Identifying technological topics and institution-topic distribution probability for patent competitive intelligence analysis: a case study in LTE technology

Scientometrics

Volumn 101, Issue 1, 2014, Pages 685-704

  Wang, Bo ^a,b   Liu, Shengbo ^a,b   Ding, Kun ^a,b   Liu, Zeyuan ^a,b   Xu, Jing ^c  

^a DALIAN UNIVERSITY OF TECHNOLOGY   (China)

^b DREXEL UNIVERSITY   (United States)

^c SICHUAN UNIVERSITY   (China)

Author keywords

Content analysis; Institution topic model; Long term evolution (LTE); Noun phrases extraction; Topic model (LDA)

Indexed keywords

EID: 84939883645     PISSN: 01389130     EISSN: 15882861     Source Type: Journal    
DOI: 10.1007/s11192-014-1342-3     Document Type: Article

References (49)

1. An, X. Y., & Wu, Q. Q. (2011). Co-word analysis of the trends in stem cells field based on subject heading weighting. Scientometrics,88(1), 133–144.
2. Bhattacharya, I. & Getoor, L. (2005). A latent Dirichlet model for unsupervised entity resolution. SIAM International Conference on Data Mining.
3. Blei, D. M. & Lafferty, J. D. (2006). Dynamic topic models. Proceedings of the 23rd international conference on Machine learning, ACM.
4. Blei, D. M., & Lafferty, J. D. (2007). A correlated topic model of science. The Annals of Applied Statistics,1, 17–35.
5. Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. The Journal of Machine Learning Research,3, 993–1022.
6. Breitzman, A. F., & Mogee, M. E. (2002). The many applications of patent analysis. Journal of Information Science,28(3), 187–205.
7. Breitzman, A., & Thomas, P. (2002). Using patent citation analysis to target/value ma candidates. Research-Technology Management,45(5), 28–36.
8. Chen, Y.-H., Chen, C.-Y., & Lee, S.-C. (2011). Technology forecasting and patent strategy of hydrogen energy and fuel cell technologies. International Journal of Hydrogen Energy,36(12), 6957–6969.
9. Church, K. W. (1988). A stochastic parts program and noun phrase parser for unrestricted text. Proceedings of the second conference on Applied natural language processing, Association for Computational Linguistics.
10. Courtial, J.-P., Callon, M., & Sigogneau, A. (1993). The use of patent titles for identifying the topics of invention and forecasting trends. Scientometrics,26(2), 231–242.
11. Ding, Y. (2011). Topic-based PageRank on author cocitation networks. Journal of the American Society for Information Science and Technology,62(3), 449–466.
12. Ding, Y., Chowdhury, G. G., & Foo, S. (2001). Bibliometric cartography of information retrieval research by using co-word analysis. Information Processing and Management,37(6), 817–842.
13. Engelsman, E. C., & van Raan, A. F. (1994). A patent-based cartography of technology. Research Policy,23(1), 1–26.
14. Ernst, H. (1997). The use of patent data for technological forecasting: The diffusion of CNC-technology in the machine tool industry. Small Business Economics,9(4), 361–381.
15. Ernst, H. (2003). Patent information for strategic technology management. World Patent Information,25(3), 233–242.
16. Griffiths, T. L., & Steyvers, M. (2004). Finding scientific topics. Proceedings of the National Academy of Sciences of the United States of America,101(Suppl 1), 5228–5235.
17. Hofer, K. M., Smejkal, A. E., Bilgin, F. Z., & Wuehrer, G. A. (2010). Conference proceedings as a matter of bibliometric studies: The Academy of International Business 2006–2008. Scientometrics,84(3), 845–862.
18. Igami, M. (2008). Exploration of the evolution of nanotechnology via mapping of patent applications. Scientometrics,77(2), 289–308.
19. Kang, J. S. L., Jai, H., & Moon, Y. H. (2010). Systematic approach for monitoring competitor’s technological challenges based upon patent analysis. Information: An International Interdisciplinary Journal,13(2), 339–352.
20. Lee, W. H. (2008). How to identify emerging research fields using scientometrics: An example in the field of information security. Scientometrics,76(3), 503.
21. Lee, S., Lee, S., Seol, H., & Park, Y. (2008). Using patent information for designing new product and technology: Keyword based technology roadmapping. R&D Management,38(2), 169–188.
22. Leydesdorff, L. (1997). Why words and co-words cannot map the development of the sciences. Journal of the American Society for Information Science,48(5), 418–427.
23. Lienou, M., Maitre, H., & Datcu, M. (2010). Semantic annotation of satellite images using latent Dirichlet allocation. IEEE Geoscience and Remote Sensing Letters,7(1), 28–32.
24. Liu, S., & Chen, C. (2013). The differences between latent topics in abstracts and citation contexts of citing papers. Journal of the American Society for Information Science and Technology,64, 627–639.
25. Liu, S.-J., & Shyu, J. (1997). Strategic planning for technology development with patent analysis. International Journal of Technology Management,13(5), 661–680.
26. Milojević, S., Sugimoto, C. R., Yan, E., & Ding, Y. (2011). The cognitive structure of library and information science: Analysis of article title words. Journal of the American Society for Information Science and Technology,62(10), 1933–1953.
27. Misra, H., Yvon, F., Cappé, O., & Jose, J. (2011). Text segmentation: A topic modeling perspective. Information Processing and Management,47(4), 528–544.
28. Niebles, J. C., Wang, H., & Fei-Fei, L. (2008). Unsupervised learning of human action categories using spatial-temporal words. International Journal of Computer Vision,79(3), 299–318.
29. Park, H., Kim, K., & Yoon, J. (2013). A patent intelligence system for strategic technology planning. Expert Systems with Applications,40(7), 2373–2390.
30. Pruteanu-Malinici, I., Ren, L., Paisley, J., Wang, E., & Carin, L. (2010). Hierarchical Bayesian modeling of topics in time-stamped documents. IEEE Transactions on Pattern Analysis and Machine Intelligence,32(6), 996–1011.
31. Rahayu, E. S. R. & Hasibuan, Z. A. (2006). Identification of technology trend on Indonesian patent documents and research reports on chemistry and metallurgy fields. Proceeding Asia Pacific Conference, Singapore.
32. Rosen-Zvi, M., Griffiths, T., Steyvers, M., & Smyth, P. (2004). The author-topic model for authors and documents. Proceedings of the 20th conference on Uncertainty in artificial intelligence, AUAI Press.
33. Saini, G. (2009). Examining the 4G Mobile standard convergence to the LTE standard, Citeseer.
34. Savoy, J. (2013). Authorship attribution based on a probabilistic topic model. Information Processing & Management,49(1), 341–354.
35. Shen, J., Gao, J., & Teng, L. (2012). Derwent manual code co-occurrence: A practical method in patent map. Science of Science and Management of S & T,1, 003.
36. Shih, M.-J., Liu, D.-R., & Hsu, M.-L. (2008). Mining changes in patent trends for competitive intelligence. Advances in knowledge discovery and data mining (pp. 999–1005). Berlin: Springer.
37. Shih, M.-J., Liu, D.-R., & Hsu, M.-L. (2010). Discovering competitive intelligence by mining changes in patent trends. Expert Systems with Applications,37(4), 2882–2890.
38. Sugimoto, C. R., Li, D., Russell, T. G., Finlay, S. C., & Ding, Y. (2011). The shifting sands of disciplinary development: Analyzing North American Library and Information Science dissertations using latent Dirichlet allocation. Journal of the American Society for Information Science and Technology,62(1), 185–204.
39. Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., & Su, Z. (2008). ArnetMiner: Extraction and mining of academic social networks. Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM.
40. Thompson, P., & Fox-Kean, M. (2005). Patent citations and the geography of knowledge spillovers: A reassessment. American Economic Review,95, 450–460.
41. Toutanova, K., & Manning, C. D. (2000). Enriching the knowledge sources used in a maximum entropy part-of-speech tagger. Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: Held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics-Volume 13, Association for Computational Linguistics.
42. Tseng, Y.-H., Lin, C.-J., & Lin, Y.-I. (2007). Text mining techniques for patent analysis. Information Processing and Management,43(5), 1216–1247.
43. von Wartburg, I., Teichert, T., & Rost, K. (2005). Inventive progress measured by multi-stage patent citation analysis. Research Policy,34(10), 1591–1607.
44. Wang, H., Ding, Y., Tang, J., Dong, X., He, B., Qiu, J et al. (2011). Finding complex biological relationships in recent PubMed articles using Bio-LDA. PLoS One,6(3), e17243.
45. Whittaker, J. (1989). Creativity and conformity in science: Titles, keywords and co-word analysis. Social Studies of Science,19(3), 473–496.
46. Wu, Q., Zhang, C., & An, X. (2013). Topic segmentation model based on ATNLDA and co-occurrence theory and its application in stem cell field. Journal of Information Science,39(3), 319–332.
47. Yoon, J., Choi, S., & Kim, K. (2011). Invention property-function network analysis of patents: A case of silicon-based thin film solar cells. Scientometrics,86(3), 687–703.
48. Zhang, J., Wolfram, D., Wolfram, D., Wang, P., Hong, Y., & Gillis, R. (2008). Visualization of health-subject analysis based on query term co-occurrences. Journal of the American Society for Information Science and Technology,59(12), 1933–1947.
49. Zhang, J., Xie, J., Hou, W., Tu, X., Xu, J., Song, F et al. (2012). Mapping the knowledge structure of research on patient adherence: Knowledge domain visualization based co-word analysis and social network analysis. PLoS One,7(4), e34497.