IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v129y2024i11d10.1007_s11192-023-04812-4.html
   My bibliography  Save this article

Detecting technological recombination using semantic analysis and dynamic network analysis

Author

Listed:
  • Xiaoli Cao

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Xiang Chen

    (Beijing Institute of Technology)

  • Lu Huang

    (Beijing Institute of Technology)

  • Lijie Deng

    (Beijing Institute of Technology)

  • Yijie Cai

    (Beijing Institute of Technology)

  • Hang Ren

    (Beijing Institute of Technology)

Abstract

Technological recombinative innovation is a crucial way of innovation, and detecting technological recombination can effectively identify the technical elements with recombinative innovation potential in the future. This study proposes a novel method for detecting technological recombination by combining semantic analysis and dynamic network analysis. The framework accurately captures the hidden semantic changes behind keywords over time and deeply excavates the dynamic evolution characteristics of keyword networks in the development process, which effectively improves the accuracy of the identification results of technological recombination. Firstly, the dynamic word embedding model is applied to generate word vectors, and construct the dynamic keyword networks. Then, the dynamic network link prediction method is trained to predict the future network and the possibility of connection between keywords is calculated, which represents the technological recombination potential value. Finally, in order to identify potential recombination opportunities of crucial technologies in the field, SLM community detection is combined with the PageRank algorithm to identify core keywords in communities of the future network, and then technological recombination candidates corresponding to core keywords are detected. A case study on artificial intelligence domain demonstrates the reliability of the methodology.

Suggested Citation

  • Xiaoli Cao & Xiang Chen & Lu Huang & Lijie Deng & Yijie Cai & Hang Ren, 2024. "Detecting technological recombination using semantic analysis and dynamic network analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(11), pages 7385-7416, November.
    • Handle: RePEc:spr:scient:v:129:y:2024:i:11:d:10.1007_s11192-023-04812-4
    DOI: 10.1007/s11192-023-04812-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-023-04812-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11192-023-04812-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Xuefeng Wang & Huichao Ren & Yun Chen & Yuqin Liu & Yali Qiao & Ying Huang, 2019. "Measuring patent similarity with SAO semantic analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(1), pages 1-23, October.
    2. Ludo Waltman & Nees Eck, 2013. "A smart local moving algorithm for large-scale modularity-based community detection," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 86(11), pages 1-14, November.
    3. Zhang, Yi & Lu, Jie & Liu, Feng & Liu, Qian & Porter, Alan & Chen, Hongshu & Zhang, Guangquan, 2018. "Does deep learning help topic extraction? A kernel k-means clustering method with word embedding," Journal of Informetrics, Elsevier, vol. 12(4), pages 1099-1117.
    4. Byeongseop Kim & Yongkuk Jeong & Jong Gye Shin, 2020. "Spatial arrangement using deep reinforcement learning to minimise rearrangement in ship block stockyards," International Journal of Production Research, Taylor & Francis Journals, vol. 58(16), pages 5062-5076, July.
    5. Ren, Haiying & Zhao, Yuhui, 2021. "Technology opportunity discovery based on constructing, evaluating, and searching knowledge networks," Technovation, Elsevier, vol. 101(C).
    6. Xiao Zhou & Lu Huang & Yi Zhang & Miaomiao Yu, 2019. "A hybrid approach to detecting technological recombination based on text mining and patent network analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(2), pages 699-737, November.
    7. Takano, Yasutomo & Mejia, Cristian & Kajikawa, Yuya, 2016. "Unconnected component inclusion technique for patent network analysis: Case study of Internet of Things-related technologies," Journal of Informetrics, Elsevier, vol. 10(4), pages 967-980.
    8. Park, Inchae & Yoon, Byungun, 2018. "Technological opportunity discovery for technological convergence based on the prediction of technology knowledge flow in a citation network," Journal of Informetrics, Elsevier, vol. 12(4), pages 1199-1222.
    9. Na Liu & Philip Shapira & Xiaoxu Yue, 2021. "Tracking developments in artificial intelligence research: constructing and applying a new search strategy," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 3153-3192, April.
    10. Faïz Gallouj, 1997. "Towards a neo-Schumpeterian theory of innovation in services?," Science and Public Policy, Oxford University Press, vol. 24(6), pages 405-420, December.
    11. Madeline K. Kneeland & Melissa A. Schilling & Barak S. Aharonson, 2020. "Exploring Uncharted Territory: Knowledge Search Processes in the Origination of Outlier Innovation," Organization Science, INFORMS, vol. 31(3), pages 535-557, May.
    12. Ma, Tingting & Zhou, Xiao & Liu, Jia & Lou, Zhenkai & Hua, Zhaoting & Wang, Ruitao, 2021. "Combining topic modeling and SAO semantic analysis to identify technological opportunities of emerging technologies," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    13. Tao Zhou & Linyuan Lü & Yi-Cheng Zhang, 2009. "Predicting missing links via local information," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 71(4), pages 623-630, October.
    14. Lu Huang & Yijie Cai & Erdong Zhao & Shengting Zhang & Yue Shu & Jiao Fan, 2022. "Measuring the interdisciplinarity of Information and Library Science interactions using citation analysis and semantic analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6733-6761, November.
    15. Lu Huang & Xiang Chen & Yi Zhang & Yihe Zhu & Suyi Li & Xingxing Ni, 2021. "Dynamic network analytics for recommending scientific collaborators," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(11), pages 8789-8814, November.
    16. Guan, Jian Cheng & Yan, Yan, 2016. "Technological proximity and recombinative innovation in the alternative energy field," Research Policy, Elsevier, vol. 45(7), pages 1460-1473.
    17. Jung, Sukhwan & Segev, Aviv, 2022. "DAC: Descendant-aware clustering algorithm for network-based topic emergence prediction," Journal of Informetrics, Elsevier, vol. 16(3).
    18. Zhigao Liu & Yimei Yin & Weidong Liu & Michael Dunford, 2015. "Visualizing the intellectual structure and evolution of innovation systems research: a bibliometric analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(1), pages 135-158, April.
    19. Verhoeven, Dennis & Bakker, Jurriën & Veugelers, Reinhilde, 2016. "Measuring technological novelty with patent-based indicators," Research Policy, Elsevier, vol. 45(3), pages 707-723.
    20. Hong, Suckwon & Kim, Juram & Woo, Han-Gyun & Kim, Young-Choon & Lee, Changyong, 2022. "Screening ideas in the early stages of technology development: A word2vec and convolutional neural network approach," Technovation, Elsevier, vol. 112(C).
    21. Joon Hyung Cho & Jungpyo Lee & So Young Sohn, 2021. "Predicting future technological convergence patterns based on machine learning using link prediction," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 5413-5429, July.
    22. Zhang, Yi & Wu, Mengjia & Miao, Wen & Huang, Lu & Lu, Jie, 2021. "Bi-layer network analytics: A methodology for characterizing emerging general-purpose technologies," Journal of Informetrics, Elsevier, vol. 15(4).
    23. Koen Frenken, 2006. "Technological innovation and complexity theory," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 15(2), pages 137-155.
    24. Souzanchi Kashani, Ebrahim & Radosevic, Slavo & Kiamehr, Mehdi & Gholizadeh, Hossein, 2022. "The intellectual evolution of the technological catch-up literature: Bibliometric analysis," Research Policy, Elsevier, vol. 51(7).
    25. Kai Hu & Huayi Wu & Kunlun Qi & Jingmin Yu & Siluo Yang & Tianxing Yu & Jie Zheng & Bo Liu, 2018. "A domain keyword analysis approach extending Term Frequency-Keyword Active Index with Google Word2Vec model," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(3), pages 1031-1068, March.
    26. Lu Huang & Xiang Chen & Yi Zhang & Changtian Wang & Xiaoli Cao & Jiarun Liu, 2022. "Identification of topic evolution: network analytics with piecewise linear representation and word embedding," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5353-5383, September.
    27. Chongfeng Wang & Gupeng Zhang, 2019. "Examining the moderating effect of technology spillovers embedded in the intra- and inter-regional collaborative innovation networks of China," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(2), pages 561-593, May.
    28. Hou, Jianhua & Tang, Shiqi & Zhang, Yang & Song, Haoyang, 2023. "Does prior knowledge affect patent technology diffusion? A semantic-based patent citation contribution analysis," Journal of Informetrics, Elsevier, vol. 17(2).
    29. Jongchan Kim & Jaehyun Choi & Sangsung Park & Dongsik Jang, 2018. "Patent Keyword Extraction for Sustainable Technology Management," Sustainability, MDPI, vol. 10(4), pages 1-18, April.
    30. Corredoira, Rafael A. & Banerjee, Preeta M., 2015. "Measuring patent's influence on technological evolution: A study of knowledge spanning and subsequent inventive activity," Research Policy, Elsevier, vol. 44(2), pages 508-521.
    31. Gozuacik, Necip & Sakar, C. Okan & Ozcan, Sercan, 2023. "Technological forecasting based on estimation of word embedding matrix using LSTM networks," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
    32. Marc Gruber & Dietmar Harhoff & Karin Hoisl, 2013. "Knowledge Recombination Across Technological Boundaries: Scientists vs. Engineers," Management Science, INFORMS, vol. 59(4), pages 837-851, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xiao Zhou & Lu Huang & Yi Zhang & Miaomiao Yu, 2019. "A hybrid approach to detecting technological recombination based on text mining and patent network analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(2), pages 699-737, November.
    2. Tingting Wei & Danyu Feng & Shiling Song & Cai Zhang, 2024. "An extraction and novelty evaluation framework for technology knowledge elements of patents," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(11), pages 7417-7442, November.
    3. Lijie Feng & Yilang Li & Zhenfeng Liu & Jinfeng Wang, 2020. "Idea Generation and New Direction for Exploitation Technologies of Coal-Seam Gas through Recombinative Innovation and Patent Analysis," IJERPH, MDPI, vol. 17(8), pages 1-21, April.
    4. Lu Huang & Xiang Chen & Yi Zhang & Changtian Wang & Xiaoli Cao & Jiarun Liu, 2022. "Identification of topic evolution: network analytics with piecewise linear representation and word embedding," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5353-5383, September.
    5. Huang, Lu & Chen, Xiang & Ni, Xingxing & Liu, Jiarun & Cao, Xiaoli & Wang, Changtian, 2021. "Tracking the dynamics of co-word networks for emerging topic identification," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
    6. Jiang, Cuiqing & Zhou, Yiru & Chen, Bo, 2023. "Mining semantic features in patent text for financial distress prediction," Technological Forecasting and Social Change, Elsevier, vol. 190(C).
    7. Zhao, Shengchao & Zeng, Deming & Li, Jian & Feng, Ke & Wang, Yao, 2023. "Quantity or quality: The roles of technology and science convergence on firm innovation performance," Technovation, Elsevier, vol. 126(C).
    8. Wang, Fang, 2024. "Does the recombination of distant scientific knowledge generate valuable inventions? An analysis of pharmaceutical patents," Technovation, Elsevier, vol. 130(C).
    9. Brea, Edgar, 2024. "The yin yang of AI: Exploring how commercial and non-commercial orientations shape machine learning innovation," Research Policy, Elsevier, vol. 53(6).
    10. Jing Ma & Yaohui Pan & Chih-Yi Su, 2022. "Organization-oriented technology opportunities analysis based on predicting patent networks: a case of Alzheimer’s disease," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5497-5517, September.
    11. Zhenyu Yang & Wenyu Zhang & Zhimin Wang & Xiaoling Huang, 2024. "A deep learning-based method for predicting the emerging degree of research topics using emerging index," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(7), pages 4021-4042, July.
    12. Wang, Liang & Li, Munan, 2024. "An exploration method for technology forecasting that combines link prediction with graph embedding: A case study on blockchain," Technological Forecasting and Social Change, Elsevier, vol. 208(C).
    13. Liu, Xielin & Ji, Xiaohui & Ge, Shuang, 2024. "Does the complexity and embeddedness of knowledge recombination contribute to economic development? —— Observations from prefecture cities in China," Research Policy, Elsevier, vol. 53(2).
    14. Lu Huang & Yijie Cai & Erdong Zhao & Shengting Zhang & Yue Shu & Jiao Fan, 2022. "Measuring the interdisciplinarity of Information and Library Science interactions using citation analysis and semantic analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6733-6761, November.
    15. Zhang, Yi & Wu, Mengjia & Miao, Wen & Huang, Lu & Lu, Jie, 2021. "Bi-layer network analytics: A methodology for characterizing emerging general-purpose technologies," Journal of Informetrics, Elsevier, vol. 15(4).
    16. Lijie Feng & Yuxiang Niu & Zhenfeng Liu & Jinfeng Wang & Ke Zhang, 2019. "Discovering Technology Opportunity by Keyword-Based Patent Analysis: A Hybrid Approach of Morphology Analysis and USIT," Sustainability, MDPI, vol. 12(1), pages 1-35, December.
    17. Lu Huang & Xiang Chen & Yi Zhang & Yihe Zhu & Suyi Li & Xingxing Ni, 2021. "Dynamic network analytics for recommending scientific collaborators," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(11), pages 8789-8814, November.
    18. Yi Zhang & Mengjia Wu & Guangquan Zhang & Jie Lu, 2023. "Stepping beyond your comfort zone: Diffusion‐based network analytics for knowledge trajectory recommendation," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(7), pages 775-790, July.
    19. Guan, Jian Cheng & Yan, Yan, 2016. "Technological proximity and recombinative innovation in the alternative energy field," Research Policy, Elsevier, vol. 45(7), pages 1460-1473.
    20. Kathryn Rudie Harrigan & Maria Chiara Guardo & Bo Cowgill, 2017. "Multiplicative-innovation synergies: tests in technological acquisitions," The Journal of Technology Transfer, Springer, vol. 42(5), pages 1212-1233, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:scient:v:129:y:2024:i:11:d:10.1007_s11192-023-04812-4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.