Skip to main content
Springer Nature Link
Log in

Mining the evolutionary process of knowledge through multiple relationships between keywords

  • Published:
Scientometrics Aims and scope Submit manuscript

Abstract

Knowledge evolution offers a road map for understanding knowledge creation, knowledge transfer, and performance in everyday work. Understanding the knowledge evolution of a research field is crucial for researchers, policymakers, and stakeholders. Further, paper keywords are considered efficient knowledge components to depict the knowledge structure of a research field by examining relationships between keywords. However, multiple relationships between keywords provided by papers are rarely used to explore knowledge evolution. Three relationships were applied: a direct co-occurrence relationship, indirect relationship by keyword pair citation, and same author trace, providing temporal and sequential knowledge evolution. The direct co-occurrence relationship is constructed by keyword co-occurrence pair and acts as the temporal structure of knowledge pairs. The indirect relationship is constructed by a keyword pair-based citation relationship, meaning the citation relationship between keyword co-occurrence pairs, acting as the sequential structure of knowledge pairs. Additionally, the same author trace represents an indirect relationship that a keyword pair provided by the same author in a different paper. Thus, knowledge evolution could be mined quantitatively from a different perspective. Therefore, we present an empirical study of the informetrics field with five evolution stages: knowledge generation, growth, obsolescence, transfer, and intergrowth. The results indicate that knowledge evolution is not a continuous trend but alternating growth and obsolescence. During evolution, knowledge pairs stimulate each other’s growth, and some knowledge pairs transfer to others, demonstrating a small step toward knowledge change. According to the indirect keyword relationship paired with the same author trace, creators and followers of knowledge evolution are different.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  • Abramo, G., & D’Angelo, C. A. (2020). The domestic localization of knowledge flows as evidenced by publication citation: The case of Italy. Scientometrics, 125(2), 1305–1329.

    Article  Google Scholar 

  • Abrishami, A., & Aliakbary, S. (2019). Predicting citation counts based on deep neural network learning techniques. Journal of Informetrics, 13(2), 485–499.

    Article  Google Scholar 

  • Allee, V. (2012). The knowledge evolution. Routledge.

    Book  Google Scholar 

  • Arthur, W. B. (2009). The nature of technology: What it is and how it evolves. Simon and Schuster.

  • Bar-Ilan, J. (2008). Informetrics at the beginning of the 21st century—A review. Journal of Informetrics, 2(1), 1–52.

    Article  Google Scholar 

  • Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. The Journal of Machine Learning Research, 3, 993–1022.

    MATH  Google Scholar 

  • Börner, K., Maru, J. T., & Goldstone, R. L. (2004). The simultaneous evolution of author and paper networks. Proceedings of the National Academy of Sciences, 101(suppl 1), 5266–5273.

    Article  Google Scholar 

  • Burt, R. S. (2004). Structural holes and good ideas. American Journal of Sociology, 110(2), 349–399.

    Article  Google Scholar 

  • Burton, R. E., & Kebler, R. W. (1960). The “half-life” of some scientific and technical literatures. American Documentation, 11(1), 18–22.

    Article  Google Scholar 

  • Chang, J., Gerrish, S., Wang, C., Boyd-Graber, J. L., & Blei, D. M. (2009). Reading tea leaves: How humans interpret topic models. In Proceedings of the 22nd international conference on neural information processing systems (NIPS) (pp. 288–296).

  • Chen, C., & Hicks, D. (2004). Tracing knowledge diffusion. Scientometrics, 59(2), 199–211.

    Article  Google Scholar 

  • Cheng, Q., Wang, J., Lu, W., Huang, Y., & Bu, Y. (2020). Keyword-citation-keyword network: A new perspective of discipline knowledge structure analysis. Scientometrics, 124(3), 1923–1943.

    Article  Google Scholar 

  • Choi, J. M. (1988). Citation analysis of intra-and interdisciplinary communication patterns of anthropology in the USA. Behavioral & Social Sciences Librarian, 6(3–4), 65–84.

    Article  Google Scholar 

  • Choudhury, N., & Uddin, S. (2016). Time-aware link prediction to explore network effects on temporal knowledge evolution. Scientometrics, 108(2), 745–776.

    Article  Google Scholar 

  • Conover, M. D., Ratkiewicz, J., Francisco, M., Gonçalves, B., Menczer, F., & Flammini, A. (2011). Political polarization on twitter. In 5th international AAAI conference on weblogs and social media.

  • D’Angelo, C. A., & van Eck, N. J. (2020). Collecting large-scale publication data at the level of individual researchers: A practical proposal for author name disambiguation. Scientometrics, 123(2), 883–907.

    Article  Google Scholar 

  • Dosi, G. (1982). Technological paradigms and technological trajectories: A suggested interpretation of the determinants and directions of technical change. Research Policy, 11(3), 147–162.

    Article  Google Scholar 

  • Dou, W., Wang, X., Ribarsky, W., & Zhou, M. (2012). Event detection in social media data. In IEEE VisWeek workshop on interactive visual text analytics-task driven analytics of social media content (pp. 971–980).

  • Egghe, L. (2005). Expansion of the field of informetrics: Origins and consequences. Information Processing and Management, 41(6), 1311–1316.

    Article  Google Scholar 

  • Figuerola, C. G., Marco, F. J. G., & Pinto, M. (2017). Mapping the evolution of library and information science (1978–2014) using topic modeling on LISA. Scientometrics, 112(3), 1507–1535.

    Article  Google Scholar 

  • Fleming, L. (2001). Recombinant uncertainty in technological search. Management Science, 47(1), 117–132.

    Article  Google Scholar 

  • Furman, J. L., & Stern, S. (2011). Climbing atop the shoulders of giants: The impact of institutions on cumulative research. American Economic Review, 101(5), 1933–1963.

    Article  Google Scholar 

  • Garfield, E. (1965). Can citation indexing be automated? In Statistical association methods for mechanized documentation, symposium proceedings (Vol. 269, pp. 189–192).

  • Gomez-Rodriguez, M., Leskovec, J., & Krause, A. (2012). Inferring networks of diffusion and influence. ACM Transactions on Knowledge Discovery from Data (TKDD), 5(4), 1–37.

    Article  Google Scholar 

  • Gosnell, C. F. (1944). Obsolescence of Books in College Libraries. College & Research Libraries, 5(2), 115–125.

  • Guerrero-Solé, F. (2017). Community detection in political discussions on Twitter: An application of the retweet overlap network method to the Catalan process toward independence. Social Science Computer Review, 35(2), 244–261.

    Article  Google Scholar 

  • Hassan, S. U., Safder, I., Akram, A., & Kamiran, F. (2018). A novel machine-learning approach to measuring scientific knowledge flows using citation context analysis. Scientometrics, 116(2), 973–996.

    Article  Google Scholar 

  • Higham, K. W., Governale, M., Jaffe, A. B., & Zülicke, U. (2017). Unraveling the dynamics of growth, aging and inflation for citations to scientific articles from specific research fields. Journal of Informetrics, 11(4), 1190–1200.

    Article  Google Scholar 

  • Hoch, P. K. (1985). Migration and the generation of new scientific ideas. Minerva, 25, 209–237. https://doi.org/10.1007/bf01097783

    Article  Google Scholar 

  • Hu, J., & Zhang, Y. (2015). Research patterns and trends of recommendation system in China using co-word analysis. Information Processing & Management, 51(4), 329–339.

    Article  Google Scholar 

  • Hu, K., Wu, H., Qi, K., Yu, J., Yang, S., Yu, T., Zheng, J., & Liu, B. (2018). A domain keyword analysis approach extending term frequency-keyword active index with Google word2vec model. Scientometrics, 114(3), 1031–1068.

    Article  Google Scholar 

  • Jung, S., & Yoon, W. C. (2020). An alternative topic model based on common interest authors for topic evolution analysis. Journal of Informetrics, 14(3), 101040.

    Article  Google Scholar 

  • Khasseh, A. A., Soheili, F., Moghaddam, H. S., & Chelak, A. M. (2017). Intellectual structure of knowledge in iMetrics: A co-word analysis. Information Processing & Management, 53(3), 705–720.

    Article  Google Scholar 

  • Kim, M., Baek, I., & Song, M. (2018). Topic diffusion analysis of a weighted citation network in biomedical literature. Journal of the Association for Information Science and Technology, 69(2), 329–342.

    Article  Google Scholar 

  • Kim, M. C., Feng, Y., & Zhu, Y. (2021). Mapping scientific profile and knowledge diffusion of Library Hi Tech. Library Hi Tech, 39(2), 549–573. https://doi.org/10.1108/LHT-08-2019-0164

    Article  Google Scholar 

  • Klavans, R., & Boyack, K. W. (2009). Toward a consensus map of science. Journal of the American Society for Information Science and Technology, 60, 455–476.

    Article  Google Scholar 

  • Koppel, M., & Winter, Y. (2014). Determining if two documents are written by the same author. Journal of the Association for Information Science and Technology, 65(1), 178–187.

    Article  Google Scholar 

  • Kuhn, T. (1962a). The nature and necessity of scientific revolutions, from the structure of scientific revolutions. In The philosophy of science (pp. 148–157). MIT Press.

  • Kuhn, T. S. (1962b). The structure of scientific revolutions (1st ed., p. 3). University of Chicago Press. ISBN:0-226-45807-5.

  • Kuhn, T., Perc, M., & Helbing, D. (2014). Inheritance patterns in citation networks reveal scientific memes. Physical Review X, 4(4), 041036.

    Article  Google Scholar 

  • Lee, D., Kim, W. C., Charidimou, A., & Song, M. (2015). A bird’s-eye view of Alzheimer’s disease research: Reflecting different perspectives of indexers, authors, or citers in mapping the field. Journal of Alzheimer’s Disease, 45(4), 1207–1222.

    Article  Google Scholar 

  • Lee, K., Kim, S., Kim, E. H. J., & Song, M. (2017). Comparative evaluation of bibliometric content networks by tomographic content analysis: An application to Parkinson’s disease. Journal of the Association for Information Science and Technology, 68(5), 1295–1307.

    Article  Google Scholar 

  • Lee, P.-C., Su, H.-N., & Chan, T.-Y. (2010). Assessment of ontology-based knowledge network formation by Vector-Space Model. Scientometrics, 85(3), 689–703.

    Article  Google Scholar 

  • Leydesdorff, L., de Moya‐Anegón, F., & de Nooy, W. (2016). Aggregated journal–journal citation relations in scopus and web of science matched and compared in terms of networks, maps, and interactive overlays. Journal of the Association for Information Science and Technology, 67(9), 2194–2211.

  • Liang, G., Hou, H., Lou, X., & Hu, Z. (2019). Qualifying threshold of “take-off” stage for successfully disseminated creative ideas. Scientometrics, 120(3), 1193–1208.

    Article  Google Scholar 

  • Liu, J., Grubler, A., Ma, T., & Kogler, D. F. (2021). Identifying the technological knowledge depreciation rate using patent citation data: A case study of the solar photovoltaic industry. Scientometrics, 126(1), 93–115.

    Article  Google Scholar 

  • Liu, Y., Yang, L., & Chen, M. (2021b). A new citation concept: Triangular citation in the literature. Journal of Informetrics, 15(2), 101141.

    Article  Google Scholar 

  • Loasby, B. J. (2002). The evolution of knowledge: Beyond the biological model. Research Policy, 31(8–9), 1227–1239.

    Article  Google Scholar 

  • Lu, W., Huang, S., Yang, J., Bu, Y., Cheng, Q., & Huang, Y. (2021). Detecting research topic trends by author-defined keyword frequency. Information Processing & Management, 58(4), 102594.

    Article  Google Scholar 

  • Lu, W., Liu, Z., Huang, Y., Bu, Y., Li, X., & Cheng, Q. (2020). How do authors select keywords? A preliminary study of author keyword selection behavior. Journal of Informetrics, 14(4), 101066.

    Article  Google Scholar 

  • Mao, J., Liang, Z., Cao, Y., & Li, G. (2020). Quantifying cross-disciplinary knowledge flow from the perspective of content: Introducing an approach based on knowledge memes. Journal of Informetrics, 14(4), 101092.

    Article  Google Scholar 

  • Mihaljević, H., & Santamaría, L. (2021). Disambiguation of author entities in ADS using supervised learning and graph theory methods. Scientometrics, 126(5), 3893–3917.

    Article  Google Scholar 

  • Mimno, D., & McCallum, A. (2012). Topic models conditioned on arbitrary features with Dirichlet-multinomial regression. arXiv preprint arXiv:1206.3278.

  • Mina, A., Ramlogan, R., Tampubolon, G., & Metcalfe, J. S. (2007). Mapping evolutionary trajectories: Applications to the growth and transformation of medical knowledge. Research Policy, 36(5), 789–806.

    Article  Google Scholar 

  • Modis, T. (2007). Strengths and weaknesses of S-curves. Technological Forecasting and Social Change, 74, 866–872.

    Article  Google Scholar 

  • Mukherjee, S., Uzzi, B., Jones, B. F., & Stringer, M. (2017). How atypical combinations of scientific ideas are related to impact: The general case and the case of the field of geography. In Knowledge and Networks (pp. 243–267). Springer.

  • Muñoz-Écija, T., Vargas-Quesada, B., & Rodríguez, Z. C. (2019). Coping with methods for delineating emerging fields: Nanoscience and nanotechnology as a case study. Journal of Informetrics, 13(4), 100976.

    Article  Google Scholar 

  • Parolo, P. D. B., Pan, R. K., Ghosh, R., Huberman, B. A., Kaski, K., & Fortunato, S. (2015). Attention decay in science. Journal of Informetrics, 9(4), 734–745.

    Article  Google Scholar 

  • Peset, F., Garzón-Farinós, F., González, L. M., García-Massó, X., Ferrer-Sapena, A., Toca-Herrera, J. L., & Sánchez-Pérez, E. A. (2020). Survival analysis of author keywords: An application to the library and information sciences area. Journal of the Association for Information Science and Technology, 71(4), 462–473.

    Article  Google Scholar 

  • Prebor, G. (2010). Analysis of the interdisciplinary nature of library and information science. Journal of Librarianship and Information Science, 42(4), 256–267.

    Article  Google Scholar 

  • Pu, T., Huang, M., & Yang, J. (2021). Migration knowledge graph framework and its application. Journal of Physics: Conference Series, 1955(1), 012071.

    Google Scholar 

  • Qian, Y., Liu, Y., & Sheng, Q. Z. (2020). Understanding hierarchical structural evolution in a scientific discipline: A case study of artificial intelligence. Journal of Informetrics, 14(3), 101047.

    Article  Google Scholar 

  • Réale, D., Khelfaoui, M., Montiglio, P. O., et al. (2020). Mapping the dynamics of research networks in ecology and evolution using co-citation analysis (1975–2014). Scientometrics, 122, 1361–1385. https://doi.org/10.1007/s11192-019-03340-4

    Article  Google Scholar 

  • Sedighi, M. (2016). Application of word co-occurrence analysis method in mapping of the scientific fields (case study: The field of informetrics). Library Review, 65(1/2), 52–64.

    Article  Google Scholar 

  • Shi, H. W., & Wang, F. N. (2009). Knowledge intergrowth and tech-information utility optimization in the process of endogenous growth. Studies in Science of Science, 27(11), 1700–1711. (in Chinese).

  • Silvello, G. (2018). Theory and practice of data citation. Journal of the Association for Information Science and Technology, 69(1), 6–20.

    Article  Google Scholar 

  • Su, H. N., & Lee, P. C. (2010). Mapping knowledge structure by keyword co-occurrence: A first look at journal papers in technology foresight. Scientometrics, 85(1), 65–79.

    Article  Google Scholar 

  • Swanson, D. R. (1993). Intervening in the life cycles of scientific knowledge. Library Trends, 41(4), 606–632.

  • Uddin, S., & Khan, A. (2016). The impact of author-selected keywords on citation counts. Journal of Informetrics., 10(4), 1166–1177.

    Article  Google Scholar 

  • Uddin, S., Khan, A., & Baur, L. A. (2015). A framework to explore the knowledge structure of multidisciplinary research fields. PLoS ONE, 10(4), e0123537.

    Article  Google Scholar 

  • Urbano, C., & Ardanuy, J. (2020). Cross-disciplinary collaboration versus coexistence in LIS serials: Analysis of authorship affiliations in four European countries. Scientometrics, 124, 575–602. https://doi.org/10.1007/s11192-020-03471-z

    Article  Google Scholar 

  • Uzzi, B., Mukherjee, S., Stringer, M., & Jones, B. (2013). Atypical combinations and scientific impact. Science, 342(6157), 468–472.

    Article  Google Scholar 

  • van der Eijk, C. C., van Mulligen, E. M., Kors, J. A., Mons, B., & van den Berg, J. (2004). Constructing an associative concept space for literature-based discovery. Journal of the American Society for Information Science and Technology, 55(5), 436–444.

    Article  Google Scholar 

  • Waltman, L. (2016). A review of the literature on citation impact indicators. Journal of Informetrics, 10(2), 365–391.

    Article  Google Scholar 

  • Wang, J., Veugelers, R., & Stephan, P. (2017). Bias against novelty in science: A cautionary tale for users of bibliometric indicators. Research Policy, 46(8), 1416–1436.

    Article  Google Scholar 

  • Wang, M., Zhang, J., Chen, G., & Chai, K. H. (2019). Examining the influence of open access on journals’ citation obsolescence by modeling the actual citation process. Scientometrics, 119(3), 1621–1641.

    Article  Google Scholar 

  • Wu, S., & Wu, H. (2013). More powerful significant testing for time course gene expression data using functional principal component analysis approaches. BMC Bioinformatics, 14(1), 1–13.

    Article  Google Scholar 

  • Wu, C., Hill, C., & Yan, E. (2017). Disciplinary knowledge diffusion in business research. Journal of Informetrics, 11(2), 655–668.

    Article  Google Scholar 

  • Xie, Q., Zhang, X., & Song, M. (2021). A network embedding-based scholar assessment indicator considering four facets: Research topic, author credit allocation, field-normalized journal impact, and published time. Journal of Informetrics, 15(4), 101201.

    Article  Google Scholar 

  • Xu, J., Ding, Y., Bu, Y., Deng, S., Yu, C., Zou, Y., & Madden, A. (2019). Interdisciplinary scholarly communication: An exploratory study for the field of joint attention. Scientometrics, 119(3), 1597–1619.

    Article  Google Scholar 

  • Xu, Y., Zhang, S., Zhang, W., Yang, S., & Shen, Y. (2019). Research front detection and topic evolution based on topological structure and the PageRank algorithm. Symmetry, 11(3), 310.

    Article  Google Scholar 

  • Yu, G., Wang, M. Y., & Yu, D. R. (2010). Characterizing knowledge diffusion of Nanoscience & Nanotechnology by citation analysis. Scientometrics, 84(1), 81–97.

    Article  Google Scholar 

  • Yu, X., Li, G., & Chen, L. (2014). Prediction and early diagnosis of complex diseases by edge-network. Bioinformatics, 30(6), 852–859.

    Article  Google Scholar 

  • Zeng, T., Zhang, W., Yu, X., Liu, X., Li, M., Liu, R., & Chen, L. (2014). Edge biomarkers for classification and prediction of phenotypes. Science China Life Sciences, 57(11), 1103–1114.

    Article  Google Scholar 

  • Zhang, H., Kiranyaz, S., & Gabbouj, M. (2017). Outlier edge detection using random graph generation models and applications. Journal of Big Data, 4(1), 1–25.

    Article  Google Scholar 

  • Zhao, H., Xu, X., Song, Y., Lee, D. L., Chen, Z., & Gao, H. (2018). Ranking users in social networks with higher-order structures. In 32nd AAAI conference on artificial intelligence.

  • Zhu, L., Liu, X., He, S., Shi, J., & Pang, M. (2015). Keywords co-occurrence mapping knowledge domain research base on the theory of big data in oil and gas industry. Scientometrics, 105(1), 249–260.

    Article  Google Scholar 

Download references

Acknowledgements

This study was partially funded by the National Natural Science Foundation of China (NSFC) Grant No. 72104220. This work was also supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2020S1A5B1104865).

Author information

Authors and Affiliations

  1. Henan Academy of Big Data, Zhengzhou University, No. 75, Daxuebei-ro, Zhengzhou, 450000, Henan, China

    Xinyuan Zhang

  2. School of Management, Shenzhen Polytechnic, Shenzhen, 518055, Guangdong, China

    Qing Xie

  3. Department of Library and Information Science, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea

    Chaemin Song & Min Song

Authors
  1. Xinyuan Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Qing Xie
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Chaemin Song
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Min Song
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Min Song.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, X., Xie, Q., Song, C. et al. Mining the evolutionary process of knowledge through multiple relationships between keywords. Scientometrics 127, 2023–2053 (2022). https://doi.org/10.1007/s11192-022-04272-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11192-022-04272-2

Keywords