IDEAS home Printed from https://ideas.repec.org/a/eee/infome/v12y2018i1p271-286.html
   My bibliography  Save this article

An improved SAO network-based method for technology trend analysis: A case study of graphene

Author

Listed:
  • Yang, Chao
  • Huang, Cui
  • Su, Jun

Abstract

This paper proposes an improved Subject-Action-Object (SAO) network-based method for analyzing trends in technology development. It attempts to address shortcomings of the traditional SAO network approach, i.e., when setting Subject, Action and Object as nodes of the network, there may be errors in explaining the relationship between Subject Node and Object Node, and the strength of the relationship between subject and object also cannot be identified. The proposed improved SAO network-based method in this paper includes: (1) a new method for constructing an SAO network based on SAO links that calculate the intensity of the relationship between nodes; (2) a model for identifying technology development trends based on structural holes, changes in the distribution of node degrees, and shifts in network centrality. An empirical study on graphene technology is used to illustrate the validity and feasibility of the proposed method.

Suggested Citation

  • Yang, Chao & Huang, Cui & Su, Jun, 2018. "An improved SAO network-based method for technology trend analysis: A case study of graphene," Journal of Informetrics, Elsevier, vol. 12(1), pages 271-286.
    • Handle: RePEc:eee:infome:v:12:y:2018:i:1:p:271-286
    DOI: 10.1016/j.joi.2018.01.006
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1751157717301645
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.joi.2018.01.006?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. 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.
    2. Chao Yang & Donghua Zhu & Xuefeng Wang & Yi Zhang & Guangquan Zhang & Jie Lu, 2017. "Requirement-oriented core technological components’ identification based on SAO analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(3), pages 1229-1248, September.
    3. Péter Érdi & Kinga Makovi & Zoltán Somogyvári & Katherine Strandburg & Jan Tobochnik & Péter Volf & László Zalányi, 2013. "Prediction of emerging technologies based on analysis of the US patent citation network," Scientometrics, Springer;Akadémiai Kiadó, vol. 95(1), pages 225-242, April.
    4. 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.
    5. Robert K. Abercrombie & Akaninyene W. Udoeyop & Bob G. Schlicher, 2012. "A study of scientometric methods to identify emerging technologies via modeling of milestones," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(2), pages 327-342, May.
    6. 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.
    7. Ismael Rafols & Alan L. Porter & Loet Leydesdorff, 2010. "Science overlay maps: A new tool for research policy and library management," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(9), pages 1871-1887, September.
    8. Hanning Guo & Scott Weingart & Katy Börner, 2011. "Mixed-indicators model for identifying emerging research areas," Scientometrics, Springer;Akadémiai Kiadó, vol. 89(1), pages 421-435, October.
    9. Jose M. Vicente-Gomila & Anna Palli & Begoña Calle & Miguel A. Artacho & Sara Jimenez, 2017. "Discovering shifts in competitive strategies in probiotics, accelerated with TechMining," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1907-1923, June.
    10. Murat Bengisu, 2003. "Critical and emerging technologies in Materials, Manufacturing, and Industrial Engineering: A study for priority setting," Scientometrics, Springer;Akadémiai Kiadó, vol. 58(3), pages 473-487, November.
    11. Huang, Mu-Hsuan & Chang, Han-Wen & Chen, Dar-Zen, 2012. "The trend of concentration in scientific research and technological innovation: A reduction of the predominant role of the U.S. in world research & technology," Journal of Informetrics, Elsevier, vol. 6(4), pages 457-468.
    12. Rotolo, Daniele & Hicks, Diana & Martin, Ben R., 2015. "What is an emerging technology?," Research Policy, Elsevier, vol. 44(10), pages 1827-1843.
    13. Mund, Carolin & Neuhäusler, Peter, 2015. "Towards an early-stage identification of emerging topics in science—The usability of bibliometric characteristics," Journal of Informetrics, Elsevier, vol. 9(4), pages 1018-1033.
    14. Liu, Xiang & Jiang, Tingting & Ma, Feicheng, 2013. "Collective dynamics in knowledge networks: Emerging trends analysis," Journal of Informetrics, Elsevier, vol. 7(2), pages 425-438.
    15. 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.
    16. Yi Zhang & Xiao Zhou & Alan L. Porter & Jose M. Vicente Gomila & An Yan, 2014. "Triple Helix innovation in China’s dye-sensitized solar cell industry: hybrid methods with semantic TRIZ and technology roadmapping," Scientometrics, Springer;Akadémiai Kiadó, vol. 99(1), pages 55-75, April.
    17. 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.
    18. 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.
    19. 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.
    20. Vaughan, Liwen & You, Justin, 2010. "Word co-occurrences on Webpages as a measure of the relatedness of organizations: A new Webometrics concept," Journal of Informetrics, Elsevier, vol. 4(4), pages 483-491.
    21. Huang, Cui & Su, Jun & Zhao, Xiaoyuan & Sui, Jigang & Ru, Peng & Zhang, Hanwei & Wang, Xin, 2012. "Government funded renewable energy innovation in China," Energy Policy, Elsevier, vol. 51(C), pages 121-127.
    22. Boon, Wouter & Moors, Ellen, 2008. "Exploring emerging technologies using metaphors - A study of orphan drugs and pharmacogenomics," Social Science & Medicine, Elsevier, vol. 66(9), pages 1915-1927, May.
    23. Silva, Filipi N. & Amancio, Diego R. & Bardosova, Maria & Costa, Luciano da F. & Oliveira, Osvaldo N., 2016. "Using network science and text analytics to produce surveys in a scientific topic," Journal of Informetrics, Elsevier, vol. 10(2), pages 487-502.
    24. Cui Huang & Jun Su & Xiang Xie & Jiang Li, 2014. "Basic research is overshadowed by applied research in China: a policy perspective," Scientometrics, Springer;Akadémiai Kiadó, vol. 99(3), pages 689-694, June.
    25. Zhang, Yi & Zhang, Guangquan & Chen, Hongshu & Porter, Alan L. & Zhu, Donghua & Lu, Jie, 2016. "Topic analysis and forecasting for science, technology and innovation: Methodology with a case study focusing on big data research," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 179-191.
    26. Ta-Shun Cho & Hsin-Yu Shih, 2011. "Patent citation network analysis of core and emerging technologies in Taiwan: 1997–2008," Scientometrics, Springer;Akadémiai Kiadó, vol. 89(3), pages 795-811, December.
    27. Hyunseok Park & Janghyeok Yoon & Kwangsoo Kim, 2012. "Identifying patent infringement using SAO based semantic technological similarities," Scientometrics, Springer;Akadémiai Kiadó, vol. 90(2), pages 515-529, February.
    28. S. Ravikumar & Ashutosh Agrahari & S. N. Singh, 2015. "Mapping the intellectual structure of scientometrics: a co-word analysis of the journal Scientometrics (2005–2010)," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(1), pages 929-955, January.
    29. Ju, Yonghan & Sohn, So Young, 2015. "Patent-based QFD framework development for identification of emerging technologies and related business models: A case of robot technology in Korea," Technological Forecasting and Social Change, Elsevier, vol. 94(C), pages 44-64.
    30. Peters, H. P. F. & van Raan, A. F. J., 1993. "Co-word-based science maps of chemical engineering. Part I: Representations by direct multidimensional scaling," Research Policy, Elsevier, vol. 22(1), pages 23-45, February.
    31. Sungchul Choi & Janghyeok Yoon & Kwangsoo Kim & Jae Yeol Lee & Cheol-Han Kim, 2011. "SAO network analysis of patents for technology trends identification: a case study of polymer electrolyte membrane technology in proton exchange membrane fuel cells," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(3), pages 863-883, September.
    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. Vicente-Gomila, J.M. & Artacho-Ramírez, M.A. & Ting, Ma & Porter, A.L., 2021. "Combining tech mining and semantic TRIZ for technology assessment: Dye-sensitized solar cell as a case," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    2. Mun, Changbae & Yoon, Sejun & Raghavan, Nagarajan & Hwang, Dongwook & Basnet, Subarna & Park, Hyunseok, 2021. "Function score-based technological trend analysis," Technovation, Elsevier, vol. 101(C).
    3. Liu, Zhenfeng & Feng, Jian & Uden, Lorna, 2023. "Technology opportunity analysis using hierarchical semantic networks and dual link prediction," Technovation, Elsevier, vol. 128(C).
    4. Liang Chen & Shuo Xu & Lijun Zhu & Jing Zhang & Xiaoping Lei & Guancan Yang, 2020. "A deep learning based method for extracting semantic information from patent documents," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 289-312, October.
    5. Ren, Haiying & Zhao, Yuhui, 2021. "Technology opportunity discovery based on constructing, evaluating, and searching knowledge networks," Technovation, Elsevier, vol. 101(C).
    6. 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.
    7. Chen, Liang & Xu, Shuo & Zhu, Lijun & Zhang, Jing & Yang, Guancan & Xu, Haiyun, 2022. "A deep learning based method benefiting from characteristics of patents for semantic relation classification," Journal of Informetrics, Elsevier, vol. 16(3).
    8. 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).
    9. 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.

    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. Chao Yang & Donghua Zhu & Xuefeng Wang & Yi Zhang & Guangquan Zhang & Jie Lu, 2017. "Requirement-oriented core technological components’ identification based on SAO analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(3), pages 1229-1248, September.
    2. Liu, Zhenfeng & Feng, Jian & Uden, Lorna, 2023. "Technology opportunity analysis using hierarchical semantic networks and dual link prediction," Technovation, Elsevier, vol. 128(C).
    3. Wooseok Jang & Yongtae Park & Hyeonju Seol, 2021. "Identifying emerging technologies using expert opinions on the future: A topic modeling and fuzzy clustering approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6505-6532, August.
    4. 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.
    5. An, Jaehyeong & Kim, Kyuwoong & Mortara, Letizia & Lee, Sungjoo, 2018. "Deriving technology intelligence from patents: Preposition-based semantic analysis," Journal of Informetrics, Elsevier, vol. 12(1), pages 217-236.
    6. Rotolo, Daniele & Hicks, Diana & Martin, Ben R., 2015. "What is an emerging technology?," Research Policy, Elsevier, vol. 44(10), pages 1827-1843.
    7. Ren, Haiying & Zhao, Yuhui, 2021. "Technology opportunity discovery based on constructing, evaluating, and searching knowledge networks," Technovation, Elsevier, vol. 101(C).
    8. 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.
    9. Erzurumlu, S. Sinan & Pachamanova, Dessislava, 2020. "Topic modeling and technology forecasting for assessing the commercial viability of healthcare innovations," Technological Forecasting and Social Change, Elsevier, vol. 156(C).
    10. 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.
    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. Mun, Changbae & Yoon, Sejun & Raghavan, Nagarajan & Hwang, Dongwook & Basnet, Subarna & Park, Hyunseok, 2021. "Function score-based technological trend analysis," Technovation, Elsevier, vol. 101(C).
    13. 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.
    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. Zhang, Yi & Huang, Ying & Porter, Alan L. & Zhang, Guangquan & Lu, Jie, 2019. "Discovering and forecasting interactions in big data research: A learning-enhanced bibliometric study," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 795-807.
    16. 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.
    17. 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.
    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. Guo, Junfang & Wang, Xuefeng & Li, Qianrui & Zhu, Donghua, 2016. "Subject–action–object-based morphology analysis for determining the direction of technological change," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 27-40.
    20. Hofmann, Peter & Keller, Robert & Urbach, Nils, 2019. "Inter-technology relationship networks: Arranging technologies through text mining," Technological Forecasting and Social Change, Elsevier, vol. 143(C), pages 202-213.

    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:12:y:2018:i:1:p:271-286. 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.