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

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  • 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
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    2. Liu, Zhenfeng & Feng, Jian & Uden, Lorna, 2023. "Technology opportunity analysis using hierarchical semantic networks and dual link prediction," Technovation, Elsevier, vol. 128(C).
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    4. 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).
    5. 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.
    6. 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.
    7. Ren, Haiying & Zhao, Yuhui, 2021. "Technology opportunity discovery based on constructing, evaluating, and searching knowledge networks," Technovation, Elsevier, vol. 101(C).
    8. Mun, Changbae & Yoon, Sejun & Raghavan, Nagarajan & Hwang, Dongwook & Basnet, Subarna & Park, Hyunseok, 2021. "Function score-based technological trend analysis," Technovation, Elsevier, vol. 101(C).
    9. 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).

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