A topic model analysis of science and technology linkages: A case study in pharmaceutical industry

Samira Ranaei, Arho Suominen, Ozgur Dedehayir

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

    7 Citations (Scopus)


    Science and technology (S&T) linkages have been studied extensively using patent and scientific publication databases. Existing methods used to track S&T linkages, such as analysis of non-patent literature (NPL) or author-inventor matching offer a narrow window for industry level analysis of the data. This paper examines the application of a machine learning algorithm, namely Latent Dirichlet Allocation, to detect the semantic relationship between patent and scientific publication corpus. The case of "Taxol", a cancer drug, is used to illustrate the performance of the unsupervised algorithm in clustering documents with similar topics. In total 26 475 documents retrieved from the Europe PMC database was used a sample for the analysis. Qualitative analysis of the clusters shows that the topic clustering algorithm is valuable approach in detection of patent and publication linkage.
    Original languageEnglish
    Title of host publication2017 IEEE Technology & Engineering Management Conference (TEMSCON)
    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
    • couplings
    • drugs
    • machine learning algorithms
    • algorithm design and analysis
    • classification algorithms
    • analytical models
    • topic modeling
    • technology management
    • taxol
    • machine learning
    • science and technology


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