Premium
Map of science with topic modeling: Comparison of unsupervised learning and human‐assigned subject classification
Author(s) -
Suominen Arho,
Toivanen Hannes
Publication year - 2016
Publication title -
journal of the association for information science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.903
H-Index - 145
eISSN - 2330-1643
pISSN - 2330-1635
DOI - 10.1002/asi.23596
Subject(s) - computer science , subject (documents) , data science , artificial intelligence , scale (ratio) , range (aeronautics) , big data , sociology of scientific knowledge , machine learning , data mining , epistemology , philosophy , physics , materials science , quantum mechanics , library science , composite material
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 F innish 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.