Browse > Article
http://dx.doi.org/10.3743/KOSIM.2018.35.3.077

Discovering Interdisciplinary Convergence Technologies Using Content Analysis Technique Based on Topic Modeling  

Jeong, Do-Heon (덕성여자대학교 문헌정보학과)
Joo, Hwang-Soo (덕성여자대학교 바이오공학과)
Publication Information
Journal of the Korean Society for information Management / v.35, no.3, 2018 , pp. 77-100 More about this Journal
Abstract
The objectives of this study is to present a discovering process of interdisciplinary convergence technology using text mining of big data. For the convergence research of biotechnology(BT) and information communications technology (ICT), the following processes were performed. (1) Collecting sufficient meta data of research articles based on BT terminology list. (2) Generating intellectual structure of emerging technologies by using a Pathfinder network scaling algorithm. (3) Analyzing contents with topic modeling. Next three steps were also used to derive items of BT-ICT convergence technology. (4) Expanding BT terminology list into superior concepts of technology to obtain ICT-related information from BT. (5) Automatically collecting meta data of research articles of two fields by using OpenAPI service. (6) Analyzing contents of BT-ICT topic models. Our study proclaims the following findings. Firstly, terminology list can be an important knowledge base for discovering convergence technologies. Secondly, the analysis of a large quantity of literature requires text mining that facilitates the analysis by reducing the dimension of the data. The methodology we suggest here to process and analyze data is efficient to discover technologies with high possibility of interdisciplinary convergence.
Keywords
convergence technology; emerging technology; intellectual structure; topic modeling; content analysis;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
연도 인용수 순위
1 Kang, T.G., Park, S.H., Jang, I.S., Kim, I.S., & Han, D.W. (2009). The convergence technology analysis of green growth led illumination. Electronics and telecommunications trends, 24(5), 30-37.   DOI
2 STEPI (2011) Science, technology and society studies for societal challenges. STEPI Policy Research 2011-14.
3 Park, Chi-Ho, Kwon, Soon, Lee, Chung-Hee, & Jung, Woo-Young (2011). A study of a reliable positioning based on technology convergence of a satellite navigation system and a vision system. Journal of the Institute of Electronics and Information Engineers, TC48(10), 20-28.
4 Baek, Hyun Mi, & Kim, Myung Seuk (2013). Technological convergence trend through patent network analysis: focusing on patent data in Korea, U.S., europe, and Japan. Asia-Pacific Journal of Business Venturing and Entrepreneurship, 8(2), 11-19.   DOI
5 Korea Institute for Industrial Economics & Trade (2014). An analysis on the trends and determinants of technology convergence of Korea. R&D Report 2014-709.
6 Biotech Policy Research Center (2015). 2015 Discovering future emerging biotechnologies - ICTconverged biohealth top 10 future emerging biotechnologies -. BioINsay No.2(Series No.223)
7 Biotech Policy Research Center (2017). 2017 Future emerging biotechnologies - top 10 future emerging technologies leading biohealth issues -. BioINsay No.14(Series No.242)
8 Biotech Policy Research Center (2018). 2018 Future emerging biotechnologies - top 10 future emerging technologies from the aspects of core, red, green, white bio -. BioInsay No.27 (Series No.261)
9 Yuk, JeeHee, & Song, Min (2018). A study of research on methods of automated biomedical document classification using topic modeling and deep learning. Journal of the Korean Society for Information Management, 35(2), 63-88. http://dx.doi.org/10.3743/KOSIM.2018.35.2.063   DOI
10 Jeong, Do-Heon (2017). Prescriptive analytics system design fusing automatic classification method and intellectual structure analysis method. Journal of the Korean Society for Information Management, 34(4), 33-57. http://dx.doi.org/10.3743/KOSIM.2017.34.4.033   DOI
11 Cho, Ah, Lee, Kyung Hee, & Cho, Wan Sup (2015). Latent mobility pattern analysis of bus passengers with LDA. Journal of the Korean Data & Information Science Society, 26(5), 1061-1069. http://dx.doi.org/10.7465/jkdi.2015.26.5.1061   DOI
12 Jin, Seol A, & Song, Min (2016). Topic modeling based interdisciplinarity measurement in the informatics related journals. Journal of the Korean Society for Information Management, 33(1), 7-32. http://dx.doi.org/10.3743/KOSIM.2016.33.1.007   DOI
13 Choi, Hochang, Kwahk, Kee-Young, & Kim, Namgyu (2018). Discovering promising convergence technologies using network analysis of maturity and dependency of technology. Journal of Intelligence and Information Systems, 24(1), 101-124. http://dx.doi.org/10.13088/jiis.2018.24.1.101   DOI
14 Blei, D.M., Ng, A.Y., & Jordan, M.I. (2003). Latent dirichlet allocation. The Journal of Machine Learning Research, 3, 993-1022.
15 Caen, O., Lu, H., Nizard, P., & Taly, V. (2017). Microfluidics as a strategic player to decipher single-cell omics?, Trends in Biotechnology, 35(8), 713-727. http://doi.org/10.1016/j.tibtech.2017.05.004   DOI
16 Deerwester, S., Dumais, S., Landauer, T., Furnas, G., & Harshman, R. (1990). Indexing by latent semantic analysis. Journal of the American Society of Information Science, 41(6), 391-407.   DOI
17 Farrahi, K., Gatica-Perez, D., & Gatica-Perez, D. (2012). Extracting mobile behavioral patterns with the distant N-gram topic model. In Proceedings of the 16th International Symposium on Wearable Computers (ISWC), 1-8. http://doi.org/10.1109/ISWC.2012.20
18 Galipeau, J., & Sensebe, L. (2018). Mesenchymal stromal cells: Clinical challenges and therapeutic opportunities. Cell Stem Cell, 22(6), 824-833. http://doi.org/10.1016/j.stem.2018.05.004   DOI
19 Gartner (2015). Are wearables fit for clincal trials?. smarter with gartner(2015.10.15.). Retrieved from http://www.gartner.com/smarterwithgartner/are-wearables-fit-for-clinical-trials/
20 Gartner (2017). Gartner says worldwide wearable device sales to grow 17 percent in 2017. newsroom press release(2017.08.24.). Retrieved from http://www.gartner.com/en/newsroom/press-releases/2017-08-24-gartner-says-worldwide-wearable-device-sales-to-grow-17-percent-in-2017
21 Gartner (2018). Wearables hold the key to connected health monitoring. smarter with gartner (2018.03.08.) Retrieved from http://www.gartner.com/smarterwithgartner/wearables-hold-the-key-to-connected-health-monitoring/
22 Hofmann, T. (1999). Probabilistic latent semantic indexing. In Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, 50-57.
23 Jeong, D.H., & Song, M. (2014). Time gap analysis by the topic model-based temporal technique. Journal of Informetrics, 8(3), 776-790. http://dx.doi.org/10.1016/j.joi.2014.07.005   DOI
24 Jung, S.Y., Ahn, S., Nam, K.H., Lee, J.P., & Lee, S.J. (2012). In vivo measurements of blood flow in a rat using X-ray imaging technique. The International Journal of Cardiovascular Imaging, 28(2), 1853-1858. http://doi.org/10.1007/s10554-012-0029-1   DOI
25 Lee, H.Y., & Hong, I.S. (2017). Double-edged sword of mesenchymal stem cells: Cancer-promoting versus therapeutic potential. Cancer Science, 108(10), 1939-1946. http://doi.org/10.1111/cas.13334   DOI
26 McKinsey Global Institute (2011). Big data: The next frontier for innovation, competition, and productivity. Retrieved from http://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/big-data-the-next-frontier-for-innovation
27 MIT (2015). MIT technology review, 10 breakthrough technologies, 2015. Retrieved from http://www.technologyreview.com/lists/technologies/2015/
28 Prakadan, S.M., Shalek, A.K., & Weitz, D.A. (2017). Scaling by shrinking: empowering singlecell 'omics' with microfluidic devices. Nature Reviews Genetics, 18(6), 345-361. http://doi.org/10.1038/nrg.2017.15   DOI
29 Quirin, A., Cordon, O., Guerrero-Bote, V.P., Vargas-Quesada, B., & Moya-Anegon, F. (2008). A quick MST-Based algorithm to obtain pathfinder networks($\infty$, n-1). Journal of the American Society for Information Science and Technology, 59(12), 1912-1924. http://doi.org/10.1002/asi.20904   DOI
30 Research and Markets (2017). mHealth (Mobile Healthcare) Ecosystem Market: 2017-2030-$23 Billion Opportunities, Challenges, Strategies & Forecasts. Globe Newswire(2017.03.02.). Retrieved from http://globenewswire.com/news-release/2017/03/02/930109/0/en/mHealth-Mobile-Healthcare-Ecosystem-Market-2017-2030-23-Billion-Opportunities-Challenges-Strategies-Forecasts.html
31 Ridge, S.M., Sullivan, F.J., & Glynn, S.A. (2017). Mesenchymal stem cells: key players in cancer progression. Molecular Cancer, 16(31). http://doi.org/10.1186/s12943-017-0597-8
32 Salton, G., & McGill, M.J. (1983). Introduction to Modern Information Retrieval. McGraw-Hill (NY).
33 Schvaneveldt, R.W., Durso, F.T., & Dearholt, D.W. (1989). Network structures in proximity data. In G. Bower(Ed.), The psychology of learning and motivation: Advances in research and theory, 24, 249-284. New York: Academic Press.
34 The Science Times (2016). '와해성 기술'이 내년 R&D 이끈다(2016.12.14.). Retrieved from http://www.sciencetimes.co.kr/?news=%EC%99%80%ED%95%B4%EC%84%B1-%EA%B8%B0%EC%88%A0%EC%9D%B4-%EB%82%B4%EB%85%84-rd-%EC%9D%B4%EB%81%88%EB%8B%A4
35 Vretos, N., Nikolaidis, N., & Pitas, I. (2012). Video fingerprinting using latent dirichlet allocation and facial images. Pattern Recognition, 45(7), 2489-2498. http://doi.org/10.1016/j.patcog.2011.12.022   DOI
36 Whitesides, G.M., Ostuni, E., Takayama, S., Jiang, X., & Ingber, D.E. (2001). Soft lithography in biology and biochemistry. Annual Review of Biomedical Engineering, 3, 335-373. http://doi.org/10.1146/annurev.bioeng.3.1.335   DOI
37 Wikipedia (2018). Disruptive Innovation. Retrieved from http://en.wikipedia.org/wiki/Disruptive_innovation
38 Song, M., & Kim, S.Y. (2013). Detecting the knowledge structure of bioinformatics by mining full-text collections. Scientometrics, 96(1), 183-201. http://doi.org/10.1007/s11192-012-0900-9   DOI