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Combining SAO semantic analysis and morphology analysis to identify technology opportunities

Author

Listed:
  • Xuefeng Wang

    (Beijing Institute of Technology)

  • Pingping Ma

    (Beijing Institute of Technology)

  • Ying Huang

    (Beijing Institute of Technology)

  • Junfang Guo

    (Beijing Institute of Technology)

  • Donghua Zhu

    (Beijing Institute of Technology)

  • Alan L. Porter

    (Georgia Institute of Technology
    Search Technology, Inc.)

  • Zhinan Wang

    (Beijing Institute of Technology)

Abstract

Increasingly complex competitive environments drive corporations in almost all industries to conduct omnibearing innovation activities to enhance their technological innovation capability and international competitiveness. Against this background, we propose subject–action–object (SAO) based morphological analysis to identify technology opportunities by detecting prioritized combinations within the morphology matrix. SAO structures emphasize the key concepts with provision of diverse technology information based on semantic relationships. The combination of SAO semantic structures can support the establishment of matrix, which consists of two dimensions: compositions and properties of technology. Later, novel indicators are used to evaluate the subsequent technological feasibility of each new configuration under a customized analysis and prior combinations aided by a high score can be identified. We apply this method to the case of dye-sensitized solar cells (DSSCs) in patents documents. The approach holds promise to strengthen information support systems for commercial enterprises in technical innovation and market innovation activities. We believe the analysis can be adapted well to fit other technologies, especially in their emerging stage.

Suggested Citation

  • Xuefeng Wang & Pingping Ma & Ying Huang & Junfang Guo & Donghua Zhu & Alan L. Porter & Zhinan Wang, 2017. "Combining SAO semantic analysis and morphology analysis to identify technology opportunities," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 3-24, April.
    • Handle: RePEc:spr:scient:v:111:y:2017:i:1:d:10.1007_s11192-017-2260-y
    DOI: 10.1007/s11192-017-2260-y
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    References listed on IDEAS

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    1. Janghyeok Yoon & Hyunseok Park & Kwangsoo Kim, 2013. "Identifying technological competition trends for R&D planning using dynamic patent maps: SAO-based content analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(1), pages 313-331, January.
    2. 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.
    3. Xuefeng Wang & Rongrong Li & Shiming Ren & Donghua Zhu & Meng Huang & Pengjun Qiu, 2014. "Collaboration network and pattern analysis: case study of dye-sensitized solar cells," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(3), pages 1745-1762, March.
    4. Ying Huang & Jannik Schuehle & Alan L. Porter & Jan Youtie, 2015. "A systematic method to create search strategies for emerging technologies based on the Web of Science: illustrated for ‘Big Data’," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 2005-2022, December.
    5. Byunghoon Kim & Gianluca Gazzola & Jae-Min Lee & Dohyun Kim & Kanghoe Kim & Myong K. Jeong, 2014. "Inter-cluster connectivity analysis for technology opportunity discovery," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(3), pages 1811-1825, March.
    6. Albert, M. B. & Avery, D. & Narin, F. & McAllister, P., 1991. "Direct validation of citation counts as indicators of industrially important patents," Research Policy, Elsevier, vol. 20(3), pages 251-259, June.
    7. 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.
    8. Jose M. Vicente-Gomila, 2014. "The contribution of syntactic–semantic approach to the search for complementary literatures for scientific or technical discovery," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(3), pages 659-673, September.
    9. Liliana Mitkova & Wang Xuefeng & Pengjun Qui & Donghua Zhu & Ming Lei & Alan L. Porter, 2015. "Identification of technology development trends based on subject–action–object analysis: The case of dye-sensitized solar cells," Post-Print hal-01202391, HAL.
    10. 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.
    11. Lee, Changyong & Kang, Bokyoung & Shin, Juneseuk, 2015. "Novelty-focused patent mapping for technology opportunity analysis," Technological Forecasting and Social Change, Elsevier, vol. 90(PB), pages 355-365.
    12. Yi Zhang & Xiao Zhou & Alan L. Porter & Jose M. Vicente Gomila, 2014. "How to combine term clumping and technology roadmapping for newly emerging science & technology competitive intelligence: “problem & solution” pattern based semantic TRIZ tool and case study," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1375-1389, November.
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    Cited by:

    1. 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.
    2. Myeongji Oh & Hyejin Jang & Sunhye Kim & Byungun Yoon, 2023. "Main path analysis for technological development using SAO structure and DEMATEL based on keyword causality," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(4), pages 2079-2104, April.
    3. Zhou, Xiao & Huang, Lu & Porter, Alan & Vicente-Gomila, Jose M., 2019. "Tracing the system transformations and innovation pathways of an emerging technology: Solid lipid nanoparticles," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 785-794.
    4. 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.
    5. 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).
    6. Li, Munan & Wang, Wenshu & Zhou, Keyu, 2021. "Exploring the technology emergence related to artificial intelligence: A perspective of coupling analyses," Technological Forecasting and Social Change, Elsevier, vol. 172(C).
    7. Gaizka Garechana & Rosa Río-Belver & Enara Zarrabeitia & Izaskun Alvarez-Meaza, 2022. "TeknoAssistant : a domain specific tech mining approach for technical problem-solving support," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5459-5473, September.
    8. 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.
    9. He, Xi-jun & Meng, Xue & Dong, Yan-bo & Wu, Yu-ying, 2019. "Demand identification model of potential technology based on SAO structure semantic analysis: The case of new energy and energy saving fields," Technology in Society, Elsevier, vol. 58(C).
    10. 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.
    11. Changbae Mun & Sejun Yoon & Hyunseok Park, 2019. "Structural decomposition of technological domain using patent co-classification and classification hierarchy," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(2), pages 633-652, November.
    12. Wang, Chang & Geng, Hongjun & Sun, Rui & Song, Huiling, 2022. "Technological potential analysis and vacant technology forecasting in the graphene field based on the patent data mining," Resources Policy, Elsevier, vol. 77(C).
    13. 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.
    14. 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).
    15. Teng, Hao & Wang, Nan & Zhao, Hongyu & Hu, Yingtong & Jin, Haitao, 2024. "Enhancing semantic text similarity with functional semantic knowledge (FOP) in patents," Journal of Informetrics, Elsevier, vol. 18(1).

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    More about this item

    Keywords

    Technology opportunities analysis; SAO semantic analysis; Morphological analysis; Technology mining; Dye-sensitized solar cells (DSSCs);
    All these keywords.

    JEL classification:

    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D
    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General

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