Skip to main content

Advertisement

Springer Nature Link
Log in

Exploring technology fusion by combining latent Dirichlet allocation with Doc2vec: a case of digital medicine and machine learning

  • Published:
Scientometrics Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

As a driving force behind innovation, technological fusion has emerged as a prevailing trend in knowledge innovation. However, current research lacks the semantic analysis and identification of knowledge fusion across technological domains. To bridge this gap, we propose a strategy that combines the latent Dirichlet allocation (LDA) topic model and the Doc2vec neural network semantic model to identify fusion topics across various technology domains. Then, we fuse the semantic information of patents to measure the characteristics of fusion topics in terms of knowledge diversity, homogeneity and cohesion. Applying this method to a case study in the fields of digital medicine and machine learning, we identify six fusion topics from two technology domains, revealing two distinct trends: diffusion from the center to the periphery and clustering from the periphery to the center. The study shows that the fusion measure of topic-semantic granularity can reveal the variability of technology fusion processes at a profound level. The proposed research method will benefit scholars in conducting multi-domain technology fusion research and gaining a deeper understanding of the knowledge fusion process across technology domains from a semantic perspective.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  • Allarakhia, M., & Walsh, S. (2012). Analyzing and organizing nanotechnology development: Application of the institutional analysis development framework to nanotechnology consortia. Technovation, 32(3–4), 216–226.

    Article  Google Scholar 

  • Barbieri, N., Manco, G., Ritacco, E., Carnuccio, M., & Bevacqua, A. (2013). Probabilistic topic models for sequence data. Machine Learning, 93, 5–29.

    Article  MathSciNet  Google Scholar 

  • Bhavsar, K. A., Singla, J., Al-Otaibi, Y. D., Song, O.-Y., Zikria, Y. B., & Bashir, A. K. (2021). medical diagnosis using machine learning: A statistical review. Computers, Materials and Continua, 67(1), 107–125.

    Article  Google Scholar 

  • Caviggioli, F. (2016). Technology fusion: Identification and analysis of the drivers of technology convergence using patent data. Technovation, 55, 22–32.

    Article  Google Scholar 

  • Chen, J., Chen, J., Zhao, S., Zhang, Y., & Tang, J. (2020). Exploiting word embedding for heterogeneous topic model towards patent recommendation. Scientometrics, 125(3), 2091–2108.

    Article  Google Scholar 

  • Cho, Y., & Kim, M. (2014). Entropy and gravity concepts as new methodological indexes to investigate technological convergence: Patent network-based approach. PLoS ONE, 9(6), e98009.

    Article  Google Scholar 

  • Choi, C., Kim, S., & Park, Y. (2007). A patent-based cross impact analysis for quantitative estimation of technological impact: The case of information and communication technology. Technological Forecasting and Social Change, 74(8), 1296–1314.

    Article  Google Scholar 

  • Eggers, J., & Kaul, A. (2018). Motivation and ability? A behavioral perspective on the pursuit of radical invention in multi-technology incumbents. Academy of Management Journal, 61(1), 67–93.

    Article  Google Scholar 

  • Eilers, K., Frischkorn, J., Eppinger, E., Walter, L., & Moehrle, M. G. (2019). Patent-based semantic measurement of one-way and two-way technology convergence: The case of ultraviolet light emitting diodes (UV-LEDs). Technological Forecasting and Social Change, 140, 341–353.

    Article  Google Scholar 

  • Ghaffari, M., Aliahmadi, A., Khalkhali, A., Zakery, A., Daim, T. U., & Yalcin, H. (2023). Topic-based technology mapping using patent data analysis: A case study of vehicle tires. Technological Forecasting and Social Change, 193, 122576.

    Article  Google Scholar 

  • Grawe, M. F., Martins, C. A., & Bonfante, A. G. (2017). Automated patent classification using word embedding. Paper presented at the 2017 16th IEEE international conference on machine learning and applications (ICMLA).

  • Hagen, L. (2018). Content analysis of e-petitions with topic modeling: How to train and evaluate LDA Models? Information Processing and Management, 54(6), 1292–1307.

    Article  Google Scholar 

  • Han, E. J., & Sohn, S. Y. (2016). Technological convergence in standards for information and communication technologies. Technological Forecasting and Social Change, 106, 1–10.

    Article  Google Scholar 

  • Han, Y.-J., & Park, Y. (2006). Patent network analysis of inter-industrial knowledge flows: The case of Korea between traditional and emerging industries. World Patent Information, 28(3), 235–247.

    Article  Google Scholar 

  • Hu, K., Luo, Q., Qi, K., Yang, S., Mao, J., Fu, X., & Zhu, Q. (2019). Understanding the topic evolution of scientific literatures like an evolving city: Using Google Word2Vec model and spatial autocorrelation analysis. Information Processing and Management, 56(4), 1185–1203.

    Article  Google Scholar 

  • Huang, L., Cai, Y., Zhao, E., Zhang, S., Shu, Y., & Fan, J. (2022b). Measuring the interdisciplinarity of Information and Library Science interactions using citation analysis and semantic analysis. Scientometrics, 127(11), 6733–6761.

    Article  Google Scholar 

  • Huang, L., Chen, X., Zhang, Y., Wang, C., Cao, X., & Liu, J. (2022a). Identification of topic evolution: Network analytics with piecewise linear representation and word embedding. Scientometrics, 127(9), 5353–5383.

    Article  Google Scholar 

  • Hullmann, A., & Meyer, M. (2003). Publications and patents in nanotechnology. Scientometrics, 58(3), 507–527.

    Article  Google Scholar 

  • Jia, S., Wang, Y., Wang, W., Zhang, Q., & Zhang, X. (2022). Value of medical imaging artificial intelligence in the diagnosis and treatment of new coronavirus pneumonia. Expert Systems, 39(3), e12740.

    Article  Google Scholar 

  • Jiang, M., Yang, S., & Gao, Q. (2024). Multidimensional indicators to identify emerging technologies: Perspective of technological knowledge flow. Journal of Informetrics, 18(1), 101483.

  • Karvonen, M., & Kässi, T. (2013). Patent citations as a tool for analysing the early stages of convergence. Technological Forecasting and Social Change, 80(6), 1094–1107.

    Article  Google Scholar 

  • Kim, E. H., Jeong, Y. K., Kim, Y., & Song, M. (2022). Exploring scientific trajectories of a large-scale dataset using topic-integrated path extraction. Journal of Informetrics, 16(1), 101242.

    Article  Google Scholar 

  • Kim, K., Jung, S., & Hwang, J. (2019). Technology convergence capability and firm innovation in the manufacturing sector: An approach based on patent network analysis. R&D Management, 49(4), 595–606.

    Article  Google Scholar 

  • Kim, K., & Park, C. (2019). Automatic IPC classification of patent documents using Word2Vec and two layers bidirectional long short term memory network. The Journal of Korean Institute of next Generation Computing, 15(2), 50–60.

    Google Scholar 

  • Ko, N., Yoon, J., & Seo, W. (2014). Analyzing interdisciplinarity of technology fusion using knowledge flows of patents. Expert Systems with Applications, 41(4), 1955–1963.

    Article  Google Scholar 

  • Kwon, S., Porter, A., & Youtie, J. (2016). Navigating the innovation trajectories of technology by combining specialization score analyses for publications and patents: Graphene and nano-enabled drug delivery. Scientometrics, 106, 1057–1071.

    Article  Google Scholar 

  • Le, Q., & Mikolov, T. (2014). Distributed representations of sentences and documents. Paper presented at the international conference on machine learning.

  • Lee, C., Kogler, D. F., & Lee, D. (2019). Capturing information on technology convergence, international collaboration, and knowledge flow from patent documents: A case of information and communication technology. Information Processing & Management, 56(4), 1576–1591.

    Article  Google Scholar 

  • Lee, H., & Kang, P. (2018). Identifying core topics in technology and innovation management studies: A topic model approach. The Journal of Technology Transfer, 43(5), 1291–1317.

    Article  Google Scholar 

  • Lee, W. S., Han, E. J., & Sohn, S. Y. (2015). Predicting the pattern of technology convergence using big-data technology on large-scale triadic patents. Technological Forecasting and Social Change, 100, 317–329.

    Article  Google Scholar 

  • Lenz, D., & Winker, P. (2020). Measuring the diffusion of innovations with paragraph vector topic models. PLoS ONE, 15(1), e0226685.

    Article  Google Scholar 

  • Levy, O., & Goldberg, Y. (2014). Neural word embedding as implicit matrix factorization. Advances in Neural Information Processing Systems, 27, 1.

    Google Scholar 

  • Manochandar, S., Punniyamoorthy, M., & Jeyachitra, R. K. (2020). Development of new seed with modified validity measures for k-means clustering. Computers & Industrial Engineering, 141, 106290.

    Article  Google Scholar 

  • Mastilović, J., Kukolj, D., Kevrešan, Ž, Ostojić, G., Kovač, R., Đerić, M., & Samek, D. U. (2023). Emerging perspectives of blockchains in food supply chain traceability based on patent analysis. Foods, 12(5), 1036.

    Article  Google Scholar 

  • McCann, K. S. (2000). The diversity–stability debate. Nature, 405(6783), 228–233.

    Article  Google Scholar 

  • Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013a). Efficient estimation of word representations in vector space. Preprint arXiv:1301.3781.

  • Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013b). Distributed representations of words and phrases and their compositionality. Advances in Neural Information Processing Systems, 26, 1.

    Google Scholar 

  • Miyazaki, K., & Islam, N. (2007). Nanotechnology systems of innovation—An analysis of industry and academia research activities. Technovation, 27(11), 661–675.

    Article  Google Scholar 

  • Nesta, L., & Saviotti, P. P. (2005). Coherence of the knowledge base and the firm’s innovative performance: Evidence from the US pharmaceutical industry. The Journal of Industrial Economics, 53(1), 123–142.

    Article  Google Scholar 

  • No, H. J., & Park, Y. (2010). trajectory patterns of technology fusion: Trend analysis and taxonomical grouping in nanobiotechnology. Technological Forecasting and Social Change, 77(1), 63–75.

    Article  Google Scholar 

  • Park, H., & Yoon, J. (2014). Assessing coreness and intermediarity of technology sectors using patent co-classification analysis: The case of Korean national R&D. Scientometrics, 98, 853–890.

    Article  Google Scholar 

  • Passing, F., & Moehrle, M. G. (2015). Measuring technological convergence in the field of smart grids: A semantic patent analysis approach using textual corpora of technologies. Paper presented at the 2015 Portland international conference on management of engineering and technology (PICMET).

  • Pennings, J. M., & Puranam, P. (2000). Market convergence & firm strategies: towards a systematic analysis. Retrieved August, 27, 2010.

  • Preschitschek, N., Niemann, H., Leker, J., & Moehrle, M. G. (2013). Anticipating industry convergence: Semantic analyses vs. IPC co-classification. Foresight, 15(6), 446–464.

    Article  Google Scholar 

  • Rafols, I., & Meyer, M. (2010). Diversity and network coherence as indicators of interdisciplinarity: Case studies in bionanoscience. Scientometrics, 82(2), 263–287.

    Article  Google Scholar 

  • Roepke, S., & Moehrle, M. G. (2014). Sequencing the evolution of technologies in a system-oriented way: The concept of technology-DNA. Journal of Engineering and Technology Management, 32, 110–128.

    Article  Google Scholar 

  • Rosenberg, N. (1963). Technological change in the machine tool industry, 1840–1910. The Journal of Economic History, 23(4), 414–443.

    Article  Google Scholar 

  • San Kim, T., & Sohn, S. Y. (2020). Machine-learning-based deep semantic analysis approach for forecasting new technology convergence. Technological Forecasting and Social Change, 157, 120095.

    Article  Google Scholar 

  • Sarica, S., Luo, J., & Wood, K. L. (2020). TechNet: Technology semantic network based on patent data. Expert Systems with Applications, 142, 112995.

    Article  Google Scholar 

  • Shim, W., Kwon, O.-J., Moon, Y.-H., & Kim, K.-H. (2016). Understanding the dynamic convergence phenomenon from the perspective of diversity and persistence: A cross-sector comparative analysis between the United States and South Korea. PLoS ONE, 11(7), e0159249.

    Article  Google Scholar 

  • Sun, X., & Ding, K. (2018). Identifying and tracking scientific and technological knowledge memes from citation networks of publications and patents. Scientometrics, 116, 1735–1748.

    Article  Google Scholar 

  • Takeda, Y., Mae, S., Kajikawa, Y., & Matsushima, K. (2009). Nanobiotechnology as an emerging research domain from nanotechnology: A bibliometric approach. Scientometrics, 80(1), 23–38.

    Article  Google Scholar 

  • Trappey, A. J., Chen, P. P., Trappey, C. V., & Ma, L. (2019). A machine learning approach for solar power technology review and patent evolution analysis. Applied Sciences, 9(7), 1478.

    Article  Google Scholar 

  • Wang, J., & Hsu, T.-Y. (2023). Early discovery of emerging multi-technology convergence for analyzing technology opportunities from patent data: The case of smart health. Scientometrics, 128(8), 4167–4196.

  • Wang, X. F., Yang, X. M., Wang, X. L., Xia, M. G., & Wang, J. Y. (2020). Evaluating the competitiveness of enterprise’s technology based on LDA topic model. Technology Analysis and Strategic Management, 32(2), 208–222.

  • Xie, Q., Zhang, X., Ding, Y., & Song, M. (2020). Monolingual and multilingual topic analysis using LDA and BERT embeddings. Journal of Informetrics, 14(3), 101055.

    Article  Google Scholar 

  • Yoon, B., Kim, S., Kim, S., & Seol, H. (2022). Doc2vec-based link prediction approach using SAO structures: Application to patent network. Scientometrics, 2022, 1–30.

    Google Scholar 

  • Yu, D., Fang, A., & Xu, Z. (2023). Topic research in fuzzy domain: Based on LDA topic modelling. Information Sciences, 648, 119600.

    Article  Google Scholar 

  • Yu, D., & Xiang, B. (2023a). Discovering knowledge map and evolutionary path of HRM and ER: Using the STM combined with Word2vec. International Journal of Manpower, 44(5), 967–988.

    Article  Google Scholar 

  • Yu, D., & Xiang, B. (2023b). Discovering topics and trends in the field of Artificial Intelligence: Using LDA topic modeling. Expert Systems with Applications, 2023, 120114.

    Article  Google Scholar 

  • Yu, D., & Yan, Z. (2022). Combining machine learning and main path analysis to identify research front: From the perspective of science-technology linkage. Scientometrics, 127(7), 4251–4274.

    Article  Google Scholar 

  • Yun, J., & Geum, Y. (2019). Analysing the dynamics of technological convergence using a co-classification approach: A case of healthcare services. Technology Analysis and Strategic Management, 31(12), 1412–1429.

    Article  Google Scholar 

  • Zhang, F., & Wu, S. (2021). Measuring academic entities’ impact by content-based citation analysis in a heterogeneous academic network. Scientometrics, 126(8), 7197–7222.

    Article  Google Scholar 

  • Zhu, W., Ma, B., & Kang, L. (2022). Technology convergence among various technical fields: Improvement of entropy estimation in patent analysis. Scientometrics, 127(12), 7731–7750.

Download references

Acknowledgements

This paper is an outcome of the ISTIC-CLARIVATE ANALYTICS Scientometrics Joint Laboratory Open Fund project "Frontier Identification of Emerging Technologies in a Convergence Perspective" (No. IT2160) and the China Tobacco Corporation for financial support through the project"Research on key core technology requirements and foresight in the tobacco industry" (No. 11020210248). The authors also wish to extend their gratitude to the two anonymous reviewers for their valuable suggestions.

Author information

Authors and Affiliations

  1. College of Cyber Security, Shandong University of Political Science and Law, Jinan, 250014, Shandong, China

    Qiang Gao

  2. School of Information Management, Wuhan University, Wuhan, 430072, China

    Man Jiang

  3. Research Center for Chinese Science Evaluation, Wuhan University, Wuhan, 430072, China

    Man Jiang

Authors
  1. Qiang Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Man Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Qiang Gao: Conceptualization, Investigation, Methodology, Data curation, Visualization, Writing- original draft. Man Jiang: Data curation, Formal analysis, Writing-review and editing.

Corresponding author

Correspondence to Man Jiang.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gao, Q., Jiang, M. Exploring technology fusion by combining latent Dirichlet allocation with Doc2vec: a case of digital medicine and machine learning. Scientometrics 129, 4043–4070 (2024). https://doi.org/10.1007/s11192-024-05069-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11192-024-05069-1

Keywords