IDEAS home Printed from https://ideas.repec.org/a/eee/infome/v17y2023i2s1751157723000287.html
   My bibliography  Save this article

Discovering technology and science innovation opportunity based on sentence generation algorithm

Author

Listed:
  • Roh, Taeyeoun
  • Yoon, Byungun

Abstract

Science and technology are crucial elements in discovering innovation opportunities. They have their own practical and theoretical unique meaning in innovation factors. Scientific or technological information can be collected by patents or papers, and various approaches for innovation opportunities discovery are being proposed using text mining. Since a previous methodology using patents and papers discovered opportunities in science or technology itself, they cannot discover opportunities reflecting the science and technology relationship. In addition, since discovered innovation opportunities are formed within the keyword or phrase level, they cannot provide innovation direction or purpose. Therefore, this study suggests a new approach to discovering science and technology innovation opportunities that reflects the science–technology relationship and their concrete directions/purposes using the sentence generation algorithm. An algorithm-generated sentence can contain contextual flow and connection between keywords. In contrast, the generated sentences from the sentence model can reflect science and technology from mass data in a readable sentence. Key innovation factors from science and technology are extracted from generated sentences and then innovation opportunities with specific directions and purposes are suggested.

Suggested Citation

  • Roh, Taeyeoun & Yoon, Byungun, 2023. "Discovering technology and science innovation opportunity based on sentence generation algorithm," Journal of Informetrics, Elsevier, vol. 17(2).
    • Handle: RePEc:eee:infome:v:17:y:2023:i:2:s1751157723000287
    DOI: 10.1016/j.joi.2023.101403
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1751157723000287
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.joi.2023.101403?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. Rotolo, Daniele & Hicks, Diana & Martin, Ben R., 2015. "What is an emerging technology?," Research Policy, Elsevier, vol. 44(10), pages 1827-1843.
    2. Ke, Qing, 2020. "Technological impact of biomedical research: The role of basicness and novelty," Research Policy, Elsevier, vol. 49(7).
    3. Narin, Francis & Olivastro, Dominic, 1992. "Status report: Linkage between technology and science," Research Policy, Elsevier, vol. 21(3), pages 237-249, June.
    4. Momeni, Abdolreza & Rost, Katja, 2016. "Identification and monitoring of possible disruptive technologies by patent-development paths and topic modeling," Technological Forecasting and Social Change, Elsevier, vol. 104(C), pages 16-29.
    5. Geels, Frank W., 2004. "From sectoral systems of innovation to socio-technical systems: Insights about dynamics and change from sociology and institutional theory," Research Policy, Elsevier, vol. 33(6-7), pages 897-920, September.
    6. Salas Gironés, Edgar & van Est, Rinie & Verbong, Geert, 2020. "The role of policy entrepreneurs in defining directions of innovation policy: A case study of automated driving in the Netherlands," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    7. Min-Hsien Weng & Shaoqun Wu & Mark Dyer, 2021. "AI Augmented Approach to Identify Shared Ideas from Large Format Public Consultation," Sustainability, MDPI, vol. 13(16), pages 1-19, August.
    8. Wang, Chenguang & Qiao, Cuixia & Ahmed, Rahil Irfan & Kirikkaleli, Dervis, 2021. "Institutional Quality, Bank Finance and Technological Innovation: A way forward for Fourth Industrial Revolution in BRICS Economies," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    9. Di Wang & Yuman Li, 2022. "Measuring the Policy Effectiveness of China’s New-Energy Vehicle Industry and Its Differential Impact on Supply and Demand Markets," Sustainability, MDPI, vol. 14(13), pages 1-16, July.
    10. Peng, Lu & Liu, Shan & Liu, Rui & Wang, Lin, 2018. "Effective long short-term memory with differential evolution algorithm for electricity price prediction," Energy, Elsevier, vol. 162(C), pages 1301-1314.
    11. Small, Henry & Boyack, Kevin W. & Klavans, Richard, 2014. "Identifying emerging topics in science and technology," Research Policy, Elsevier, vol. 43(8), pages 1450-1467.
    12. Janghyeok Yoon & Kwangsoo Kim, 2011. "Identifying rapidly evolving technological trends for R&D planning using SAO-based semantic patent networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(1), pages 213-228, July.
    13. Dotsika, Fefie & Watkins, Andrew, 2017. "Identifying potentially disruptive trends by means of keyword network analysis," Technological Forecasting and Social Change, Elsevier, vol. 119(C), pages 114-127.
    14. Taeyeoun Roh & Yujin Jeong & Byungun Yoon, 2017. "Developing a Methodology of Structuring and Layering Technological Information in Patent Documents through Natural Language Processing," Sustainability, MDPI, vol. 9(11), pages 1-19, November.
    15. Taeyeoun Roh & Yujin Jeong & Hyejin Jang & Byungun Yoon, 2019. "Technology opportunity discovery by structuring user needs based on natural language processing and machine learning," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-27, October.
    16. Fang Han & Christopher L. Magee, 2018. "Testing the science/technology relationship by analysis of patent citations of scientific papers after decomposition of both science and technology," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(2), pages 767-796, August.
    17. Choi, Jinho & Hwang, Yong-Sik, 2014. "Patent keyword network analysis for improving technology development efficiency," Technological Forecasting and Social Change, Elsevier, vol. 83(C), pages 170-182.
    18. Ji-ping Gao & Kun Ding & Li Teng & Jie Pang, 2012. "Hybrid documents co-citation analysis: making sense of the interaction between science and technology in technology diffusion," Scientometrics, Springer;Akadémiai Kiadó, vol. 93(2), pages 459-471, November.
    19. Yoshiyuki Takeda & Shiho Mae & Yuya Kajikawa & Katsumori Matsushima, 2009. "Nanobiotechnology as an emerging research domain from nanotechnology: A bibliometric approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 80(1), pages 23-38, July.
    20. Narin, Francis & Hamilton, Kimberly S. & Olivastro, Dominic, 1997. "The increasing linkage between U.S. technology and public science," Research Policy, Elsevier, vol. 26(3), pages 317-330, October.
    21. Sunhye Kim & Inchae Park & Byungun Yoon, 2020. "SAO2Vec: Development of an algorithm for embedding the subject–action–object (SAO) structure using Doc2Vec," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-26, February.
    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. Li, Xin & Xie, Qianqian & Jiang, Jiaojiao & Zhou, Yuan & Huang, Lucheng, 2019. "Identifying and monitoring the development trends of emerging technologies using patent analysis and Twitter data mining: The case of perovskite solar cell technology," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 687-705.
    2. 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.
    3. Ke, Qing, 2020. "Technological impact of biomedical research: The role of basicness and novelty," Research Policy, Elsevier, vol. 49(7).
    4. Ki Hong Kim & Young Jae Han & Sugil Lee & Sung Won Cho & Chulung Lee, 2019. "Text Mining for Patent Analysis to Forecast Emerging Technologies in Wireless Power Transfer," Sustainability, MDPI, vol. 11(22), pages 1-24, November.
    5. Porter, Alan L. & Garner, Jon & Carley, Stephen F. & Newman, Nils C., 2019. "Emergence scoring to identify frontier R&D topics and key players," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 628-643.
    6. Keye Wu & Ziyue Xie & Jia Tina Du, 2024. "Does science disrupt technology? Examining science intensity, novelty, and recency through patent-paper citations in the pharmaceutical field," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(9), pages 5469-5491, September.
    7. Puccetti, Giovanni & Giordano, Vito & Spada, Irene & Chiarello, Filippo & Fantoni, Gualtiero, 2023. "Technology identification from patent texts: A novel named entity recognition method," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).
    8. Sun, Bixuan & Kolesnikov, Sergey & Goldstein, Anna & Chan, Gabriel, 2021. "A dynamic approach for identifying technological breakthroughs with an application in solar photovoltaics," Technological Forecasting and Social Change, Elsevier, vol. 165(C).
    9. Porter, Alan L. & Chiavetta, Denise & Newman, Nils C., 2020. "Measuring tech emergence: A contest," Technological Forecasting and Social Change, Elsevier, vol. 159(C).
    10. Xu, Haiyun & Winnink, Jos & Yue, Zenghui & Zhang, Huiling & Pang, Hongshen, 2021. "Multidimensional Scientometric indicators for the detection of emerging research topics," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    11. Li, Xin & Xie, Qianqian & Daim, Tugrul & Huang, Lucheng, 2019. "Forecasting technology trends using text mining of the gaps between science and technology: The case of perovskite solar cell technology," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 432-449.
    12. Qing Ke, 2023. "Interdisciplinary research and technological impact: evidence from biomedicine," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(4), pages 2035-2077, April.
    13. Joung, Junegak & Kim, Kwangsoo, 2017. "Monitoring emerging technologies for technology planning using technical keyword based analysis from patent data," Technological Forecasting and Social Change, Elsevier, vol. 114(C), pages 281-292.
    14. Moehrle, Martin G. & Caferoglu, Hüseyin, 2019. "Technological speciation as a source for emerging technologies. Using semantic patent analysis for the case of camera technology," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 776-784.
    15. Wang, Jean J. & Ye, Fred Y., 2021. "Probing into the interactions between papers and patents of new CRISPR/CAS9 technology: A citation comparison," Journal of Informetrics, Elsevier, vol. 15(4).
    16. Xu, Shuo & Hao, Liyuan & Yang, Guancan & Lu, Kun & An, Xin, 2021. "A topic models based framework for detecting and forecasting emerging technologies," Technological Forecasting and Social Change, Elsevier, vol. 162(C).
    17. Sommarberg, Matti & Mäkinen, Saku J., 2019. "A method for anticipating the disruptive nature of digitalization in the machine-building industry," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 808-819.
    18. Yuan Zhou & Heng Lin & Yufei Liu & Wei Ding, 2019. "A novel method to identify emerging technologies using a semi-supervised topic clustering model: a case of 3D printing industry," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(1), pages 167-185, July.
    19. Lee, Changyong & Kwon, Ohjin & Kim, Myeongjung & Kwon, Daeil, 2018. "Early identification of emerging technologies: A machine learning approach using multiple patent indicators," Technological Forecasting and Social Change, Elsevier, vol. 127(C), pages 291-303.
    20. Park, Inchae & Triulzi, Giorgio & Magee, Christopher L., 2022. "Tracing the emergence of new technology: A comparative analysis of five technological domains," Technological Forecasting and Social Change, Elsevier, vol. 184(C).

    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:eee:infome:v:17:y:2023:i:2:s1751157723000287. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/joi .

    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.