摘要(英) |
The knowledge developed in one discipline may revolutionize the practice of another discipline. Furthermore, there is a growing tendency towards the fusion of existing technologies and the integration of applications that spread cross different technological areas. Therefore, a tool that can help to track application trend of cross discipline technologies should be valuable to technology officers in all enterprises. However, none of the research pays special attention to identify cross discipline technology application trend. To achieve the goal, the research adopted Cross-Collection Mixture Model (CCMM) originally developed for identifying concepts among collections of documents; three kinds of concepts are retrieved from collections of documents, namely, common, specific and background themes. The proposed method can also work with Chinese patent documents with which the word segmentation systems tend to append core technologies with other characters to form long words. The proposed method applies 2~3-gram to break the long words to find popular terms and applies the popular terms (technologies) to identify cross discipline technology application trend. To verify the effectiveness of the developed method, four categories of Chinese patent documents (IPC classification code: H04N, H04B, H04L, and G06Q) of the WEBPAT Taiwan were collected. The result shows that the proposed method indeed can track across discipline trendy technology and find 4 cross discipline application technologies terms. Furthermore, our method also can detect the emerging technology or technology opportunity which has great potential for new technology development. This is the unique contribution of this study. |
參考文獻 |
1. Chang, P.-L., et al. (2012). "Investigation of technological trends in flexible display fabrication through patent analysis." Displays 33(2): 68-73.
2. Chen, J., et al. (2012). "New Product Development Speed: Too Much of a Good Thing?" Journal of Product Innovation Management 29(2): 288-303.
3. Chinese Knowledge Information Processing (CKIP): the categorical analysis of Chinese. Technical Report; 1993; 05; Taipei: Institute of Information Science Academia Sinica.
4. Corrocher, N., et al. (2003). The emergence of new technologies in the ICT field: main actors, geographical distribution and knowledge sources, Department of Economics, University of Insubria.
5. Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K., and Harshman, R. (1990). Indexing by Latent Semantic Analysis. Journal of the American Society for Information Science, 41:391–407.
6. Dempster, A. P., et al. (1977). "Maximum likelihood from incomplete data via the EM algorithm." Journal of the royal statistical society. Series B (methodological): 1-38.
7. Hofmann, T. (1999). Probabilistic latent semantic indexing. Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, ACM.
8. Jeong, Y. and B. Yoon (2015). "Development of patent roadmap based on technology roadmap by analyzing patterns of patent development." , Technovation 39: 37-52.
9. Kim, C., et al. (2009). "Developing a technology roadmap for construction R&D through interdisciplinary research efforts." Automation in Construction 18(3): 330-337.
10. Kim, Y. G., et al. (2008). "Visualization of patent analysis for emerging technology." Expert Systems with Applications 34(3): 1804-1812.
11. Kostoff, R. N. (1999). "Science and technology innovation." Technovation 19(10): 593-604.
12. Kostoff, R. N. and R. A. DeMarco (2001). "Extracting information from the literature by text mining." Analytical Chemistry 73(13): 370A-378A.
13. Kroll, H. (2011). "Exploring the validity of patent applications as an indicator of Chinese competitiveness and market structure." World Patent Information 33(1): 23-33.
14. Lee, S., et al. (2009). "Business planning based on technological capabilities: Patent analysis for technology-driven roadmapping." Technological Forecasting and Social Change 76(6): 769-786.
15. Lee, C., et al. (2011). "Monitoring trends of technological changes based on the dynamic patent lattice: A modified formal concept analysis approach." Technological Forecasting and Social Change 78(4): 690-702.
16. Lin Qian-Xiang, Chang Chia-Hui, Chen Chen-Ling (2010). “A Simple and Effective Closed Test for Chinese Word Segmentation Based on Sequence Labeling”. Computational Linguistics and Chinese Language Processing Vol. 15, No. 3-4, September/December 2010, pp. 161-180
17. Liu, Y.-C. and C.-W. Lin (2012). "A new method to compose long unknown Chinese keywords." Journal of Information Science 38(4): 366-382.
18. Mei, Q., et al. (2006). A probabilistic approach to spatiotemporal theme pattern mining on weblogs. Proceedings of the 15th international conference on World Wide Web, ACM.
19. Mei, Q. and C. Zhai (2005). Discovering evolutionary theme patterns from text: an exploration of temporal text mining. Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, ACM.
20. Mei, Q. and C. Zhai (2006). A mixture model for contextual text mining. Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM.
21. Morinaga, S. and K. Yamanishi (2004). Tracking dynamics of topic trends using a finite mixture model. Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, ACM.
22. Papadimitriou, C. H., et al. (1998). Latent semantic indexing: A probabilistic analysis. Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems, ACM.
23. Salton, G. and M. J. McGill (1983). "Introduction to modern information retrieval."
24. Son, C., et al. (2012). "Development of a GTM-based patent map for identifying patent vacuums." Expert Systems with Applications 39(3): 2489-2500.
25. Wong, P.-k. and C. Chan (1996). Chinese word segmentation based on maximum matching and word binding force. Proceedings of the 16th conference on Computational linguistics-Volume 1, Association for Computational Linguistics.
26. Wu, Z. and G. Tseng (1995). "ACTS: An automatic Chinese text segmentation system for full text retrieval." Journal of the American Society for Information Science 46(2): 83-96.
27. Yang, C. H., et al. (2010). "A network analysis of interdisciplinary research relationships: The Korean government’s R&D grant program." Scientometrics 83(1): 77-92.
28. Yoon, B. and Y. Park (2004). "A text-mining-based patent network: Analytical tool for high-technology trend." The Journal of High Technology Management Research 15(1): 37-50.
29. Yoon, J. and K. Kim (2012). "TrendPerceptor: A property–function based technology intelligence system for identifying technology trends from patents." Expert Systems with Applications 39(3): 2927-2938.
30. Zhai, C., et al. (2004). “A cross-collection mixture model for comparative text mining”. Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, ACM.
31. Wikipedia, “Radio-frequency identification”, from https://en.wikipedia.org/wiki/ Radio-frequency_ identification, 2016.
32. Ministry of Economic Affairs Intellectual Property Office (經濟部智慧財產局), “Taiwan Patent Search System” (中華民國專利資訊檢索系統), 2013, from ”http://twpat-simple. tipo.gov.tw/tipotwoc/tipotwkm. |