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

    1 Citation (Scopus)


    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
    ISBN (Electronic)978-1-5090-1114-8
    ISBN (Print)978-1-5090-1115-5
    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


    ConferenceIEEE Technology & Engineering Management Conference, TEMSCON 2017
    Abbreviated titleTEMSCON 2017
    Country/TerritoryUnited States
    CitySanta Clara


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


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