IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v180y2022ics004016252200244x.html
   My bibliography  Save this article

Technology Opportunity Discovery using Deep Learning-based Text Mining and a Knowledge Graph

Author

Listed:
  • Lee, MyoungHoon
  • Kim, Suhyeon
  • Kim, Hangyeol
  • Lee, Junghye

Abstract

To capture emerging technologies in the fast-changing technology market, use of information concerning new technology-based firms (NTBFs) is strongly encouraged, in addition to the information about the technology itself. Especially, NTBFs rapidly respond to technological change, and their investment information is a significant criterion of technology valuation. Therefore, this study proposes a new technology opportunity discovery (TOD) framework that exploits text mining by deep learning and a knowledge graph (KG) by using three data sources: technology, NTBF, and investor data. First, a technology-classification model was developed using technical text data acquired using Doc2vec and logistic regression, and then this model assigned highly-relevant technology fields to NTBFs using NTBFs’ investor relation text data. Next, a KG that considers technology, NTBF, and NTBF’s investor was constructed to represent their relations for TOD by using the results of previous steps. Lastly, considering inter-connectivities of such factors, a TOD index that measures the potential of technologies was proposed. The accuracy and validity of the methods were demonstrated empirically, and an evaluation of emerging technologies identified by the analysis was provided. Our framework will be of great significance as a useful alternative to provide new insights for emerging technologies in the industry and market.

Suggested Citation

  • Lee, MyoungHoon & Kim, Suhyeon & Kim, Hangyeol & Lee, Junghye, 2022. "Technology Opportunity Discovery using Deep Learning-based Text Mining and a Knowledge Graph," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
    • Handle: RePEc:eee:tefoso:v:180:y:2022:i:c:s004016252200244x
    DOI: 10.1016/j.techfore.2022.121718
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S004016252200244X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.techfore.2022.121718?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. Yongho Lee & So Young Kim & Inseok Song & Yongtae Park & Juneseuk Shin, 2014. "Technology opportunity identification customized to the technological capability of SMEs through two-stage patent analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(1), pages 227-244, July.
    2. Lee, Sungjoo & Park, Gwangman & Yoon, Byungun & Park, Jinwoo, 2010. "Open innovation in SMEs--An intermediated network model," Research Policy, Elsevier, vol. 39(2), pages 290-300, March.
    3. Kyuwoong Kim & Kyeongmin Park & Sungjoo Lee, 2019. "Investigating technology opportunities: the use of SAOx analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(1), pages 45-70, January.
    4. Lee, Jeongjin & Kim, Changseok & Shin, Juneseuk, 2017. "Technology opportunity discovery to R&D planning: Key technological performance analysis," Technological Forecasting and Social Change, Elsevier, vol. 119(C), pages 53-63.
    5. Colombo, Massimo G. & D’Adda, Diego & Pirelli, Lorenzo H., 2016. "The participation of new technology-based firms in EU-funded R&D partnerships: The role of venture capital," Research Policy, Elsevier, vol. 45(2), pages 361-375.
    6. Ryu, Do-Hyeon & Lim, Chiehyeon & Kim, Kwang-Jae, 2020. "Development of a service blueprint for the online-to-offline integration in service," Journal of Retailing and Consumer Services, Elsevier, vol. 54(C).
    7. Klevorick, Alvin K. & Levin, Richard C. & Nelson, Richard R. & Winter, Sidney G., 1995. "On the sources and significance of interindustry differences in technological opportunities," Research Policy, Elsevier, vol. 24(2), pages 185-205, March.
    8. Ola Olsson, 2005. "Technological Opportunity and Growth," Journal of Economic Growth, Springer, vol. 10(1), pages 31-53, January.
    9. Changyong Lee & Gyumin Lee, 2019. "Technology opportunity analysis based on recombinant search: patent landscape analysis for idea generation," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(2), pages 603-632, November.
    10. von Wartburg, Iwan & Teichert, Thorsten & Rost, Katja, 2005. "Inventive progress measured by multi-stage patent citation analysis," Research Policy, Elsevier, vol. 34(10), pages 1591-1607, December.
    11. 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.
    12. Choi, Jaewoong & Jeong, Byeongki & Yoon, Janghyeok, 2019. "Technology opportunity discovery under the dynamic change of focus technology fields: Application of sequential pattern mining to patent classifications," Technological Forecasting and Social Change, Elsevier, vol. 148(C).
    13. Lee, In & Shin, Yong Jae, 2018. "Fintech: Ecosystem, business models, investment decisions, and challenges," Business Horizons, Elsevier, vol. 61(1), pages 35-46.
    14. Yoon, Byungun & Park, Inchae & Coh, Byoung-youl, 2014. "Exploring technological opportunities by linking technology and products: Application of morphology analysis and text mining," Technological Forecasting and Social Change, Elsevier, vol. 86(C), pages 287-303.
    15. Yoon, Janghyeok & Park, Hyunseok & Seo, Wonchul & Lee, Jae-Min & Coh, Byoung-youl & Kim, Jonghwa, 2015. "Technology opportunity discovery (TOD) from existing technologies and products: A function-based TOD framework," Technological Forecasting and Social Change, Elsevier, vol. 100(C), pages 153-167.
    16. Abolfazl Ghadiri Moghaddam & Mahdi Sadeghi Kashkoueyeh & Mohammadmahdi Talezadeh & Mohammadreza Aala & Mahdi Ebrahimpour & Mina Tehranypour, 2015. "The Impact of Capital Structure on Corporate Performance," International Journal of Academic Research in Business and Social Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Business and Social Sciences, vol. 5(3), pages 404-418, March.
    17. Park, Youngjin & Yoon, Janghyeok, 2017. "Application technology opportunity discovery from technology portfolios: Use of patent classification and collaborative filtering," Technological Forecasting and Social Change, Elsevier, vol. 118(C), pages 170-183.
    18. Dushnitsky, Gary & Lenox, Michael J., 2006. "When does corporate venture capital investment create firm value?," Journal of Business Venturing, Elsevier, vol. 21(6), pages 753-772, November.
    19. Jose Ramon Saura & Pedro Palos-Sanchez & Antonio Grilo, 2019. "Detecting Indicators for Startup Business Success: Sentiment Analysis Using Text Data Mining," Sustainability, MDPI, vol. 11(3), pages 1-14, February.
    20. Hyunsung D. Kang & Vikram K. Nanda & Haemin D. Park, 2021. "Technology spillovers and capital gains in corporate venture capital investments: evidence from the biopharmaceutical industry," Venture Capital, Taylor & Francis Journals, vol. 23(2), pages 129-155, April.
    21. Lee, Changyong & Jeon, Daeseong & Ahn, Joon Mo & Kwon, Ohjin, 2020. "Navigating a product landscape for technology opportunity analysis: A word2vec approach using an integrated patent-product database," Technovation, Elsevier, vol. 96.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Motohashi, Kazuyuki & Zhu, Chen, 2023. "Identifying technology opportunity using dual-attention model and technology-market concordance matrix," Technological Forecasting and Social Change, Elsevier, vol. 197(C).
    2. Richarz, Jan & Wegewitz, Stephan & Henn, Sarah & Müller, Dirk, 2023. "Graph-based research field analysis by the use of natural language processing: An overview of German energy research," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).
    3. MOTOHASHI Kazuyuki, 2023. "Identifying Technology Opportunity Using a Dual-attention Model and a Technology-market Concordance Matrix," Discussion papers 23024, Research Institute of Economy, Trade and Industry (RIETI).
    4. Jeon, Eunji & Yoon, Naeun & Sohn, So Young, 2023. "Exploring new digital therapeutics technologies for psychiatric disorders using BERTopic and PatentSBERTa," Technological Forecasting and Social Change, Elsevier, vol. 186(PA).
    5. Aidin Salamzadeh & Morteza Hadizadeh & Niloofar Rastgoo & Md. Mizanur Rahman & Soodabeh Radfard, 2022. "Sustainability-Oriented Innovation Foresight in International New Technology Based Firms," Sustainability, MDPI, vol. 14(20), pages 1-21, October.

    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. Ren, Haiying & Zhao, Yuhui, 2021. "Technology opportunity discovery based on constructing, evaluating, and searching knowledge networks," Technovation, Elsevier, vol. 101(C).
    2. Choi, Jaewoong & Lee, Changyong & Yoon, Janghyeok, 2023. "Exploring a technology ecology for technology opportunity discovery: A link prediction approach using heterogeneous knowledge graphs," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).
    3. Wu, Yingwen & Ji, Yangjian, 2023. "Identifying firm-specific technology opportunities from the perspective of competitors by using association rule mining," Journal of Informetrics, Elsevier, vol. 17(2).
    4. Liu, Zhenfeng & Feng, Jian & Uden, Lorna, 2023. "Technology opportunity analysis using hierarchical semantic networks and dual link prediction," Technovation, Elsevier, vol. 128(C).
    5. 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.
    6. Li, Xin & Wu, Yundi & Cheng, Haolun & Xie, Qianqian & Daim, Tugrul, 2023. "Identifying technology opportunity using SAO semantic mining and outlier detection method: A case of triboelectric nanogenerator technology," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
    7. Choi, Jaewoong & Jeong, Byeongki & Yoon, Janghyeok, 2019. "Technology opportunity discovery under the dynamic change of focus technology fields: Application of sequential pattern mining to patent classifications," Technological Forecasting and Social Change, Elsevier, vol. 148(C).
    8. Kyuwoong Kim & Kyeongmin Park & Sungjoo Lee, 2019. "Investigating technology opportunities: the use of SAOx analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(1), pages 45-70, January.
    9. Motohashi, Kazuyuki & Zhu, Chen, 2023. "Identifying technology opportunity using dual-attention model and technology-market concordance matrix," Technological Forecasting and Social Change, Elsevier, vol. 197(C).
    10. Lee, Jiho & Ko, Namuk & Yoon, Janghyeok & Son, Changho, 2021. "An approach for discovering firm-specific technology opportunities: Application of link prediction to F-term networks," Technological Forecasting and Social Change, Elsevier, vol. 168(C).
    11. Song, Kisik & Kim, Karp Soo & Lee, Sungjoo, 2017. "Discovering new technology opportunities based on patents: Text-mining and F-term analysis," Technovation, Elsevier, vol. 60, pages 1-14.
    12. Jungpyo Lee & So Young Sohn, 2021. "Recommendation system for technology convergence opportunities based on self-supervised representation learning," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(1), pages 1-25, January.
    13. Park, Youngjin & Yoon, Janghyeok, 2017. "Application technology opportunity discovery from technology portfolios: Use of patent classification and collaborative filtering," Technological Forecasting and Social Change, Elsevier, vol. 118(C), pages 170-183.
    14. Jinzhu Zhang & Wenqian Yu, 2020. "Early detection of technology opportunity based on analogy design and phrase semantic representation," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 551-576, October.
    15. Xuefeng Wang & Shuo Zhang & Yuqin liu, 2022. "ITGInsight–discovering and visualizing research fronts in the scientific literature," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6509-6531, November.
    16. Teng, Fei & Sun, Yuling & Chen, Fang & Qin, Aning & Zhang, Qi, 2021. "Technology opportunity discovery of proton exchange membrane fuel cells based on generative topographic mapping," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    17. Han, Xiaotong & Zhu, Donghua & Lei, Ming & Daim, Tugrul, 2021. "R&D trend analysis based on patent mining: An integrated use of patent applications and invalidation data," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    18. MOTOHASHI Kazuyuki, 2023. "Identifying Technology Opportunity Using a Dual-attention Model and a Technology-market Concordance Matrix," Discussion papers 23024, Research Institute of Economy, Trade and Industry (RIETI).
    19. Xuan Shi & Lingfei Cai & Hongfang Song, 2019. "Discovering Potential Technology Opportunities for Fuel Cell Vehicle Firms: A Multi-Level Patent Portfolio-Based Approach," Sustainability, MDPI, vol. 11(22), pages 1-22, November.
    20. Choi, Kwang Hun & Kwon, Gyu Hyun, 2023. "Strategies for sensing innovation opportunities in smart grids: In the perspective of interactive relationships between science, technology, and business," Technological Forecasting and Social Change, Elsevier, vol. 187(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:tefoso:v:180:y:2022:i:c:s004016252200244x. 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.sciencedirect.com/science/journal/00401625 .

    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.