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 workshop 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 collections. This workshop 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. © 2014 Springer International Publishing Switzerland.
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CITATION STYLE
Mayr, P., Scharnhorst, A., Larsen, B., Schaer, P., & Mutschke, P. (2014). Bibliometric-enhanced information retrieval. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8416 LNCS, pp. 798–801). Springer Verlag. https://doi.org/10.1007/978-3-319-06028-6_99
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