A Review on Methods for Domain Knowledge Evolution Analysis
Li Xuesi1,2,Zhang Zhixiong1,2(),Wang Yufei1,2,Liu Yi1
1National Science Library, Chinese Academy of Sciences, Beijing 100190, China 2Department of Information Resources Management, School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China
[Objective] Domain knowledge evolution analysis has been a long-standing research topic in the field of Library and Information Science. This paper provides a comprehensive review of the research methods related to the domain knowledge evolution analysis, both nationally and internationally, aiming to offer valuable references for future studies in this area. [Coverage] We conducted searches in CNKI and Web of Science using keywords related to domain knowledge evolution. The search results were manually evaluated and analyzed, and a total of 84 key literatures closely related to the methods of domain knowledge evolution analysis were selected for review. [Methods] By reviewing the research literature, we clarified the relevant concepts of domain knowledge evolution. Based on this, we classified the existing domain knowledge evolution analysis methods into three categories: citation-based, structure-based and content-based. For each category, we first elucidated the theoretical basis, then explained their basic analytical frameworks and highlighted the relevant advances. Finally, we summarized the existing methods of domain knowledge evolution analysis and provided perspectives. [Results] The three categories of existing methods for domain knowledge evolution analysis rely on their respective scientific theories. With the advancement of technology and the improvement of data resources, these methods are continuously deepening and improving the analytical framework for the study of evolution. Although significant research achievements have been made, there has been no breakthrough in the research perspective of knowledge evolution analysis, and the limitations within the current research paradigm remain unresolved. [Limitations] The review analysis was based on selected literature, which may not have comprehensively covered all relevant research. [Conclusions] Based on the summary and analysis of the current research, we believe that the following two directions are worth focusing on in the future research on domain knowledge evolution analysis: first, exploring new entry points for domain knowledge evolution analysis, and second, attempting to integrate existing research methods to improve the limitations of current analytical approaches.
李雪思, 张智雄, 王宇飞, 刘熠. 领域知识演化分析方法综述*[J]. 数据分析与知识发现, 2024, 8(1): 1-15.
Li Xuesi, Zhang Zhixiong, Wang Yufei, Liu Yi. A Review on Methods for Domain Knowledge Evolution Analysis. Data Analysis and Knowledge Discovery, 2024, 8(1): 1-15.
(Wang Qian, Qian Li, Liu Xiwen. A Review on Technical Methods for Knowledge Evolution Analysis[J]. Library and Information Service, 2023, 67(7): 121-134.)
doi: 10.13266/j.issn.0252-3116.2023.07.011
[3]
de Solla Price D J. Quantitative Measures of the Development of Science[J]. Archives Internationales d’Histoire des Sciences, 1951, 4(14):85-93.
[4]
Garfield E. Citation Indexes for Science. A New Dimension in Documentation Through Association of Ideas[J]. International Journal of Epidemiology, 2006, 35(5): 1123-1127.
pmid: 16987841
[5]
杨中楷, 梁永霞. 专利引文分析的理论与实践[M]. 第1版. 北京: 科学出版社, 2017.
[5]
(Yang Zhongkai, Liang Yongxia. The Theory and Practice of Patent Citation Analysis[M]. The 1st Edition. Beijing: Science Press, 2017.)
(Liang Yongxia, Liu Zeyuan, Yang Zhongkai. Explore and Discussion on Knowledge Flowing Theory of Citation Analytics[J]. Studies in Science of Science, 2010, 28(5): 668-674.)
[7]
Small H. Co-Citation in the Scientific Literature: A New Measure of the Relationship Between Two Documents[J]. Journal of the American Society for Information Science, 1973, 24(4): 265-269.
doi: 10.1002/asi.v24:4
(Yang Ruixian, Jiang Xiaohan. The Knowledge Structure and Evolution of Interdisciplinary Subjects from the Citation Perspective of Discipline and Journals: A Case Study of Library and Information Science[J]. Library and Information Service, 2018, 62(5): 30-39.)
doi: 10.13266/j.issn.0252-3116.2018.05.004
(Li Qiao. The Impact of Chinese Library and Information Science Knowledge from the Perspective of External Citations[J]. Journal of Library Science in China, 2019, 45(6): 65-83.)
(Mo Xueying, Lu Long. Research on the Relationship Between Knowledge Fusion and Papers’ Social Impact from the Perspective of Citation[J]. Journal of Information Resources Management, 2022, 12(6): 133-141.)
[11]
González-Alcaide G, Llorente P, Ramos J M. Bibliometric Indicators to Identify Emerging Research Fields: Publications on Mass Gatherings[J]. Scientometrics, 2016, 109(2): 1283-1298.
doi: 10.1007/s11192-016-2083-2
[12]
Sun Y, Latora V. The Evolution of Knowledge Within and Across Fields in Modern Physics[J]. Scientific Reports, 2020, 10: Article No.12097.
[13]
Marshakova-Shaikevich I. System of Document Connections Based on References[J]. Nauchno-Tekhnicheskaya Informatsiya Seriya 2, 1973(6): 3-8.
[14]
Small H, Griffith B C. The Structure of Scientific Literatures I: Identifying and Graphing Specialties[J]. Science Studies, 1974, 4(1): 17-40.
doi: 10.1177/030631277400400102
[15]
Griffith B C, Small H G, Stonehill J A, et al. The Structure of Scientific Literatures II: Toward a Macro- and Microstructure for Science[J]. Science Studies, 1974, 4(4): 339-365.
doi: 10.1177/030631277400400402
[16]
Small H. Macro-Level Changes in the Structure of Co-Citation Clusters: 1983-1989[J]. Scientometrics, 1993, 26(1): 5-20.
doi: 10.1007/BF02016789
[17]
Small H. Visualizing Science by Citation Mapping[J]. Journal of the American Society for Information Science, 1999, 50(9): 799-813.
doi: 10.1002/(SICI)1097-4571(1999)50:9<>1.0.CO;2-J
[18]
Li X R, Qiao H, Wang S Y. Exploring Evolution and Emerging Trends in Business Model Study: A Co-Citation Analysis[J]. Scientometrics, 2017, 111(2): 869-887.
doi: 10.1007/s11192-017-2266-5
(Hou Yuefang, Cui Lei, Wu Di. Study on the History and the Current Work of a Subject by Combined Co-Citation Cluster and Content Words Analysis[J]. Journal of the China Society for Scientific and Technical Information, 2007, 26 (2): 309-314.)
[20]
Pilkington A, Meredith J. The Evolution of the Intellectual Structure of Operations Management—1980-2006: A Citation/Co-Citation Analysis[J]. Journal of Operations Management, 2009, 27(3): 185-202.
doi: 10.1016/j.jom.2008.08.001
[21]
White H D, Griffith B C. Author Cocitation: A Literature Measure of Intellectual Structure[J]. Journal of the American Society for Information Science, 1981, 32(3): 163-171.
doi: 10.1002/asi.v32:3
(Qiu Junping, Zhang Xiaopei. Author Co-Citation Analysis of Knowledge Management in China Based on the CSSCI[J]. Information Science, 2011, 29(10): 1441-1445.)
(Lin Chunpei, Huang Haimei, Wu Tungju. Scholar Groups and Evolution of Disruptive Innovation Research Based on Author Co-Citation Analysis[J]. Science of Science and Management of S.& T., 2016, 37(8): 68-81.)
[24]
White H D, McCain K W. Visualizing a Discipline: An Author Co-Citation Analysis of Information Science, 1972-1995[J]. Journal of the American Society for Information Science, 1998, 49(4): 327-355.
[25]
González-Valiente C L, León Santos M, Arencibia-Jorge R, et al. Mapping the Evolution of Intellectual Structure in Information Management Using Author Co-Citation Analysis[J]. Mobile Networks and Applications, 2021, 26(6): 2374-2388.
doi: 10.1007/s11036-019-01231-9
[26]
Ma R M. Discovering and Analyzing the Intellectual Structure and Its Evolution of LIS in China, 1998-2007[J]. Scientometrics, 2012, 93(3): 645-659.
doi: 10.1007/s11192-012-0702-0
[27]
Kessler M M. Bibliographic Coupling Between Scientific Papers[J]. American Documentation, 1963, 14(1): 10-25.
doi: 10.1002/asi.v14:1
[28]
邱均平. 信息计量学[M]. 武汉: 武汉大学出版社, 2007.
[28]
(Qiu Junping. Information Metrology[M]. Wuhan: Wuhan University Press, 2007.)
[29]
Morris S A, Yen G, Wu Z, et al. Time Line Visualization of Research Fronts[J]. Journal of the American Society for Information Science and Technology, 2003, 54(5): 413-422.
doi: 10.1002/asi.v54:5
[30]
Huang M H, Chang C P. Detecting Research Fronts in OLED Field Using Bibliographic Coupling with Sliding Window[J]. Scientometrics, 2014, 98(3): 1721-1744.
doi: 10.1007/s11192-013-1126-1
(Xiao Ming, Li Guojun, Yuan Hao. Visualization Analysis of Research Fronts of Digital Library Based on Bibliographic Coupling Analysis[J]. Library and Information Service, 2010, 54(7): 51-54.)
(Qiu Junping, Liu Guohui. Research Status and Prospect of Coupling Analysis Method in China[J]. Library and Information Service, 2014, 58(7): 131-136.)
[33]
Zhao D Z, Strotmann A. Evolution of Research Activities and Intellectual Influences in Information Science 1996-2005: Introducing Author Bibliographic-Coupling Analysis[J]. Journal of the American Society for Information Science and Technology, 2008, 59(13): 2070-2086.
doi: 10.1002/asi.v59:13
(Ma Ruimin, Ni Chaoqun. Author Coupling Analysis: An Exploratory Study on a New Approach to Discover Intellectual Structure of a Discipline[J]. Journal of Library Science in China, 2012, 38(2): 4-11.)
[35]
Yang S L, Han R Z, Wolfram D, et al. Visualizing the Intellectual Structure of Information Science (2006-2015): Introducing Author Keyword Coupling Analysis[J]. Journal of Informetrics, 2016, 10(1): 132-150.
doi: 10.1016/j.joi.2015.12.003
[36]
Small H. Co-Citation Context Analysis and the Structure of Paradigms[J]. Journal of Documentation, 1980, 36(3): 183-196.
doi: 10.1108/eb026695
[37]
Small H, Greenlee E. Citation Context Analysis of a Co-Citation Cluster: Recombinant-DNA[J]. Scientometrics, 1980, 2(4): 277-301.
doi: 10.1007/BF02016349
(Zhang Jinzhu, Qiu Mengmeng, Wang Qiuyue. Citation Topic Identification and Evolution Based on Citation Content Clustering[J]. Information Science, 2023, 41(2): 107-117.)
[39]
Jebari C, Herrera-Viedma E, Cobo M J. The Use of Citation Context to Detect the Evolution of Research Topics: A Large-Scale Analysis[J]. Scientometrics, 2021, 126(4): 2971-2989.
doi: 10.1007/s11192-020-03858-y
(Zhang Yiman, Ma Xiufeng, Cheng Jiejing. Research of Knowledge Flows Based on Citation Content Analysis[J]. Journal of Intelligence, 2015, 34(11): 50-54.)
[41]
Petrovich E. Accumulation of Knowledge in Para-Scientific Areas: The Case of Analytic Philosophy[J]. Scientometrics, 2018, 116(2): 1123-1151.
doi: 10.1007/s11192-018-2796-5
[42]
Jurgens D, Kumar S, Hoover R, et al. Measuring the Evolution of a Scientific Field Through Citation Frames[J]. Transactions of the Association for Computational Linguistics, 2018, 6: 391-406.
doi: 10.1162/tacl_a_00028
(Chen Yingfang, Ma Xiaolei. Measuring the Developmental Trend of a Knowledge Domain Through Citation Content and Citation Function Analysis[J]. Journal of Intelligence, 2020, 39(3): 71-80.)
(Teng Guangqing, Bi Qiang. Research on the Inner Mechanism and Subject Origin Evolution of Knowledge Linking[J]. Information Studies: Theory & Application, 2010, 33(2): 21-24.)
[45]
Brookes B C. Robert Fairthorne and the Scope of Information Science[J]. Journal of Documentation, 1974, 30(2): 139-152.
doi: 10.1108/eb026572
[46]
Belkin N J, Robertson S E. Information Science and the Phenomenon of Information[J]. Journal of the American Society for Information Science, 1976, 27(4): 197-204.
doi: 10.1002/asi.v27:4
(Teng Guangqing, He Defang, Peng Jie, et al. Structure and Order: The Evolution of Structuralism in the Field of Knowledge Organization[J]. Information Studies: Theory & Application, 2015, 38(4): 6-10.)
[49]
Seufert A, von Krogh G, Bach A. Towards Knowledge Networking[J]. Journal of Knowledge Management, 1999, 3(3): 180-190.
doi: 10.1108/13673279910288608
[50]
Ali Köseoglu M, Parnell J A, Yick M Y Y. Identifying Influential Studies and Maturity Level in Intellectual Structure of Fields: Evidence from Strategic Management[J]. Scientometrics, 2021, 126(2): 1271-1309.
doi: 10.1007/s11192-020-03776-z
[51]
Choi J, Yi S, Lee K C. Analysis of Keyword Networks in MIS Research and Implications for Predicting Knowledge Evolution[J]. Information & Management, 2011, 48(8): 371-381.
doi: 10.1016/j.im.2011.09.004
(Wang Yuefen, Wang Jinshu, Guan Peng. Research on Construction and Evolution Analysis of Discipline Knowledge Network Based on Topics Association[J]. Information Science, 2018, 36(9): 9-15.)
[53]
Lucio-Arias D, Leydesdorff L. Main-Path Analysis and Path-Dependent Transitions in HistCiteTM -Based Historiograms[J]. Journal of the American Society for Information Science and Technology, 2008, 59(12): 1948-1962.
doi: 10.1002/asi.v59:12
(Li Jian, Han Yi. Evolution of Domain Knowledge Diffusion Based on Main Path: A Case Study of Solar Energy[J]. Journal of the China Society for Scientific and Technical Information, 2014, 33(8): 883-891.)
[55]
Xiao Y, Lu L Y Y, Liu J S, et al. Knowledge Diffusion Path Analysis of Data Quality Literature: A Main Path Analysis[J]. Journal of Informetrics, 2014, 8(3): 594-605.
doi: 10.1016/j.joi.2014.05.001
(Zhu Qingsong, Leng Fuhai. Analysis of Topic Evolution Based on Co-Citation of Documents on the Main Citation Path[J]. Journal of the China Society for Scientific and Technical Information, 2014, 33(5): 498-506.)
[57]
Chen L, Xu S, Zhu L J, et al. A Semantic Main Path Analysis Method to Identify Multiple Developmental Trajectories[J]. Journal of Informetrics, 2022, 16(2): Article No.101281.
Guo Qing, Deng Sanhong, Kong Jia, et al. Viewing the Development, Evolution and Expansion of Library and Information Science in China from the Co-Authorship Networks(1998-2018)[J]. Information Studies: Theory & Application, 2021, 44(6): 90-97.)
doi: 10.16353/j.cnki.1000-7490.2021.06.013
[60]
Wang X G, He J, Huang H, et al. MatrixSim: A New Method for Detecting the Evolution Paths of Research Topics[J]. Journal of Informetrics, 2022, 16(4): Article No.101343.
(Chen Xiang, Huang Lu, Ni Xingxing, et al. Identifying Topic Evolutionary Pathways Through Dynamic Semantic Network Analytics[J]. Journal of the China Society for Scientific and Technical Information, 2021, 40(5): 500-512.)
(Zhang Bin, Li Yating. A Review of the Evolution Model of Scientific Knowledge Network[J]. Journal of Library Science in China, 2016, 42(5): 85-101.)
[63]
de Solla Price D J. Networks of Scientific Papers[J]. Science, 1965, 149(3683): 510-515.
pmid: 14325149
[64]
de Solla Price D J. A General Theory of Bibliometric and Other Cumulative Advantage Processes[J]. Journal of the American Society for Information Science, 1976, 27(5): 292-306.
doi: 10.1002/asi.v27:5
[65]
Barabási A L, Albert R. Emergence of Scaling in Random Networks[J]. Science, 1999, 286(5439): 509-512.
doi: 10.1126/science.286.5439.509
pmid: 10521342
(Ma Feicheng, Liu Xiang. Evolvement Model for Scientific Knowledge Networks[J]. Systems Engineering-Theory & Practice, 2013, 33(2): 437-443.)
doi: 10.12011/1000-6788(2013)2-437
[67]
Antal T, Krapivsky P L. Weight-Driven Growing Networks[J]. Physical Review E, Statistical, Nonlinear, and Soft Matter Physics, 2005, 71(2 Pt 2): Article No.026103.
[68]
Pan Z F, Li X, Wang X F. Generalized Local-World Models for Weighted Networks[J]. Physical Review E, Statistical, Nonlinear, and Soft Matter Physics, 2006, 73(5 Pt 2): Article No.056109.
(Chen Guo, Zhao Yixin. A Network Evolution Model for Domain Knowledge Driven by Multiple Factors: Following Suit, Conservatism, and Innovation[J]. Journal of the China Society for Scientific and Technical Information, 2020, 39(1): 1-11.)
[70]
Abbasi A, Hossain L, Leydesdorff L. Betweenness Centrality as a Driver of Preferential Attachment in the Evolution of Research Collaboration Networks[J]. Journal of Informetrics, 2012, 6(3): 403-412.
doi: 10.1016/j.joi.2012.01.002
[71]
Sun X L, Kaur J, Milojević S, et al. Social Dynamics of Science[J]. Scientific Reports, 2013, 3: Article No. 1069.
[72]
UKEssays. Bernard Berelson a Content Analysis Media Essay[EB/OL]. (2015-01-01). [2023-05-17]. https://www.ukessays.com/essays/media/bernard-berelson-a-content-analysis-media-essay.php?vref=1.
[73]
Liu L Q, Mei S Y. Visualizing the GVC Research: A Co-Occurrence Network Based Bibliometric Analysis[J]. Scientometrics, 2016, 109(2): 953-977.
doi: 10.1007/s11192-016-2100-5
[74]
Web of Science[EB/OL].[2023-05-17]. https://webofscience.help.clarivate.com/zh-cn/Content/wos-core-collection/wos-full-record.htm.
[75]
Liu G Y, Hu J M, Wang H L. A Co-Word Analysis of Digital Library Field in China[J]. Scientometrics, 2012, 91(1): 203-217.
doi: 10.1007/s11192-011-0586-4
[76]
Romo-Fernández L M, Guerrero-Bote V P, Moya-Anegón F. Co-Word Based Thematic Analysis of Renewable Energy (1990-2010)[J]. Scientometrics, 2013, 97(3): 743-765.
doi: 10.1007/s11192-013-1009-5
[77]
Ravikumar S, Agrahari A, Singh S N. Mapping the Intellectual Structure of Scientometrics: A Co-Word Analysis of the Journal Scientometrics (2005-2010)[J]. Scientometrics, 2015, 102(1): 929-955.
doi: 10.1007/s11192-014-1402-8
[78]
Jeong S, Kim H G. Intellectual Structure of Biomedical Informatics Reflected in Scholarly Events[J]. Scientometrics, 2010, 85(2): 541-551.
doi: 10.1007/s11192-010-0166-z
[79]
Figuerola C G, García Marco F J, Pinto M. Mapping the Evolution of Library and Information Science (1978-2014) Using Topic Modeling on LISA[J]. Scientometrics, 2017, 112(3): 1507-1535.
doi: 10.1007/s11192-017-2432-9
[80]
Sugimoto C R, Li D F, Russell T G, et al. The Shifting Sands of Disciplinary Development: Analyzing North American Library and Information Science Dissertations Using Latent Dirichlet Allocation[J]. Journal of the American Society for Information Science and Technology, 2011, 62(1): 185-204.
doi: 10.1002/asi.v62.1
[81]
Han X Y. Evolution of Research Topics in LIS Between 1996 and 2019: An Analysis Based on Latent Dirichlet Allocation Topic Model[J]. Scientometrics, 2020, 125(3): 2561-2595.
doi: 10.1007/s11192-020-03721-0
(Wang Liang, Zhang Shaowu, Ding Kun, et al. HDP-Based Vehicle Patent Topic Evolution[J]. Journal of the China Society for Scientific and Technical Information, 2014, 33(9): 944-951.)
(Wang Kang, Chen Yue, Su Cheng, et al. Analysis Framework for the Evolution of Scientific Themes from a Multi-Dimensional Perspective[J]. Journal of the China Society for Scientific and Technical Information, 2021, 40(3): 297-307.)
[85]
Chen B T, Tsutsui S, Ding Y, et al. Understanding the Topic Evolution in a Scientific Domain: An Exploratory Study for the Field of Information Retrieval[J]. Journal of Informetrics, 2017, 11(4): 1175-1189.
doi: 10.1016/j.joi.2017.10.003