IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v162y2021ics0040162520311574.html
   My bibliography  Save this article

Cross-domain function analysis and trend study in Chinese construction industry based on patent semantic analysis

Author

Listed:
  • WANG, La-yin
  • ZHAO, Dong

Abstract

In this paper, cross-domain function analysis and trend forecasting in China's construction industry is investigated in order to understand the technical challenges of special construction projects and improve research and development (R&D). The semantic analysis method is used to extract and cluster key context and function information from China's construction patents under Python. Heat maps are then employed to visualize patent function and evolution in different contexts. The most significant function and least significant function are determined using function matrix heat mapping (FMHM), and the logical equations of searching across domain are utilized for lack of specific technology (LST) and lack of common technology (LCT) in cases of technical vacancies. Additionally, time matrix heat map (TMHM) is used to determine if a technology behind a function will be a promising direction or abandoned, providing a macro path for function analysis. The results illustrate that Equipment & Device with functions of ‘Anti-settlement’ and ‘Bar reinforcing’ may be the future direction of Chinese construction, as well as Renovation & Finishing with techniques of ‘Waterproof & Anti-seepage’ and ‘Green energy efficiency’. However, Materials with functions of ‘Masonry’ and ‘Hanging’ may be eliminated due to policy restrictions and technological growth.

Suggested Citation

  • WANG, La-yin & ZHAO, Dong, 2021. "Cross-domain function analysis and trend study in Chinese construction industry based on patent semantic analysis," Technological Forecasting and Social Change, Elsevier, vol. 162(C).
  • Handle: RePEc:eee:tefoso:v:162:y:2021:i:c:s0040162520311574
    DOI: 10.1016/j.techfore.2020.120331
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040162520311574
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.techfore.2020.120331?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. Patrick S. W. Fong & Xuhua Chang & Qiang Chen, 2018. "Faculty patent assignment in the Chinese mainland: evidence from the top 35 patent application universities," The Journal of Technology Transfer, Springer, vol. 43(1), pages 69-95, February.
    2. Zvi Griliches, 1998. "Patent Statistics as Economic Indicators: A Survey," NBER Chapters, in: R&D and Productivity: The Econometric Evidence, pages 287-343, National Bureau of Economic Research, Inc.
    3. Cho, Han Pil & Lim, Hyunsu & Lee, Dongmin & Cho, Hunhee & Kang, Kyung-In, 2018. "Patent analysis for forecasting promising technology in high-rise building construction," Technological Forecasting and Social Change, Elsevier, vol. 128(C), pages 144-153.
    4. Noailly, Joëlle, 2012. "Improving the energy efficiency of buildings: The impact of environmental policy on technological innovation," Energy Economics, Elsevier, vol. 34(3), pages 795-806.
    5. Altwies, Joy E. & Nemet, Gregory F., 2013. "Innovation in the U.S. building sector: An assessment of patent citations in building energy control technology," Energy Policy, Elsevier, vol. 52(C), pages 819-831.
    6. Wei, Yi-Ming & Kang, Jia-Ning & Yu, Bi-Ying & Liao, Hua & Du, Yun-Fei, 2017. "A dynamic forward-citation full path model for technology monitoring: An empirical study from shale gas industry," Applied Energy, Elsevier, vol. 205(C), pages 769-780.
    7. Tom Magerman & Bart Looy & Xiaoyan Song, 2010. "Exploring the feasibility and accuracy of Latent Semantic Analysis based text mining techniques to detect similarity between patent documents and scientific publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 82(2), pages 289-306, February.
    8. repec:fth:harver:1473 is not listed on IDEAS
    9. Janghyeok Yoon & Kwangsoo Kim, 2012. "Detecting signals of new technological opportunities using semantic patent analysis and outlier detection," Scientometrics, Springer;Akadémiai Kiadó, vol. 90(2), pages 445-461, February.
    10. Noailly, Joëlle & Batrakova, Svetlana, 2010. "Stimulating energy-efficient innovations in the Dutch building sector: Empirical evidence from patent counts and policy lessons," Energy Policy, Elsevier, vol. 38(12), pages 7803-7817, December.
    11. Mu-Hsuan Huang & Li-Yun Chiang & Dar-Zen Chen, 2003. "Constructing a patent citation map using bibliographic coupling: A study of Taiwan's high-tech companies," Scientometrics, Springer;Akadémiai Kiadó, vol. 58(3), pages 489-506, November.
    12. Birgitte Andersen, 1999. "The hunt for S-shaped growth paths in technological innovation: a patent study," Journal of Evolutionary Economics, Springer, vol. 9(4), pages 487-526.
    13. Lim, Up, 2003. "The Spatial Distribution of Innovative Activity in U.S. Metropolitan Areas: Evidence from Patent Data," Journal of Regional Analysis and Policy, Mid-Continent Regional Science Association, vol. 33(2), pages 1-30.
    14. Jan M. Gerken & Martin G. Moehrle, 2012. "A new instrument for technology monitoring: novelty in patents measured by semantic patent analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(3), pages 645-670, June.
    15. Tanunya Visessonchok & Masahiro Sugiyama & Hajime Sasaki & Ichiro Sakata, 2016. "Detection and introduction of emerging technologies for green buildings in Thailand," International Journal of Energy Technology and Policy, Inderscience Enterprises Ltd, vol. 12(1), pages 2-19.
    16. Zubizarreta, Mikel & Cuadrado, Jesús & Iradi, Jon & García, Harkaitz & Orbe, Aimar, 2017. "Innovation evaluation model for macro-construction sector companies: A study in Spain," Evaluation and Program Planning, Elsevier, vol. 61(C), pages 22-37.
    17. Haupt, Reinhard & Kloyer, Martin & Lange, Marcus, 2007. "Patent indicators for the technology life cycle development," Research Policy, Elsevier, vol. 36(3), pages 387-398, April.
    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. Liu, Weiwei & Song, Yifan & Bi, Kexin, 2021. "Exploring the patent collaboration network of China's wind energy industry: A study based on patent data from CNIPA," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).

    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. Costantini, Valeria & Crespi, Francesco & Palma, Alessandro, 2017. "Characterizing the policy mix and its impact on eco-innovation: A patent analysis of energy-efficient technologies," Research Policy, Elsevier, vol. 46(4), pages 799-819.
    2. Yuan Zhou & Fang Dong & Yufei Liu & Liang Ran, 2021. "A deep learning framework to early identify emerging technologies in large-scale outlier patents: an empirical study of CNC machine tool," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 969-994, February.
    3. Valeria Costantini & Francesco Crespi & Alessandro Palma, 2015. "Characterizing the policy mix and its impact on eco-innovation in energy-efficient technologies," SEEDS Working Papers 1115, SEEDS, Sustainability Environmental Economics and Dynamics Studies, revised Jun 2015.
    4. Hain, Daniel S. & Jurowetzki, Roman & Buchmann, Tobias & Wolf, Patrick, 2022. "A text-embedding-based approach to measuring patent-to-patent technological similarity," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
    5. Valeria Costantini & Francesco Crespi & Alessandro Palma, 2014. "Policy Inducement Effects in Energy Efficiency Technologies. An Empirical Analysis on the Residential Sector," SEEDS Working Papers 1914, SEEDS, Sustainability Environmental Economics and Dynamics Studies, revised Aug 2014.
    6. Hyunseok Park & Janghyeok Yoon & Kwangsoo Kim, 2013. "Identification and evaluation of corporations for merger and acquisition strategies using patent information and text mining," Scientometrics, Springer;Akadémiai Kiadó, vol. 97(3), pages 883-909, December.
    7. Su, Hsin-Ning & Moaniba, Igam M., 2017. "Investigating the dynamics of interdisciplinary evolution in technology developments," Technological Forecasting and Social Change, Elsevier, vol. 122(C), pages 12-23.
    8. Nils Grashof, 2020. "Sinking or swimming in the cluster labour pool? A firm-specific analysis of the effect of specialized labour," Jena Economics Research Papers 2020-006, Friedrich-Schiller-University Jena.
    9. 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.
    10. 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.
    11. Rik L. Rozendaal & Herman R. J. Vollebergh, 2021. "Policy-Induced Innovation in Clean Technologies: Evidence from the Car Market," CESifo Working Paper Series 9422, CESifo.
    12. Stern, Nicholas & Sivropoulos-Valero, Anna Valero, 2021. "Innovation, growth and the transition to net-zero emissions," LSE Research Online Documents on Economics 114385, London School of Economics and Political Science, LSE Library.
    13. Altuntas, Serkan & Dereli, Turkay & Kusiak, Andrew, 2015. "Analysis of patent documents with weighted association rules," Technological Forecasting and Social Change, Elsevier, vol. 92(C), pages 249-262.
    14. Jan M. Gerken & Martin G. Moehrle, 2012. "A new instrument for technology monitoring: novelty in patents measured by semantic patent analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(3), pages 645-670, June.
    15. Nicholas Stern & Anna Valero, 2021. "Innovation, growth and the transition to net-zero emissions," CEP Discussion Papers dp1773, Centre for Economic Performance, LSE.
    16. Xi, Xi & Ren, Feifei & Yu, Lean & Yang, Jing, 2023. "Detecting the technology's evolutionary pathway using HiDS-trait-driven tech mining strategy," Technological Forecasting and Social Change, Elsevier, vol. 195(C).
    17. Stern, Nicholas & Valero, Anna, 2021. "Innovation, growth and the transition to net-zero emissions," Research Policy, Elsevier, vol. 50(9).
    18. Roman Jurowetzki, 2015. "Unpacking Big Systems - Natural Language Processing meets Network Analysis. A Study of Smart Grid Development in Denmark," SPRU Working Paper Series 2015-15, SPRU - Science Policy Research Unit, University of Sussex Business School.
    19. Christian Mühlroth & Michael Grottke, 2018. "A systematic literature review of mining weak signals and trends for corporate foresight," Journal of Business Economics, Springer, vol. 88(5), pages 643-687, July.
    20. Jungpyo Lee & So Young Sohn, 2017. "What makes the first forward citation of a patent occur earlier?," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(1), pages 279-298, 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:eee:tefoso:v:162:y:2021:i:c:s0040162520311574. 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.