Topic modelling approach to knowledge depth and breadth: Analyzing trajectories of technological knowledge

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

    Abstract

    Technology assessment and planning requires that we can reliably, but indirectly, measure knowledge embedded in the organization. Operationalizing knowledge embedded into companies is increasingly challenging but also more and more relevant in the current cross-disciplinary and complex technological environment. Existing approaches for operationalizing company knowledge are based on patent data and analyzing patent classifications. These approaches have, however, significant limitations. In this study, knowledge depth and breadth is studied using full-text patent data from seven large telecommunication companies totaling 157,718 patents. The data was analyzed with Latent Dirichlet Allocation, an unsupervised learning method. The results are quantified using a technological diversity metric, showing temporal changes in companies knowledge. The result show how the operationalization of company knowledge is independent of patent count and that companies have their specific trajectory of knowledge development. The approach offers a novel method of analyzing the knowledge trajectory of a company, compared to existing patent classification based methods.
    Original languageEnglish
    Title of host publication2017 IEEE Technology and Engineering Management Society Conference, TEMSCON 2017
    PublisherIEEE Institute of Electrical and Electronic Engineers
    Pages55-60
    Number of pages6
    ISBN (Electronic)978-1-5090-1114-8
    ISBN (Print)978-1-5090-1115-5
    DOIs
    Publication statusPublished - 31 Jul 2017
    MoE publication typeA4 Article in a conference publication
    EventIEEE Technology & Engineering Management Conference, TEMSCON 2017 - Santa Clara, United States
    Duration: 8 Jun 201710 Jun 2017

    Conference

    ConferenceIEEE Technology & Engineering Management Conference, TEMSCON 2017
    Abbreviated titleTEMSCON 2017
    CountryUnited States
    CitySanta Clara
    Period8/06/1710/06/17

    Fingerprint

    Trajectories
    Industry
    Unsupervised learning
    Technological knowledge
    Trajectory
    Modeling
    Patents
    Telecommunication
    Planning
    Patent data

    Keywords

    • patents
    • companies
    • portfolios
    • communications technology
    • unsupervised learning
    • trajectory
    • time series analysis

    Cite this

    Suominen, A. (2017). Topic modelling approach to knowledge depth and breadth: Analyzing trajectories of technological knowledge. In 2017 IEEE Technology and Engineering Management Society Conference, TEMSCON 2017 (pp. 55-60). [7998354] IEEE Institute of Electrical and Electronic Engineers . https://doi.org/10.1109/TEMSCON.2017.7998354
    Suominen, Arho. / Topic modelling approach to knowledge depth and breadth: Analyzing trajectories of technological knowledge. 2017 IEEE Technology and Engineering Management Society Conference, TEMSCON 2017. IEEE Institute of Electrical and Electronic Engineers , 2017. pp. 55-60
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    abstract = "Technology assessment and planning requires that we can reliably, but indirectly, measure knowledge embedded in the organization. Operationalizing knowledge embedded into companies is increasingly challenging but also more and more relevant in the current cross-disciplinary and complex technological environment. Existing approaches for operationalizing company knowledge are based on patent data and analyzing patent classifications. These approaches have, however, significant limitations. In this study, knowledge depth and breadth is studied using full-text patent data from seven large telecommunication companies totaling 157,718 patents. The data was analyzed with Latent Dirichlet Allocation, an unsupervised learning method. The results are quantified using a technological diversity metric, showing temporal changes in companies knowledge. The result show how the operationalization of company knowledge is independent of patent count and that companies have their specific trajectory of knowledge development. The approach offers a novel method of analyzing the knowledge trajectory of a company, compared to existing patent classification based methods.",
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    Suominen, A 2017, Topic modelling approach to knowledge depth and breadth: Analyzing trajectories of technological knowledge. in 2017 IEEE Technology and Engineering Management Society Conference, TEMSCON 2017., 7998354, IEEE Institute of Electrical and Electronic Engineers , pp. 55-60, IEEE Technology & Engineering Management Conference, TEMSCON 2017, Santa Clara, United States, 8/06/17. https://doi.org/10.1109/TEMSCON.2017.7998354

    Topic modelling approach to knowledge depth and breadth: Analyzing trajectories of technological knowledge. / Suominen, Arho.

    2017 IEEE Technology and Engineering Management Society Conference, TEMSCON 2017. IEEE Institute of Electrical and Electronic Engineers , 2017. p. 55-60 7998354.

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

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    Suominen A. Topic modelling approach to knowledge depth and breadth: Analyzing trajectories of technological knowledge. In 2017 IEEE Technology and Engineering Management Society Conference, TEMSCON 2017. IEEE Institute of Electrical and Electronic Engineers . 2017. p. 55-60. 7998354 https://doi.org/10.1109/TEMSCON.2017.7998354