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A new method for measuring the originality of academic articles based on knowledge units in semantic networks

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  • Hou, Jianhua
  • Wang, Dongyi
  • Li, Jing

Abstract

Research on the evaluation of the quality of academic papers is attracting more attention from scholars in scientometrics. However, most previous researches have assessed paper quality based on external indicators, such as citations, which failed to account for the content of the research. To that end, this paper proposed a new method for measuring a paper's originality. The method was based on knowledge units in semantic networks, focusing on the relationship and semantic similarity of different knowledge units. Connectivity and path similarity between different content elements were used in particular networks as indicators of originality. This study used papers published between 2014 and 2018 in three categories (i.e. Library & Information Science, Educational Psychology, and Carbon Nanotubes) and divided their content into three parts (i.e. research topics, research methods and research results). It was found that the originality in all categories increase each year. Furthermore, a comparison of our new method with previous models of citation network analysis and knowledge combination analysis showed that our new method is better than those previous methods when used in measuring originality.

Suggested Citation

  • Hou, Jianhua & Wang, Dongyi & Li, Jing, 2022. "A new method for measuring the originality of academic articles based on knowledge units in semantic networks," Journal of Informetrics, Elsevier, vol. 16(3).
    • Handle: RePEc:eee:infome:v:16:y:2022:i:3:s175115772200058x
    DOI: 10.1016/j.joi.2022.101306
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    as
    1. Bruce Kogut & Udo Zander, 1992. "Knowledge of the Firm, Combinative Capabilities, and the Replication of Technology," Organization Science, INFORMS, vol. 3(3), pages 383-397, August.
    2. Terrence A. Brooks, 1986. "Evidence of complex citer motivations," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 37(1), pages 34-36, January.
    3. Yan Yan & Shanwu Tian & Jingjing Zhang, 2020. "The impact of a paper’s new combinations and new components on its citation," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(2), pages 895-913, February.
    4. Chaomei Chen, 2006. "CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(3), pages 359-377, February.
    5. Bornmann, Lutz & Tekles, Alexander & Zhang, Helena H. & Ye, Fred Y., 2019. "Do we measure novelty when we analyze unusual combinations of cited references? A validation study of bibliometric novelty indicators based on F1000Prime data," Journal of Informetrics, Elsevier, vol. 13(4).
    6. Xiaoying Li & Suyuan Peng & Jian Du, 2021. "Towards medical knowmetrics: representing and computing medical knowledge using semantic predications as the knowledge unit and the uncertainty as the knowledge context," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 6225-6251, July.
    7. Trapido, Denis, 2015. "How novelty in knowledge earns recognition: The role of consistent identities," Research Policy, Elsevier, vol. 44(8), pages 1488-1500.
    8. Lin, Yiling & Evans, James A. & Wu, Lingfei, 2022. "New directions in science emerge from disconnection and discord," Journal of Informetrics, Elsevier, vol. 16(1).
    9. Wang, Jian & Veugelers, Reinhilde & Stephan, Paula, 2017. "Bias against novelty in science: A cautionary tale for users of bibliometric indicators," Research Policy, Elsevier, vol. 46(8), pages 1416-1436.
    10. Russell J. Funk & Jason Owen-Smith, 2017. "A Dynamic Network Measure of Technological Change," Management Science, INFORMS, vol. 63(3), pages 791-817, March.
    11. Chaomei Chen, 2012. "Predictive effects of structural variation on citation counts," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(3), pages 431-449, March.
    12. Weitzman, Martin L, 1996. "Hybridizing Growth Theory," American Economic Review, American Economic Association, vol. 86(2), pages 207-212, May.
    13. Lin Zhang & Ronald Rousseau & Wolfgang Glänzel, 2016. "Diversity of references as an indicator of the interdisciplinarity of journals: Taking similarity between subject fields into account," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(5), pages 1257-1265, May.
    14. Fairclough, Ruth & Thelwall, Mike, 2015. "More precise methods for national research citation impact comparisons," Journal of Informetrics, Elsevier, vol. 9(4), pages 895-906.
    15. Tahamtan, Iman & Bornmann, Lutz, 2018. "Creativity in science and the link to cited references: Is the creative potential of papers reflected in their cited references?," Journal of Informetrics, Elsevier, vol. 12(3), pages 906-930.
    16. Pierre Azoulay & Joshua S. Graff Zivin & Gustavo Manso, 2011. "Incentives and creativity: evidence from the academic life sciences," RAND Journal of Economics, RAND Corporation, vol. 42(3), pages 527-554, September.
    17. Bornmann, Lutz, 2014. "Do altmetrics point to the broader impact of research? An overview of benefits and disadvantages of altmetrics," Journal of Informetrics, Elsevier, vol. 8(4), pages 895-903.
    18. Lee Fleming, 2001. "Recombinant Uncertainty in Technological Search," Management Science, INFORMS, vol. 47(1), pages 117-132, January.
    19. Bornmann, Lutz & Haunschild, Robin & Adams, Jonathan, 2019. "Do altmetrics assess societal impact in a comparable way to case studies? An empirical test of the convergent validity of altmetrics based on data from the UK research excellence framework (REF)," Journal of Informetrics, Elsevier, vol. 13(1), pages 325-340.
    20. Lingfei Wu & Dashun Wang & James A. Evans, 2019. "Large teams develop and small teams disrupt science and technology," Nature, Nature, vol. 566(7744), pages 378-382, February.
    21. Alan L Porter & J David Roessner & Alex S Cohen & Marty Perreault, 2006. "Interdisciplinary research: meaning, metrics and nurture," Research Evaluation, Oxford University Press, vol. 15(3), pages 187-195, December.
    22. Michael H. MacRoberts & Barbara R. MacRoberts, 1989. "Problems of citation analysis: A critical review," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 40(5), pages 342-349, September.
    23. Bertoli-Barsotti, Lucio & Lando, Tommaso, 2015. "On a formula for the h-index," Journal of Informetrics, Elsevier, vol. 9(4), pages 762-776.
    24. Chaomei Chen, 2012. "Predictive effects of structural variation on citation counts," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(3), pages 431-449, March.
    25. Bornmann, Lutz & Tekles, Alexander, 2021. "Convergent validity of several indicators measuring disruptiveness with milestone assignments to physics papers by experts," Journal of Informetrics, Elsevier, vol. 15(3).
    26. Mingers, John & Leydesdorff, Loet, 2015. "A review of theory and practice in scientometrics," European Journal of Operational Research, Elsevier, vol. 246(1), pages 1-19.
    27. Sotaro Shibayama & Jian Wang, 2020. "Measuring originality in science," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 409-427, January.
    28. Kevin J. Boudreau & Eva C. Guinan & Karim R. Lakhani & Christoph Riedl, 2016. "Looking Across and Looking Beyond the Knowledge Frontier: Intellectual Distance, Novelty, and Resource Allocation in Science," Management Science, INFORMS, vol. 62(10), pages 2765-2783, October.
    29. Ostermaier, Andreas & Uhl, Matthias, 2020. "Performance evaluation and creativity: Balancing originality and usefulness," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 86(C).
    30. Chao Min & Ying Ding & Jiang Li & Yi Bu & Lei Pei & Jianjun Sun, 2018. "Innovation or imitation: The diffusion of citations," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 69(10), pages 1271-1282, October.
    31. Abramo, Giovanni, 2018. "Revisiting the scientometric conceptualization of impact and its measurement," Journal of Informetrics, Elsevier, vol. 12(3), pages 590-597.
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