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Hierarchical main path analysis to identify decompositional multi-knowledge trajectories

Sejun Yoon (Department of Information Systems, Hanyang University, Seoul, Republic of Korea)
Changbae Mun (Department of Information Systems, Hanyang University, Seoul, Republic of Korea)
Nagarajan Raghavan (Department of Engineering Product Development, Singapore University of Technology and Design, Singapore, Singapore)
Dongwook Hwang (SUTD-MIT International Design Center (IDC), Singapore University of Technology and Design, Singapore, Singapore)
Sohee Kim (Department of Information Systems, Hanyang University, Seoul, Republic of Korea)
Hyunseok Park (Department of Information Systems, Hanyang University, Seoul, Republic of Korea)

Journal of Knowledge Management

ISSN: 1367-3270

Article publication date: 12 June 2020

Issue publication date: 8 March 2021

644

Abstract

Purpose

The purpose of this paper is to propose a quantitative method for identifying multiple and hierarchical knowledge trajectories within a specific technological domain (TD).

Design/methodology/approach

The proposed method as a patent-based data-driven approach is basically based on patent classification systems and patent citation information. Specifically, the method first analyzes hierarchical structure under a specific TD based on patent co-classification and hierarchical relationships between patent classifications. Then, main paths for each sub-TD and overall-TD are generated by knowledge persistence-based main path approach. The all generated main paths at different level are integrated into the hierarchical main paths.

Findings

This paper conducted an empirical analysis by using Genome sequencing technology. The results show that the proposed method automatically identifies three sub-TDs, which are major functionalities in the TD, and generates the hierarchical main paths. The generated main paths show knowledge flows across different sub-TDs and the changing trends in dominant sub-TD over time.

Originality/value

To the best of the authors’ knowledge, the proposed method is the first attempt to automatically generate multiple hierarchical main paths using patent data. The generated main paths objectively show not only knowledge trajectories for each sub-TD but also interactive knowledge flows among sub-TDs. Therefore, the method is definitely helpful to reduce manual work for TD decomposition and useful to understand major trajectories for TD.

Keywords

Acknowledgements

This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (No. 2019S1A5A8036427) and supported by Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Science, ICT & Future Planning (No. 2017R1A2B4012431).

Citation

Yoon, S., Mun, C., Raghavan, N., Hwang, D., Kim, S. and Park, H. (2021), "Hierarchical main path analysis to identify decompositional multi-knowledge trajectories", Journal of Knowledge Management, Vol. 25 No. 2, pp. 454-476. https://doi.org/10.1108/JKM-01-2020-0030

Publisher

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Emerald Publishing Limited

Copyright © 2020, Emerald Publishing Limited

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