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An integrated approach for detecting and quantifying the topic evolutions of patent technology: a case study on graphene field

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Abstract

Comprehensive, in-depth and accurate analyses of patent technology topic evolutions become increasingly significant since the analytical results can offer related personnel the scientific support to explore or trace back to the origin and the development of the technology. However, existing methods of topic evolutions do not facilitate better understanding of how a technology topic has evolved. This paper introduces an integrated method with the LDA topic identification analysis, the improved topic life cycle analysis, and the improved technology entropy analysis for identifying, measuring and interpreting topics evolutions from patent literatures. Multiple indicators we proposed and improved have been used to measure the degree of topic development and identify the topic types of different states. And, the concept of technology entropy has been redefined and improved to measure the changes of evolution intensity and evolution direction among topics, mainly used the topic word and its probability. The results from different methods are mutually connected and complemented. The process and characteristics of topic evolution are further overviewed. Graphene is selected for the case study. The mechanism of evolution and the effect of improved methods are focused on. The research has clearly shown that more accurate and comprehensive results can be achieved for topic evolution by employing this integrated method. Furthermore, the above integration of methods has potential contributions to hot spot detection and potential technology discovery.

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References

  • Allen, M. J., Tung, V. C., & Kaner, R. B. (2010). Honeycomb carbon: A review of graphene. Chemical Reviews, 110(1), 132–145

    Article  Google Scholar 

  • Amoualian, H., Clausel, M., Gaussier, E., & Amini, M. R. (2016) Streaming-LDA: A copula-based approach to modeling topic dependencies in document streams. In: Proceedings of the 22Nd ACM SIGKDD international conference on knowledge discovery and data mining, Vol. 16, (pp. 695–704).

  • Bai, R., & Leng, F. (2013). K-clique research on the evolution of knowledge innovation in community. Library and Information Work, 57(11), 86–94

    Google Scholar 

  • Bart, V. (2007). Mapping technological trajectories as patent citation networks: A study on the history of fuel cell research. Advances in Complex Systems, 10(01), 93–115

    Article  MATH  Google Scholar 

  • Blau, J. (2013). Europe betting big on graphene. Research Technology Management, 56(4), 7–8

    Google Scholar 

  • Blei, D. M., & Lafferty, J. D. (2006) Dynamic topic models. In: Proceedings of the 23rd international conference on machine learning , (pp. 113–120).

  • Blei, D. M., Ng, A. Y., & Jordan, M. I. (2012). Latent dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022

    MATH  Google Scholar 

  • Chen, B., & Ma, J. (2017). Research on topic mining of web text based on HLDA-IDF model. Information Studies Theory & Application, 40(10), 117–122

    Google Scholar 

  • Chen, S., Huang, M., & Chen, D. Z. (2012). Identifying and visualizing technology evolution: A case study of smart grid technology. Technological Forecasting and Social Change, 79(6), 1099–1110

    Article  Google Scholar 

  • Chen, S., Huang, M., & Chen, D. Z. (2013). Exploring technology evolution and transition characteristics of leading countries: A case of fuel cell field. Advanced Engineering Informatics, 27(3), 366–377

    Article  Google Scholar 

  • Cheng, Q., & Wang, X. (2013). A research framework for the evolution of scientific research topics based on the community of common words. Library and Information Work, 57(8), 91–96

    Google Scholar 

  • DFG (2011) Priority programme on graphene. http://www.spp1459.uni-erlangen.de/about-spp-1459/. Accessed 16 March 2018.

  • Geim, A. K. (2009). Graphene: Status and prospects. Science, 324(5934), 1530–1534

    Article  Google Scholar 

  • Girvan, M., & Newman, M. E. J. (2002). Community structure in social and biological networks. Proceedings of the National Academy of Sciences of the United States of America, 99(12), 7821–7826

    Article  MathSciNet  MATH  Google Scholar 

  • Hou, J., & Guo, S. (2017). Analyzing emerging issues with technology entropy method based on patents: Case study of carbon capture. Data Analysis and Knowledge Discovery, 1(1), 55–63

    Google Scholar 

  • Hu, Y. L., Bai, L., & Zhang, W. M. (2012). Modeling and analyzing topic evolution. Acta Automatic Sinica, 38(10), 1690–1697

    Article  Google Scholar 

  • Hummon, N. P., & Doreian, P. (1989). Connectivity in a citation network: The development of DNA theory. Social Networks, 11(1), 39–63

    Article  Google Scholar 

  • Ilevbare, I. M., Probert, D., & Phaal, R. (2013). A review of TRIZ, and its benefits and challenges in practice. Technovation, 33, 30–37

    Article  Google Scholar 

  • Jeong, D. H., & Song, M. (2014). Time gap analysis by the topic model-based temporal technique. Journal of Informetrics, 8(3), 776–790

    Article  Google Scholar 

  • Kim, E., Cho, Y., & Kim, W. (2014). Dynamic patterns of technological convergence in printed electronics technologies: Patent citation network. Scientometrics, 98(2), 975–998

    Article  Google Scholar 

  • Kim, J., Kim, F., & Huang, J. (2010). Seeing graphene-based sheets. Materials Today, 13(3), 28–38

    Article  Google Scholar 

  • Kullback, S., & Leibler, R. A. (1951). On information and sufficiency. Annals of Mathematical Statistics, 22(1), 79–86

    Article  MathSciNet  MATH  Google Scholar 

  • Le, M. H., Ho, T. B., & Nakamori, Y. (2005). Detecting emerging trends from scientific corpora. International Journal of Knowledge and Systems Sciences, 2(2), 63–69

    Google Scholar 

  • Lee, L., (2001) On the eectiveness of the skew divergence for statistical language analysis. In: Proceeding of the 4th international conference on artificial intelligence & statistics, (pp. 65–72).

  • Li, X., Zhang, J., & Yuan, M. (2014). On topic evolution of a scientific journal based on LDA model. Journal of Intelligence, 33(7), 115–121

    Google Scholar 

  • Liao, L., & Le, F. (2017). Research on patent technology evolution based on LDA model and classification number. Journal of Modern Information, 37(5), 13–18

    Google Scholar 

  • Liuph_script home (2017) Python_LDA implementation method detailed. (2017). Retrieved March 16 2018 from https://www.jb51.net/article/126747.htm.

  • National Natural Science Foundation of China (2013) National natural science foundation of China 2013 notice of approval of the joint research fund project of the Hong Kong research grants council. http:/ / www. nsfc.gov.cn/publish/ portal0/ tab442/ info61689.htm. Accessed 16 March 2018.

  • National Nanotechnology Initiative (2014) Supplement to the President’s 2015 budget. The national science and technology council, Executive office of the President. https://www.nano.gov/node/1128. Accessed 16 March 2018.

  • Newman, D., Asuncion, A., Smyth, P., & Welling, M. (2009). Distributed algorithms for topic models. Journal of Machine Learning Research, 10(12), 1801–1828

    MathSciNet  MATH  Google Scholar 

  • Novoselov, K. S. (2004). Electric field effect in atomically thin carbon films. Science, 306(5696), 666–669

    Article  Google Scholar 

  • Owens, E. W. (1986). Demographic trends and saving propensities: A revisit with life cycle theory. Atlantic Economic Journal, 14(4), 106–106

    Article  Google Scholar 

  • Qu, J. B., & Ou, S. Y. (2018). Analyzing topic evolution with topic filtering and relevance. Data Analysis & Knowledge Discovery, 1, 64–73

    Google Scholar 

  • Ruan, G., & Xia, L. (2016). Mining document topics based on association rules. New Technology of Library & Information Service, 12, 50–56

    Google Scholar 

  • SAC: Standardization Administration of the People’s Republic of China (2016) The terminology, definition and code of graphene materials. http://www.sac.gov.cn/sgybzyb/gzdt/bzzxd1/201604/t20160405_206390.htm. Accessed 16 March 2018.

  • Shan, B., & Li, F. (2010). A Survey of topic evolution based on lda. Journal of Chinese Information Processing, 24(6), 43–49

    Google Scholar 

  • Shan, B., & Li, F. (2011). Topic evolution based on seminal document and topic model. New Technology of Library and Information Service, 7, 104–109

    Google Scholar 

  • Shen, J., Wang, X., Chen, Y., Gao, J., Teng, L., & Liao, J. (2012). Analysis on technology focus from the perspective of strategic diagram: A case in the field of 3G mobile communication. Journal of Intelligence, 11, 88–94

    Google Scholar 

  • Shibata, N., Kajikawa, Y., & Sakata, I. (2010). Extracting the commercialization gap between science and technology—Case study of a solar cell. Technological Forecasting and Social Change, 77(7), 1147–1155

    Article  Google Scholar 

  • Sugimoto, C. R., Li, D., Russell, T. G., Finlay, S. C., & Ding, Y. (2014). The shifting sands of disciplinary development: analyzing north american library and information science dissertations using latent dirichlet allocation. Journal of the Association for Information Science & Technology, 62(1), 185–204

    Google Scholar 

  • Sung, H. Y., Yeh, H. Y., Lin, J. K., & Chen, S. H. (2017). A visualization tool of patent topic evolution using a growing cell structure neural network. Scientometrics, 111(8), 1–19

    Google Scholar 

  • Tian, J., & Zhang, C. (2016). Overview of graphene industry development. Jiangsu Sci-ence & Technology Information, 7, 22–24

    Google Scholar 

  • Tijssen, R. (1993). A scientometric cognitive study of neural network research: Expert mental maps versus bibliometric maps. Scientometrics, 28(1), 111–136

    Article  Google Scholar 

  • Vajda, S., Shannon, C. E., & Weaver, W. (1950). The mathematical theory of communication. Bell System Technical Journal, 27(4), 379–423

    MathSciNet  Google Scholar 

  • Wallace, M. L., Gingras, Y., & Duhon, R. (2014). A new approach for detecting scientific specialties from raw cocitation networks. Journal of the Association for Information Science & Technology, 60(2), 240–246

    Google Scholar 

  • Wang, X., & McCallum, A. (2006) Topic over time: A non-markov continuous-time model of topical trends. In: Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining, (pp. 424–433)

  • Wang, Y., Ma, T., Fen, R., Jiang, S., & Huang, J. (2014). Overview of foreign research and development policies in graphene. Advanced Materials Industry, 11, 2–5

    Google Scholar 

  • Yang, B., Liu, D., Jin, D., & Ma, H. (2009). Complex network clustering method. Journal of Software, 20(1), 54–66

    Article  MATH  Google Scholar 

  • Yang, C., Zhu, D., Wang, X., Zhu, F., & Heng, X. (2017). Technical topic analysis in patents: SAO-based LDA modeling. Library and Information Service., 61(3), 86–96

    Google Scholar 

  • Yang, H. X., Gao, B. J., & Sun, H. L. (2016). Extracting topics ofcomputer science literature with lda model. New Technology of Library and Information Service, 11, 20–26

    Google Scholar 

  • Yi, H., Wu, H., Ma, Y., & Ji, F. (2018). Technical topic analysis in patents based on lda and strategic diagram by taking graphene technology as an example. Journal of Intelligence, 37(5), 97–102

    Google Scholar 

  • Yi, N. T., & Jia, L. S. (2012). Indices of novelty for emerging topic detection. Information Processing & Management, 48(2), 303–325

    Article  Google Scholar 

  • Zhang, Y., Zhou, X., & Porter, A. L. (2014). Triple helix innovation in China’s dye-sensitized solar cell industry: Hybrid methods with semantic TRIZ and technology roadmapping. Scientometrics, 99(1), 55–75

    Article  Google Scholar 

  • Zheng, J., Zhao, Z. Y., Zhang, X., Chen, D. Z., Huang, M. H., Lei, X. P., et al. (2011). Industry evolution and key technologies in China based on patent analysis. Scientometrics, 87, 175–188

    Article  Google Scholar 

  • Zhou, Y., & Pu, X. (2014). An empirical study on the performance evaluation of information resources in databases based on entropy-weight topsis. Library & Information Service, 58(8), 36–41

    Google Scholar 

  • Zhu, N., & Wang, F. (2016). Identification of knowledge evolutionary path based on topic relevance: Taking the case of 3D printing field. Library and Information Service, 60(5), 101–109

    Google Scholar 

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Acknowledgements

This research was supported by National Natural Science Foundation of China (Grant No.16BTQ029).

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Authors and Affiliations

  1. Science and Technology Information Research Institute, Shandong University of Technology, Zibo, 255000, China

    Hong Wu & Chang Li

  2. National Science Library, Chinese Academy of Sciences, Beijing, 100190, China

    Huifang Yi

  3. Department of Library, Information and Archives Management, School of Economics and Management, University of Chinese Academy of Sciences, Beijing, 100190, China

    Huifang Yi

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  1. Hong Wu
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  2. Huifang Yi
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Correspondence to Hong Wu.

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Wu, H., Yi, H. & Li, C. An integrated approach for detecting and quantifying the topic evolutions of patent technology: a case study on graphene field. Scientometrics 126, 6301–6321 (2021). https://doi.org/10.1007/s11192-021-04000-2

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