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.",
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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.