Abstract We propose new methods to detect paradigmatic fields through simple statistics over a scientific content database. We proposean asymmetric paradigmatic proximity metric between terms which provide insight into hierarchical structure of scientific activity and test our methods on a case studywith a database made of several millions of resources. We also propose overlapping categorization to describe paradigmaticfields as sets of terms that may have several different usages. Terms can also be dynamically clustered providing a high-leveldescription of the evolution of the paradigmatic fields.
%0 Journal Article
%1 chavalarias2008bottomup
%A Chavalarias, David
%A Cointet, Jean-Philippe
%D 2008
%J Scientometrics
%K scientific_disciplines toread taxonomy_learning
%N 1
%P 37--50
%T Bottom-up scientific field detection for dynamical and hierarchical science mapping, methodology and case study
%U http://dx.doi.org/10.1007/s11192-007-1825-6
%V 75
%X Abstract We propose new methods to detect paradigmatic fields through simple statistics over a scientific content database. We proposean asymmetric paradigmatic proximity metric between terms which provide insight into hierarchical structure of scientific activity and test our methods on a case studywith a database made of several millions of resources. We also propose overlapping categorization to describe paradigmaticfields as sets of terms that may have several different usages. Terms can also be dynamically clustered providing a high-leveldescription of the evolution of the paradigmatic fields.
@article{chavalarias2008bottomup,
abstract = {Abstract We propose new methods to detect paradigmatic fields through simple statistics over a scientific content database. We proposean asymmetric paradigmatic proximity metric between terms which provide insight into hierarchical structure of scientific activity and test our methods on a case studywith a database made of several millions of resources. We also propose overlapping categorization to describe paradigmaticfields as sets of terms that may have several different usages. Terms can also be dynamically clustered providing a high-leveldescription of the evolution of the paradigmatic fields.},
added-at = {2011-01-28T11:34:00.000+0100},
author = {Chavalarias, David and Cointet, Jean-Philippe},
biburl = {https://www.bibsonomy.org/bibtex/20fa339fd6b43c77beda39bf0feeac3f8/dbenz},
description = {SpringerLink - Journal Article},
interhash = {67c8e3284451ab2bd5b5d61184ff788c},
intrahash = {0fa339fd6b43c77beda39bf0feeac3f8},
journal = {Scientometrics},
keywords = {scientific_disciplines toread taxonomy_learning},
month = {#apr#},
number = 1,
pages = {37--50},
timestamp = {2013-07-31T15:39:42.000+0200},
title = {Bottom-up scientific field detection for dynamical and hierarchical science mapping, methodology and case study},
url = {http://dx.doi.org/10.1007/s11192-007-1825-6},
volume = 75,
year = 2008
}