Bibliometric-enhanced Retrieval Models for Big Scholarly Information Systems
Digital Libraries
2016-11-17 v1
Abstract
Bibliometric techniques are not yet widely used to enhance retrieval processes in digital libraries, although they offer value-added effects for users. In this paper we will explore how statistical modelling of scholarship, such as Bradfordizing or network analysis of coauthorship network, can improve retrieval services for specific communities, as well as for large, cross-domain large collections. This paper aims to raise awareness of the missing link between information retrieval (IR) and bibliometrics / scientometrics and to create a common ground for the incorporation of bibliometric-enhanced services into retrieval at the digital library interface.
Cite
@article{arxiv.1309.7949,
title = {Bibliometric-enhanced Retrieval Models for Big Scholarly Information Systems},
author = {Philipp Mayr and Peter Mutschke},
journal= {arXiv preprint arXiv:1309.7949},
year = {2016}
}
Comments
4 pages, IEEE BigData 2013, Workshop on Scholarly Big Data: Challenges and Ideas