Today, full-texts of scientific articles are often stored in different locations than the used datasets. Dataset registries aim at a closer integration by making datasets citable but authors typically refer to datasets using inconsistent abbreviations and heterogeneous metadata (e.g. title, publication year). It is thus hard to reproduce research results, to access datasets for further analysis, and to determine the impact of a dataset. Manually detecting references to datasets in scientific articles is time-consuming and requires expert knowledge in the underlying research domain.We propose and evaluate a semi-automatic three-step approach for finding explicit references to datasets in social sciences articles.We first extract pre-defined special features from dataset titles in the da|ra registry, then detect references to datasets using the extracted features, and finally match the references found with corresponding dataset titles. The approach does not require a corpus of articles (avoiding the cold start problem) and performs well on a test corpus. We achieved an F-measure of 0.84 for detecting references in full-texts and an F-measure of 0.83 for finding correct matches of detected references in the da|ra dataset registry.
Schaefer2014 proposed the Normalized Relevance Distance (NRD) @cite_12 . This metric measures the semantic relatedness of terms. NRD is based on the co-occurrence of terms in documents, and extends NGD by using relevance weights of terms. The quality of these methods depends on the size of the corpus used.
@cite_12
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We propose the Normalized Relevance Distance (NRD): a robust metric for computing semantic relatedness between terms. NRD makes use of a controlled reference corpus for a statistical analysis. The analysis is based on the relevance scores and joint occurrence of terms in documents. On the basis of established reference datasets, we demonstrate that NRD does not require sophisticated data tuning and is less dependent on the choice of the reference corpus than comparable approaches.