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Paper 109_elpub2016:
Identifying and Improving Dataset References in Social Sciences Full Texts

id 109_elpub2016
authors Ghavimi, Behnam; Philipp Mayr, Sahar Vahdati and Christoph Lange
year 2016
title Identifying and Improving Dataset References in Social Sciences Full Texts
source ELPUB2016. Positioning and Power in Academic Publishing: Players, Agents and Agendas, 20th International Conference on Electronic Publishing, 7–9 June 2016 in Göttingen, Germany
summary Scientific full text papers are usually stored in separate places than their underlying research datasets. Authors typically make references to datasets by mentioning them for example by using their titles and the year of publication. However, in most cases explicit links that would provide readers with direct access to referenced datasets are missing. Manually detecting references to datasets in papers is time consuming and requires an expert in the domain of the paper. In order to make explicit all links to datasets in papers that have been published already, we suggest and evaluate a semi-automatic approach for finding references to datasets in social sciences papers. Our approach does not need a corpus of papers (no cold start problem) and it performs well on a small test corpus (gold standard). Our approach achieved an F-measure of 0.84 for identifying references in full texts and an F-measure of 0.83 for finding correct matches of detected references in the dara dataset registry.
keywords Information extraction, Link discovery, Data linking, Research data,Social sciences, Scientific papers
series ELPUB:2016
type normal paper
content file.pdf (183,986 bytes)
discussion No discussions. Post discussion ...
urn:nbn urn:nbn:se:elpub-109_elpub2016
last changed 2016/07/03 10:57
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