Unconnected component inclusion technique for patent network analysis: Case study of Internet of Things-related technologies

Journal of Informetrics

Volumn 10, Issue 4, 2016, Pages 967-980

  Takano, Yasutomo ^a   Mejia, Cristian ^a   Kajikawa, Yuya ^a  

^a TOKYO INSTITUTE OF TECHNOLOGY   (Japan)

Author keywords

Clustering; Internet of Things; Patent family analysis; Patent network analysis; Unconnected component

Indexed keywords

INTERNET; INTERNET OF THINGS;

CLUSTERING; CLUSTERING PHENOMENA; CONNECTED COMPONENT; PATENT CITATION ANALYSIS; PATENT FAMILY ANALYSIS; PATENT NETWORK ANALYSIS; TECHNOLOGY LANDSCAPES; UNCONNECTED COMPONENT;

PATENTS AND INVENTIONS;

EID: 84984950707     PISSN: 17511577     EISSN: 18755879     Source Type: Journal    
DOI: 10.1016/j.joi.2016.05.004     Document Type: Article

References (43)

1. Al-Shboul, B., Myaeng, S.H., Analyzing topic drift in query expansion for information retrieval from a large-scale patent dataBase. 2014 International conference on big data and smart computing, BIGCOMP 2014, 2014, 177–182, 10.1109/BIGCOMP.2014.6741432.
2. Altuntas, S., Dereli, T., Kusiak, A., Forecasting technology success based on patent data. Technological Forecasting and Social Change 96 (2015), 202–214, 10.1016/j.techfore.2015.03.011 Elsevier Inc.
3. Atzori, L., Iera, A., Morabito, G., The Internet of Things: a survey. Computer Networks 54:15 (2010), 2787–2805, 10.1016/j.comnet.2010.05.010 Elsevier B.V.
4. Bache, R., Patent retrieval—a question of access. World Patent Information 33:4 (2011), 345–351.
5. Boyack, K.W., Newman, D., Duhon, R.J., Klavans, R., Patek, M., Biberstine, J.R., et al. Clustering more than two million biomedical publications: comparing the accuracies of nine text-based similarity approaches. PLoS One, 6(3), 2011.
6. Clauset, A., Newman, M.E.J., Moore, C., Finding community structure in very large networks. Physical Review E 70:6 Pt 2 (2004), 1–6, 10.1103/PhysRevE.70.066111.
7. Daim, T.U., Rueda, G., Martin, H., Gerdsri, P., Forecasting emerging technologies: use of bibliometrics and patent analysis. Technological Forecasting and Social Change 73 (2006), 981–1012, 10.1016/j.techfore.2006.04.004.
8. De Solla Price, D.J., Networks of scientific papers. Science, 149(3683), 1965, 510.
9. Fajardo-Ortiz, D., Ortega-Sánchez-de-Tagle, J., Castaño, V.M., Hegemonic structure of basic, clinical and patented knowledge on ebola research: a US Army reductionist initiative. Journal of Translational Medicine, 13(1), 2015, 124, 10.1186/s12967-015-0496-y.
10. Gretarsson, B., O Donovan, J., Asuncion, A., Newman, D., TopicNets: visual analysis of large text corpora with topic modeling. ACM Transactions on Intelligent Systems and Technology V(2), 2011, 1–26, 10.1126/science.1178206.
11. Hall, B.H., Jaffe, A., Trajtenberg, M., Market value and patent citations. RAND Journal of Economics 36 (2005), 16–38, 10.2307/1593752.
12. Hamedani, M., Kim, S.-W., On computing similarity in academic literature data: methods and evaluation. Web-Age information management: Waim 2014 international workshops vol. 8597 (2014), 403–412, 10.1007/978-3-319-11538-2_37.
13. Harhoff, D., Narin, F., Scherer, F.M., Vopel, K., Citation frequency and the value of patented inventions. Review of Economics and Statistics 81:3 (1999), 511–515, 10.1162/003465399558265.
14. Hu, G., Liu, W., Nano/micro-electro mechanical systems: a patent view. Journal of Nanoparticle Research, 17(12), 2015, 465, 10.1007/s11051-015-3273-1 Springer Netherlands.
15. Hu, Z., Fang, S., Liang, T., Empirical study of constructing a knowledge organization system of patent documents using topic modeling. Scientometrics 100:19 (2014), 787–799, 10.1007/s11192-014-1328-1.
16. Karki, A. M., & Krishnan, K. S. (1998). Patent Citation Analysis: A Policy Analysis Tool, World Patent Information 19(4), 269–272 http://dx.doi.org/10.1016/S0172-2190(97)00033-1.
17. Kessler, M. (1963). Bibliographic Coupling Between Scientific Papers. American Documentation 14(1): 10–25.
18. Klavans, R., Boyack, K. W. (2015). Which Type of Citation Analysis Generates the Most Accurate Taxonomy of Scientific and Technical Knowledge? E-Print, 1–26. arXiv:151105078v2.
19. Leydesdorff, L., Patent classifications as indicators of intellectual organization. Journal of the American Society for Information Science & Technology 59:10 (2008), 1582–1597, 10.1002/asi.20814.
20. Leydesdorff, L., Kushnir, D., Rafols, I., Interactive overlay maps for US patent (USPTO) data based on international patent classification (IPC). Scientometrics 98:3 (2014), 1583–1599, 10.1007/s11192-012-0923-2.
21. Lopez, P., Romary, L., Experiments with citation mining and key-term extraction for prior art search. CLEF 2010—Conference on multilingual and multimodal information access evaluation, 2010.
22. Mahdabi, P., Crestani, F., The effect of citation analysis on query expansion for patent retrieval. Information Retrieval 17:5-6 (2014), 412–429, 10.1007/s10791-013-9232-5.
23. Marco, A.C., The dynamics of patent citations. Economics Letters 94 (2007), 290–296, 10.1016/j.econlet.2006.08.014.
24. Marra, M., Emrouznejad, A., Ho, W., Edwards, J.S., The value of indirect ties in citation networks: SNA analysis with OWA operator weights. Information Sciences 314 (2015), 135–151, 10.1016/j.ins.2015.02.017 Elsevier Inc.
25. Michel, J., Bettels, B., Patent citation analysis: a close look at the basic input data from patent research reports. Scientometrics 51:1 (2001), 185–201, 10.1023/A:1010577030871.
26. Nakamura, H., Suzuki, S., Kajikawa, Y., The effect of patent family information in patent citation network analysis: a comparative case study in the drivetrain domain. Scientometrics 104:2 (2015), 437–452, 10.1007/s11192-015-1626-2.
27. Narin, F., Patent bibliometrics. Scientometrics 30:1 (1994), 147–155, 10.1007/BF02017219.
28. Newman, M.E.J., Modularity and community structure in networks. Proceedings of the National Academy of Sciences of the United States of America 103:23 (2006), 8577–8582, 10.1073/pnas.0601602103.
29. Ogawa, T., Kajikawa, Y., Assessing the industrial opportunity of academic research with patent relatedness: a case study on polymer electrolyte fuel cells. Technological Forecasting and Social Change 90 (2014), 469–475, 10.1016/j.techfore.2014.04.002 Elsevier Inc.
30. Shibata, N., Kajikawa, Y., Sakata, I., Extracting the commercialization gap between science and technology—case study of a solar cell. Technological Forecasting and Social Change 77:7 (2010), 1147–1155, 10.1016/j.techfore.2010.03.008 Elsevier Inc.
31. Shibata, N., Kajikawa, Y., Sakata, I., Detecting potential technological fronts by comparing scientific papers and patents. Foresight 13:5 (2011), 51–60.
32. Shibata, N., Kajikawa, Y., Sakata, I., Measuring relatedness between communities in a citation network. Journal of the American Society for Information Science and Technology 62:7 (2011), 1360–1369, 10.1002/asi.21477.
33. Shibata, N., Kajikawa, Y., Takeda, Y., Matsushima, K., Comparative study on methods of detecting research fronts using different types of citation. Journal of the American Society for Information Science and Technology 60:3 (2009), 571–580.
34. Small, H., Co-citation in the scientific literature: a new measure of the relationship between two documents. Journal of the American Society for Information Science 24:4 (1973), 265–269, 10.1002/asi.4630240406.
35. Thomson Innovation, Family in Thomson Innovation. 2016 Retrieved 15.02.16 http://www.thomsoninnovation.com/tip-innovation/support/help/patent_fields.htm#family_members.
36. Verspagen, B., Mapping technological trajectories as patent citation networks: a study on the history of fuel cell research. Advances in Complex Systems 10:01 (2007), 93–115, 10.1142/S0219525907000945.
37. Waltman, L., Van Eck, N.J., A new methodology for constructing a publication-level classification system of science. Journal of the American Society for Information Science and Technology 63:12 (2012), 2378–2392, 10.1002/asi.22748.
38. Wang, B., Liu, S., Ding, K., Liu, Z., Xu, J., Identifying technological topics and institution-topic distribution probability for patent competitive intelligence analysis: a case study in LTE technology. Scientometrics(101), 2014, 685–704, 10.1007/s11192-014-1342-3.
39. Whitmore, A., Agarwal, A., Da Xu, Li, The Internet of Things—a survey of topics and trends. Information Systems Frontiers(March), 2014, 1–14, 10.1007/s10796-014-9489-2.
40. Wilson, P., Unused relevant information in research and development. Journal of the American Society for Information Science 46:1 (1995), 45–51, 10.1002/(SICI)1097-4571(199501)46:1<45::AID-ASI5>3.0.CO;2-X Wiley Subscription Services, Inc., A Wiley Company.
41. WIPO, World Intellectual Property Organization, Guide to the international patent classification. 2015 Retrieved 15.02.16 http://www.wipo.int/export/sites/www/classifications/ipc/en/guide/guide_ipc.pdf.
42. Yau, C.K., Porter, A., Newman, N., Suominen, A., Clustering scientific documents with topic modeling. Scientometrics 100:3 (2014), 767–786.
43. Zhang, Y., Zhang, G., Chen, H., Porter, A.L., Zhu, D., Lu, J., Topic analysis and forecasting for science, technology and innovation: methodology with a case study focusing on big data research. Technological Forecasting and Social Change, 2016, 10.1016/j.techfore.2016.01.015 Elsevier B.V.