1478.G2.20221026.0906.002.html.)
[10] 石湘, 刘萍. 学者研究兴趣识别综述[J]. 数据分析与知识发现, 2022, 6(4):16-27. (SHI X, LIU P. Review of studies identifying research interests[J]. Data analysis and knowledge discovery, 2022, 6(4):16-27.)
[11] 池雪花, 刘丽帆, 章成志. 基于学术论文的学者研究兴趣标签发现研究[J]. 情报工程, 2019, 5(2):28-39.(CHI X H, LIU L F, ZHANG C Z. Analysis of scholars research interest tag discovery based on academic papers[J]. Technology intelligence engineering, 2019, 5(2):28-39.)
[12] ERICSSON K A, CHARNESS N, et al. The cambridge handbook of expertise and expert performance[M]. New York:Cambridge University Press, 2018.
[13] PETKOVA D, CROFT W B. Hierarchical language models for expert finding in enterprise corpora[J]. International journal on artificial intelligence tools, 2008, 17(1):5-18.
[14] BALOG K, AZZOPARDI L, RIJKE M D. Formal models for expert finding in enterprise corpora[C]//Association for Computing Machinery. Proceedings of the 29th annual international ACM SIGIR conference on research and development in information retrieval. New York:Association for Computing Machinery, 2006:43-50.
[15] XIA F, WANG W, BEKELE T M, et al. Big scholarly data:a survey[J]. IEEE transactions on big data, 2017, 3(1):18-35.
[16] 温昂展. 基于多源异构大数据的学者用户画像关键技术研究[D]. 广州:华南理工大学, 2018. (WEN A Z. Study on the key technology of scholarly user profile based on multi-source and heterogenous big data[D]. Guangzhou:South China University of Technology, 2018.)
[17] 中国大百科全书(第三版)网络版. 专家知识[EB/OL].[2023-08-04]. https://www.zgbk.com/ecph/words?SiteID=1&ID=135276&Type=bkzyb&SubID=104094. (Encyclopedia of China (3rd ed.) Network ed. Expertise knowledge[EB/OL].[2023-08-04]. https://www.zgbk.com/ecph/words?SiteID=1&ID=135276&Type=bkzyb&SubID=104094.)
[18] 周园春, 王卫军, 乔子越, 等. 科技大数据知识图谱构建方法及应用研究综述[J]. 中国科学:信息科学, 2020, 50(7):957-987. (ZHOU Y C, WANG W J, QIAO Z Y, et al. A survey on the construction methods and applications of sci-tech big data knowledge graph[J]. Science in China (Information Sciences), 2020, 50(7):957-987.)
[19] 李贺, 杜杏叶. 基于知识元的学术论文内容创新性智能化评价研究[J]. 图书情报工作, 2020, 64(1):93-104. (LI H, DU X Y. Research on intelligent evaluation for the content innovation of academic papers[J]. Library and information service, 2020, 64(1):93-104.)
[20] ZHANG L, KOPAK R W, FREUND L, et al. A taxonomy of functional units for information use of scholarly journal articles[J]. Proceedings of the American society for information science and technology, 2010, 47(1):1-10.
[21] SPAR Ontologies. The discourse element ontology[EB/OL].[2023-07-05]. http://www.sparontologies.net/ontologies/deo.
[22] 王晓光, 李梦琳, 宋宁远. 科学论文功能单元本体设计与标引应用实验[J]. 中国图书馆学报, 2018, 44(4):73-88. (WANG X G, LI M L, SONG N Y. Design and application of scientific paper functional units ontology[J]. Journal of Library Science in China, 2018, 44(4):73-88.)
[23] KONDO T, NANBA H, TAKEZAWA T, et al. Technical trend analysis by analyzing research papers'titles[C]//CHATTERJEE S, DEV P. Proceedings of the 4th language and technology conference (LTC 2009). Heidelberg:Association for Computing Machinery, 2009:512-521.
[24] 刘智锋, 李信, 程齐凯, 等. 学术文本关键词语义功能数据集构建与分析——以Journal of Informetrics为例[J]. 图书馆论坛, 2019, 39(7):64-74. (LIU Z F, LI X, CHENG Q K, et al. Construction and analysis of semantic data sets for academic text keywords[J]. Library tribune, 2019, 39(7):64-74.)
[25] 章成志, 张颖怡. 基于学术论文全文的研究方法实体自动识别研究[J]. 情报学报, 2020, 39(6):589-600. (ZHANG C Z, ZHANG Y Y. Automatic recognition of research methods from the full-text of academic articles[J]. Journal of the China society for scientific and technical information, 2020, 39(6):589-600.)
[26] 程齐凯, 李鹏程, 张国标, 等. 学术文本词汇功能识别——基于标题生成策略和注意力机制的问题方法抽取[J]. 情报学报, 2021, 40(1):43-52. (CHENG Q K, LI P C, ZHANG G B, et al. Recognition of lexical functions in academic texts:problem method extraction based on title generation strategy and attention mechanism[J]. Journal of the China society for scientific and technical information, 2021, 40(1):43-52.)
[27] WANG Y, ZHANG C, LI K. A review on method entities in the academic literature:extraction, evaluation, and application[J]. Scientometrics, 2022, 127(5):2479-2520.
[28] WANG R, ZHANG C, ZHANG Y, et al. Extracting methodological sentences from unstructured abstracts of academic articles[C]//Proceedings of iconference 2020. Sweden:Springer Cham, 2020:790-798.
[29] 黄永, 陆伟, 程齐凯. 学术文本的结构功能识别——基于章节内容的识别[J]. 情报学报, 2016, 35(3):293-300. (HUANG Y, LU W, CHENG Q K. The structure function recognition of academic text:chapter content based recognition[J]. Journal of the China society for scientific and technical information, 2016, 35(3):293-300.)
[30] 王平, 辜希武, 赵慧慧. 基于多重关系异构网络的学术实体权威度评估方法研究[J]. 情报学报, 2014, 33(8):872-882. (WANG P, GU X W, ZHAO H H. Research on the evaluation of authoritativeness for academic entities based on multi-relationship heterogeneous network[J]. Journal of the China Society for Scientific and Technical Information, 2014, 33(8):872-882.)
[31] 张燕, 赵婉忻, 董凯. 基于他引频次和贡献率的学者影响力评价[J]. 情报理论与实践, 2021, 44(10):65-71. (ZHANG Y, ZHAO W X, DONG K. Scholar influence evaluation research based on external citations and contribution[J]. Information studies:theory & application, 2021, 44(10):65-71.)
[32] 高志, 张志强. 个人学术影响力定量评价方法研究综述[J]. 情报理论与实践, 2016, 39(1):133-138. (GAO Z, ZHANG Z Q. Review of quantitative evaluation method of individual academic influence[J]. Information studies:theory & application, 2016, 39(1):133-138.)
[33] 张宝隆, 王昊, 李心蕾. 基于TDC的学者差异性测度及创新能力分析[J]. 情报理论与实践, 2021, 44(7):138-144. (ZHANG B L, WANG H, LI X L. TDC-based scholar differentiation measurement and innovation capability analysis[J]. Information studies:theory & application, 2021, 44(7):138-144.)
[34] 陆伟, 罗卓然, 李信. 科技创新评价研究进展[J]. 情报学进展, 2022, 14:158-187. (LU W, LUO Z R, LI X. Research progress of science and technology innovation[J]. Advances in information science, 2022, 14:158-187.)
[35] CHEN L, FANG H. An automatic method for extracting innovative ideas based on the scopus database[J]. Knowledge organization:KO, 2019, 46(3):171-186.
[36] LI X, TUR G, HAKKANI-TUR D, et al. Personal knowledge graph population from user utterances in conversational understanding[C]//AKBACAK M, HANSEN J. Proceedings of 2014 IEEE spoken language technology workshop. Nevada:Conference Management Services, 2014:224-229.
[37] BALOG K, KENTER T. Personal knowledge graphs:a research agenda[C]//FANG Y, ZHANG Y. Proceedings of the 2019 ACM SIGIR international conference on theory of information retrieval. London:Association for Computing Machinery, 2019:217-220.
[38] 胡志伟, 裴雷. 基于自述研究专长的研究领域识别与特征差异分析——以国内图书情报与档案管理专业教师为样本[J]. 文献与数据学报, 2020, 2(4):40-48. (HU Z W, PEI L. Researchfield identification and characteristics-difference analysis based on self-reported expertise in CVs:evidence from 1016 faculties in 52 Chinese LIS schools[J]. Journal of Library and Data, 2020, 2(4):40-48.)
[39] CHENG Y, QIU G, BU J, et al. Model bloggers'interests based on forgetting mechanism[C]//HUAI J P, CHEN R, HSIAO W H, et al. Proceedings of the international con-reference on World Wide Web. New York:Association for Computing Machinery, 2008:1129-1130.
[40] CHEN C, LUO P. Enhancing navigability:an algorithm for constructing tag trees[J]. Journal of data and information science. 2017, 2(2):56-75.
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