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Similarity measures for document mapping: A comparative study on the level of an individual scientist

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  • Christian Sternitzke

    (Technische Universität Ilmenau
    Universität Bremen)

  • Isumo Bergmann

    (Universität Bremen)

Abstract

This paper investigates the utility of the Inclusion Index, the Jaccard Index and the Cosine Index for calculating similarities of documents, as used for mapping science and technology. It is shown that, provided that the same content is searched across various documents, the Inclusion Index generally delivers more exact results, in particular when computing the degree of similarity based on citation data. In addition, various methodologies such as co-word analysis, Subject-Action-Object (SAO) structures, bibliographic coupling, co-citation analysis, and self-citation links are compared. We find that the two former ones tend to describe rather semantic similarities that differ from knowledge flows as expressed by the citation-based methodologies.

Suggested Citation

  • Christian Sternitzke & Isumo Bergmann, 2009. "Similarity measures for document mapping: A comparative study on the level of an individual scientist," Scientometrics, Springer;Akadémiai Kiadó, vol. 78(1), pages 113-130, January.
    • Handle: RePEc:spr:scient:v:78:y:2009:i:1:d:10.1007_s11192-007-1961-z
    DOI: 10.1007/s11192-007-1961-z
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    Cited by:

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    3. Martin G. Moehrle & Jan M. Gerken, 2012. "Measuring textual patent similarity on the basis of combined concepts: design decisions and their consequences," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(3), pages 805-826, June.
    4. Nassiri, Isar & Masoudi-Nejad, Ali & Jalili, Mahdi & Moeini, Ali, 2013. "Normalized Similarity Index: An adjusted index to prioritize article citations," Journal of Informetrics, Elsevier, vol. 7(1), pages 91-98.
    5. Xuefeng Wang & Huichao Ren & Yun Chen & Yuqin Liu & Yali Qiao & Ying Huang, 2019. "Measuring patent similarity with SAO semantic analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(1), pages 1-23, October.
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    7. Sungchul Choi & Janghyeok Yoon & Kwangsoo Kim & Jae Yeol Lee & Cheol-Han Kim, 2011. "SAO network analysis of patents for technology trends identification: a case study of polymer electrolyte membrane technology in proton exchange membrane fuel cells," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(3), pages 863-883, September.
    8. Christian Sternitzke, 2009. "Patents and publications as sources of novel and inventive knowledge," Scientometrics, Springer;Akadémiai Kiadó, vol. 79(3), pages 551-561, June.
    9. Ramazan Yirci & Turgut Karakose & Ibrahim Kocabas & Tijen Tülübaş & Stamatios Papadakis, 2023. "A Bibliometric Review of the Knowledge Base on Mentoring for the Professional Development of School Administrators," Sustainability, MDPI, vol. 15(4), pages 1-25, February.
    10. Martin G. Moehrle, 2010. "Measures for textual patent similarities: a guided way to select appropriate approaches," Scientometrics, Springer;Akadémiai Kiadó, vol. 85(1), pages 95-109, October.
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    13. H. Simon & N. Sick, 2016. "Technological distance measures: new perspectives on nearby and far away," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(3), pages 1299-1320, June.
    14. Babak Amiri & Ramin Karimianghadim & Navid Yazdanjue & Liaquat Hossain, 2021. "Research topics and trends of the hashtag recommendation domain," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 2689-2735, April.
    15. Hain, Daniel S. & Jurowetzki, Roman & Buchmann, Tobias & Wolf, Patrick, 2022. "A text-embedding-based approach to measuring patent-to-patent technological similarity," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
    16. Francesco Foglia, 2023. "Is smart specialisation monopolising the research on the EU cohesion policy? Evidence from a bibliometric analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(2), pages 1001-1021, February.
    17. Cobo, M.J. & López-Herrera, A.G. & Herrera-Viedma, E. & Herrera, F., 2011. "An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the Fuzzy Sets Theory field," Journal of Informetrics, Elsevier, vol. 5(1), pages 146-166.
    18. Turgut Karakose & Tijen Tülübaş & Stamatios Papadakis, 2022. "Revealing the Intellectual Structure and Evolution of Digital Addiction Research: An Integrated Bibliometric and Science Mapping Approach," IJERPH, MDPI, vol. 19(22), pages 1-27, November.
    19. Hwang, Seonho & Shin, Juneseuk, 2019. "Extending technological trajectories to latest technological changes by overcoming time lags," Technological Forecasting and Social Change, Elsevier, vol. 143(C), pages 142-153.
    20. Hauke Simon & Jens Leker, 2016. "Using Startup Communication For Opportunity Recognition — An Approach To Identify Future Product Trends," International Journal of Innovation Management (ijim), World Scientific Publishing Co. Pte. Ltd., vol. 20(08), pages 1-22, December.
    21. Eva-María Mora-Valentín & Marta Ortiz-de-Urbina-Criado & Juan-José Nájera-Sánchez, 2018. "Mapping the conceptual structure of science and technology parks," The Journal of Technology Transfer, Springer, vol. 43(5), pages 1410-1435, October.
    22. Raquel Ruiz-Rodríguez & Marta Ortiz-de-Urbina-Criado & Rafael Ravina-Ripoll, 2023. "Neuroleadership: a new way for happiness management," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-14, December.
    23. Lee, Changyong & Kang, Bokyoung & Shin, Juneseuk, 2015. "Novelty-focused patent mapping for technology opportunity analysis," Technological Forecasting and Social Change, Elsevier, vol. 90(PB), pages 355-365.
    24. E. M. Murgado-Armenteros & M. Gutiérrez-Salcedo & F. J. Torres-Ruiz & M. J. Cobo, 2015. "Analysing the conceptual evolution of qualitative marketing research through science mapping analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(1), pages 519-557, January.

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