Map of science based on unsupervised learning

Arho Suominen, Hannes Toivanen

    Research output: Contribution to conferenceConference articleScientific

    Abstract

    The delineation of coordinates is fundamental for the cartography of science, and accurate and credible classification of scientific knowledge presents a persistent challenge in this regard. We present a map of Finnish science based on unsupervised-learning classification, and discuss the advantages and disadvantages of this approach vis-à-vis those generated by human reasoning. We conclude that from theoretical and practical perspectives there exist several challenges for human reasoning-based classification frameworks of scientific knowledge, as they typically try to fit new-to-the-world knowledge into historical models of scientific knowledge, and cannot easily be deployed for new large-scale data sets. Automated classification schemes, in contrast, generate classification models only from the available text corpus, thereby identifying credibly novel bodies of knowledge. They also lend themselves to versatile large-scale data analysis, and enable a range of Big Data possibilities. However, we also argue that it is neither possible nor fruitful to declare one or another method a superior approach in terms of realism to classify scientific knowledge, and we believe that the merits of each approach are dependent on the practical objectives of analysis.
    Original languageEnglish
    Publication statusPublished - 2015
    EventAtlanta Conference on Science and Innovation Policy 2015 - Atlanta, United States
    Duration: 17 Sep 201519 Sep 2015
    Conference number: 6

    Conference

    ConferenceAtlanta Conference on Science and Innovation Policy 2015
    CountryUnited States
    CityAtlanta
    Period17/09/1519/09/15

    Fingerprint

    learning
    cartography
    science

    Keywords

    • science
    • classfication
    • machine-learning
    • topic modelling

    Cite this

    Suominen, A., & Toivanen, H. (2015). Map of science based on unsupervised learning. Paper presented at Atlanta Conference on Science and Innovation Policy 2015, Atlanta, United States.
    Suominen, Arho ; Toivanen, Hannes. / Map of science based on unsupervised learning. Paper presented at Atlanta Conference on Science and Innovation Policy 2015, Atlanta, United States.
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    title = "Map of science based on unsupervised learning",
    abstract = "The delineation of coordinates is fundamental for the cartography of science, and accurate and credible classification of scientific knowledge presents a persistent challenge in this regard. We present a map of Finnish science based on unsupervised-learning classification, and discuss the advantages and disadvantages of this approach vis-{\`a}-vis those generated by human reasoning. We conclude that from theoretical and practical perspectives there exist several challenges for human reasoning-based classification frameworks of scientific knowledge, as they typically try to fit new-to-the-world knowledge into historical models of scientific knowledge, and cannot easily be deployed for new large-scale data sets. Automated classification schemes, in contrast, generate classification models only from the available text corpus, thereby identifying credibly novel bodies of knowledge. They also lend themselves to versatile large-scale data analysis, and enable a range of Big Data possibilities. However, we also argue that it is neither possible nor fruitful to declare one or another method a superior approach in terms of realism to classify scientific knowledge, and we believe that the merits of each approach are dependent on the practical objectives of analysis.",
    keywords = "science, classfication, machine-learning, topic modelling",
    author = "Arho Suominen and Hannes Toivanen",
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    Suominen, A & Toivanen, H 2015, 'Map of science based on unsupervised learning', Paper presented at Atlanta Conference on Science and Innovation Policy 2015, Atlanta, United States, 17/09/15 - 19/09/15.

    Map of science based on unsupervised learning. / Suominen, Arho; Toivanen, Hannes.

    2015. Paper presented at Atlanta Conference on Science and Innovation Policy 2015, Atlanta, United States.

    Research output: Contribution to conferenceConference articleScientific

    TY - CONF

    T1 - Map of science based on unsupervised learning

    AU - Suominen, Arho

    AU - Toivanen, Hannes

    N1 - Project : 101488

    PY - 2015

    Y1 - 2015

    N2 - The delineation of coordinates is fundamental for the cartography of science, and accurate and credible classification of scientific knowledge presents a persistent challenge in this regard. We present a map of Finnish science based on unsupervised-learning classification, and discuss the advantages and disadvantages of this approach vis-à-vis those generated by human reasoning. We conclude that from theoretical and practical perspectives there exist several challenges for human reasoning-based classification frameworks of scientific knowledge, as they typically try to fit new-to-the-world knowledge into historical models of scientific knowledge, and cannot easily be deployed for new large-scale data sets. Automated classification schemes, in contrast, generate classification models only from the available text corpus, thereby identifying credibly novel bodies of knowledge. They also lend themselves to versatile large-scale data analysis, and enable a range of Big Data possibilities. However, we also argue that it is neither possible nor fruitful to declare one or another method a superior approach in terms of realism to classify scientific knowledge, and we believe that the merits of each approach are dependent on the practical objectives of analysis.

    AB - The delineation of coordinates is fundamental for the cartography of science, and accurate and credible classification of scientific knowledge presents a persistent challenge in this regard. We present a map of Finnish science based on unsupervised-learning classification, and discuss the advantages and disadvantages of this approach vis-à-vis those generated by human reasoning. We conclude that from theoretical and practical perspectives there exist several challenges for human reasoning-based classification frameworks of scientific knowledge, as they typically try to fit new-to-the-world knowledge into historical models of scientific knowledge, and cannot easily be deployed for new large-scale data sets. Automated classification schemes, in contrast, generate classification models only from the available text corpus, thereby identifying credibly novel bodies of knowledge. They also lend themselves to versatile large-scale data analysis, and enable a range of Big Data possibilities. However, we also argue that it is neither possible nor fruitful to declare one or another method a superior approach in terms of realism to classify scientific knowledge, and we believe that the merits of each approach are dependent on the practical objectives of analysis.

    KW - science

    KW - classfication

    KW - machine-learning

    KW - topic modelling

    M3 - Conference article

    ER -

    Suominen A, Toivanen H. Map of science based on unsupervised learning. 2015. Paper presented at Atlanta Conference on Science and Innovation Policy 2015, Atlanta, United States.