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Fuzzy logic in knowledge dissemination due to citation trees. Contribution to discipline vector

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Języki publikacji
EN
Abstrakty
EN
Large sets of articles are evaluated by predefined measures such as the article numbers and h-indexes. All of these indicators are scalars and refer rather to one discipline or the comprehensive science. Thus, according to disciplinary categories in scientific databases, the distribution has become too rigid for current science needs, dynamically growing towards inter- and trans-disciplinarity. We propose a new method of calculating the impact on knowledge of articles and their citations, creating citation networks, and using one of the optimistic fuzzy aggregation norms to estimate the contribution to the knowledge considering the citation inheritance of citing papers to cited papers (paper children to the paper-parents). Due to this method, we produced the contribution vectors for various disciplines/subdisciplines based on articles and their citations of publications belonging to the considered disciplines. We can prepare the scientific profiles of papers and disciplines based on the contribution vectors. Moreover, we can evaluate how much citations matter in the development of science. Applying this method, we can estimate the contribution to the considered research field caused by papers and their citations from different areas of science. The proposed method might be applicable in the assessment of developing concepts.
Rocznik
Strony
art. no. e141988
Opis fizyczny
Bibliogr. 31 poz., rys., tab.
Twórcy
  • Institute of Informatics, Kazimierz Wielki University in Bydgoszcz, Kopernika 1, 85-074 Bydgoszcz, Poland
  • Institute of Information and Communication Research, Nicolaus Copernicus University in Torun, W. Bojarskiego 1, 87-100 Torun, Poland
  • Department of Informatics, Faculty of Physics, Astronomy and Informatics, Nicolaus Copernicus University in Torun
Bibliografia
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  • [5] L. Waltman, “A review of the literature on citation impact indicators,” J. Informetr., vol. 10, no. 2, pp. 365–391, 2016, doi: 10.1016/j.joi.2016.02.007.
  • [6] E. Yan, “Disciplinary knowledge production and diffusion in science,” J. Assoc. Inf. Sci. Technol., vol. 67, no. 9, pp. 2223–2245, 2016, doi: 10.1002/asi.23541.
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  • [13] H.F. Moed, F. de Moya-Anegon, V. Guerrero-Bote, and C. Lopez-Illescas, “Are nationally oriented journals indexed in scopus becoming more international? the effect of publication language and access modality,” J. Informetr., vol. 14, no. 2, p. 101011, 2020.
  • [14] R.N. Kostoff andW.L. Martinez, “Is citation normalization realistic?” J. Inf. Sci., vol. 31, no. 1, pp. 57–61, 2005, doi: 10.1177/0165551505049260.
  • [15] “Web of science core collection: Data mining using analyze results,” 2021. [Online]. Available: https://clarivate.libguides.com/woscc/analyze (Accessed 2021-04-21).
  • [16] V. Radu, F. Radu, A.I. Tabirca, S.I. Saplacan, and R. Lile, “Bibliometric analysis of fuzzy logic research in international scientific databases.” Int. J. Comput. Commun. Control, vol. 16, no. 1, 2021, doi: 10.15837/ijccc.2021.1.4120.
  • [17] J.M. Merigó, W. Pedrycz, R. Weber, and C. de la Sotta, “Fifty years of information sciences: A bibliometric overview,” Inf. Sci., vol. 432, pp. 245–268, 2018.
  • [18] D. Yu and S. Shi, “Researching the development of atanassov intuitionistic fuzzy set: Using a citation network analysis,” Appl. Soft Comput., vol. 32, pp. 189–198, 2015, doi: 10.1016/j.asoc.2015.03.027.
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  • [21] P. Prokopowicz, D. Mikołajewski, K. Tyburek, and E. Mikołajewska, “Computational gait analysis for post-stroke rehabilitation purposes using fuzzy numbers, fractal dimension and neural networks,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 68, pp. 191–198, 2020. doi: 10.24425/bpasts.2020.131843.
  • [22] O. Sokolov, W. Osi´nska, A. Mreła, and W. Duch, “Modeling of scientific publications disciplinary collocation based on optimistic fuzzy aggregation norms,” in International Conference on Information Systems Architecture and Technology. Springer, 2018, pp. 145–153.
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  • [26] R. Yan, J. Tang, X. Liu, D. Shan, and X. Li, “Citation count prediction: learning to estimate future citations for literature,” in Proceedings of the 20th ACM international conference on Information and knowledge management, 2011, pp. 1247–1252. doi: 10.1145/2063576.2063757.
  • [27] “Normalized indicators,” 2020. [Online]. Available: https://incites.help.clarivate.com/Content/Indicators-Handbook/ih-normalized-indicators.htm (Accessed 2021-04-21).
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  • [29] “Expected citation rates, half-life, and impact ratio,” 2021. [Online]. Available: https://clarivate.com/webofsciencegroup/essays/expected-citation-rates-half-life-and-impact-ratio (Accessed 2021-02-20).
  • [30] E. Yan and Q. Yu, “Using path-based approaches to examine the dynamic structure of discipline-level citation networks: 1997–2011,” J. Assoc. Inf. Sci. Technol., vol. 67, no. 8, pp. 1943–1955, 2016, doi: 10.1002/asi.23516.
  • [31] J. Stallings et al., “Scientific impact using a collaboration index,” in Proceedings of the National Academy of Sciences Jun. 2013, 2013, pp. 9680–9685, doi: 10.1073/pnas.1220184110.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
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