Abstract
The arrival of information age provides a platform for the development of academic field. Knowledge exchanges between disciplines are becoming more frequent, boundaries are gradually blurred, interdisciplinary has become an inevitable trend. In the field of Scientometrics, it has become a new hotspot to study the interdisciplinarity of specific documents by identifying the subject characteristics of references. However, facing a massive academic documents, how to identify the subject characteristics of references quickly and accurately still restricts the development of related research. In this paper, we use the reference data in the field of Gene Editing of 2015 from Web of Science core database, with the help of the indicators of complex network link prediction, five features are selected. Base on this, a non-linear Support Vector Machine classification model is constructed to judge the interdisciplinarity of citation. Finally, the F1 value obtained by 5 fold cross-validation is 0.63, which indicators that the indicators can distinguish the academic citation from the interdisciplinary citation.
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References
Tan, Y.T., Deng, X.Z.: Research review on interdisciplinary information seeking behaviors. Libr. Inf. Serv. 59(16), 135–142 (2015)
Han, P., Wang, D.B.: A Review on theories and practices of interdisciplinarity. J. China Soc. Sci. Tech. Inf. 11, 1222–1232 (2014)
Larivière, V., Gingras, Y.: On the relationship between interdisciplinarity and scientific impact. J. Am. Soc. Inf. Sci. Technol. 61(1), 126–131 (2010)
Silva, F.N., Rodrigues, F.A., Oliveira, O.N., et al.: Quantifying the interdisciplinarity of scientific journals and fields. J. Informetr. 7(2), 469–477 (2013)
Fiala, F.: PageRank-based prediction of award-winning researchers and the impact of citations. J. Inf. 11, 1044–1068 (2017)
Zhou, T., Lü, L., Zhang, Y.C.: Predicting missing links via local information. Eur. Phys. J. B-Condens. Matter Complex Syst. 71(4), 623–630 (2009)
Shibata, N., Kajikawa, Y., Sakata, I.: Link prediction in citation networks. J. Assoc. Inf. Sci. Technol. 63(1), 78–85 (2011)
Chakraborty, T., Kumar, S., Goyal, P., et al.: On the categorization of scientific citation profiles in computer science. Commun. ACM 58(9), 82–90 (2015)
Small, H.: Maps of science as interdisciplinary discourse: co-citation contexts and the role of analogy. Scientometrics 83(3), 835–849 (2010)
Chakraborty, T., Kumar, S., Reddy, M.D., et al.: Automatic classification and analysis of interdisciplinary fields in computer sciences. In: International Conference on Social Computing, pp. 180–187. IEEE (2013)
Hasan, M.A., Chaoji, V., Salem, S., Zaki, M.: Link prediction using supervised learning. In: The Proceedings of the Fourth Workshop on Link Analysis, Counterterrorism and Security, Bethesda (2006)
Chen, W.J., Zhang, D.B.: Study on the internal factors and effects of gene editing technology development. Genomics Appl. Biol. 1–8, 07 January 2020. http://kns.cnki.net/kcms/detail/45.1369.Q.20190614.0951.002.html
Liu, Q., Li, Y., Duan, H., et al.: Knowledge graph construction techniques. J. Comput. Res. Dev. 53(03), 582–600 (2016)
Hsu, C., Chang, C., Lin, C.: A practical guide to support vector classification. Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan (2003). www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf)
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Zhang, X., Xu, S., An, X. (2020). Method for Judging Interdisciplinary References in Literature Based on Complex Networks. In: Xu, Z., Parizi, R., Hammoudeh, M., Loyola-González, O. (eds) Cyber Security Intelligence and Analytics. CSIA 2020. Advances in Intelligent Systems and Computing, vol 1146. Springer, Cham. https://doi.org/10.1007/978-3-030-43306-2_50
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DOI: https://doi.org/10.1007/978-3-030-43306-2_50
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