Abstract
One of the learning tasks which play central roles in the knowledge discovery field is to understand the content of a corpus such that one can efficiently understand the potential knowledge of data texts. Moreover, the availability of various big scholarly data enables us to develop methods for digging deep into the generation of new domains. In this paper, especially, we take cybersecurity disciplinary as an example, collect the relevant data from Microsoft Academic, and then extract the citation and reference relations among different domains. We further propose the domain-derived space, representing the inspiration relations for the emergence domains, by identifying the emergence of domains and the significant derived relations. In the context of the developed domain-derived space, we develop methods to study the growth of domains as well as the characteristics of its ancestral domains. The results reveal the dissipation of the interdisciplinary effect and the importance of a domain in its early stage for the emergence of new domains. These studies quantitatively analyze the trends and impact factors of domain derivation, providing a new research and forecast path for knowledge discovery.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Fischhoff, B., Weinberger, P.J., Millett, L.I.: Foundational Cybersecurity Research: Improving Science, Engineering, and Institutions. National Academies Press, Washington, D.C. (1969)
Guo, Z., Zhang, Z., Zhu, S., Chi, Y., Gong, Y.: Knowledge discovery from citation networks. In: IEEE International Conference on Data Mining, pp. 800–805 (2009)
Guo, Z., Zhang, Z., Zhu, S., Chi, Y., Gong, Y.: Knowledge discovery from citation networks 26(4), 800–805 (2018)
Hidalgo, C., Klinger, B., Barabasi, A.L., Hausmann, R.: The product space and its consequences for economic growth. In: APS Meeting, pp. 439–446 (2007)
Williamson, J.: The philosophy of science and its relation to machine learning. In: Gaber, M. (ed.) Scientific Data Mining and Knowledge Discovery, pp. 77–89. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-02788-8_4
Holzinger, A., et al.: On graph entropy measures for knowledge discovery from publication network data. In: Cuzzocrea, A., Kittl, C., Simos, D.E., Weippl, E., Xu, L. (eds.) CD-ARES 2013. LNCS, vol. 8127, pp. 354–362. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40511-2_25
Mangan, S., Alon, U.: Structure and function of the feed-forward loop network motif. Proc. Nat. Acad. Sci. U.S.A. 100(21), 11980–11985 (2003)
Petersen, A.M., Riccaboni, M., Stanley, H.E., Pammolli, F.: Persistence and uncertainty in the academic career. Proc. Nat. Acad. Sci. U.S.A. 109(14), 5213–8 (2012)
Sinatra, R., Wang, D., Deville, P., Song, C., Barabási, A.L.: Quantifying the evolution of individual scientific impact. Science 354(6312), aaf5239 (2016)
Sinha, A., et al.: An overview of microsoft academic service (MAS) and applications, pp. 243–246 (2015)
Sorenson, O., Fleming, L.: Science and the diffusion of knowledge. Res. Policy 33(10), 1615–1634 (2004)
Vazhkudai, S.S., et al.: Constellation: a science graph network for scalable data and knowledge discovery in extreme-scale scientific collaborations. In: IEEE International Conference on Big Data, pp. 3052–3061 (2017)
Wang, D., Song, C., Barabási, A.L.: Quantifying long-term scientific impact. Science 342(6154), 127–132 (2013)
Wernicke, S., Rasche, F.: FANMOD: a tool for fast network motif detection. Bioinformatics 22(9), 1152–1153 (2006)
Acknowledgments
This work is supported by the National Natural Science Foundation of China grants 61502333, 61572350, 61672377.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Hu, X., Feng, Z., Chen, S., He, D., Huang, K. (2018). Quantifying the Emergence of New Domains: Using Cybersecurity as a Case. In: Liu, W., Giunchiglia, F., Yang, B. (eds) Knowledge Science, Engineering and Management. KSEM 2018. Lecture Notes in Computer Science(), vol 11062. Springer, Cham. https://doi.org/10.1007/978-3-319-99247-1_29
Download citation
DOI: https://doi.org/10.1007/978-3-319-99247-1_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-99246-4
Online ISBN: 978-3-319-99247-1
eBook Packages: Computer ScienceComputer Science (R0)