Map of science with topic modeling: Comparison of unsupervised learning and human-assigned subject classification.
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...
| Publicado en: | Journal of the Association for Information Science & Technology Vol. 67; no. 10; pp. 2464 - 2477 |
|---|---|
| Autores principales: | , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Oct2016
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=118093763&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 118093763 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23301635 H6JN jtl: Journal of the Association for Information Science & Technology issn: 23301635 maglogo: N pubinfo: dt: Oct2016 vid: 67 iid: 10 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 118093763 118093763 118093763 10.1002/asi.23596 118093763 ppf: 2464 ppct: 13 formats: tig: atl: Map of science with topic modeling: Comparison of unsupervised learning and human-assigned subject classification. aug: au: Suominen, Arho Toivanen, Hannes affil: Innovation, Policy & Economy, VTT Technical Research Centre of Finland, P.O.Box 1000, Espoo 02044, Finland sug: subj: Science Classification Subject Headings Data Analysis, Statistical Classification Methods Human Finland Metadata Time Series Data Analysis Software Algorithms Funding Source 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|