Scholarly communication graphs represent semantic relations between scientific products (papers, data, algorithms, etc), authors, organizations and research projects. In this context the aim is to find a way to measure the distance between papers and data (semantic correlation) to obtain a better data discovery. In fact, data metadata are poor, and the identification of a correlation distance between a paper (richer) and data allows to propagate the context (for example abstract) from the richer object to the other one
Analysis of DataCite for paper - dataset context propagation
Baglioni M.
2016
Abstract
Scholarly communication graphs represent semantic relations between scientific products (papers, data, algorithms, etc), authors, organizations and research projects. In this context the aim is to find a way to measure the distance between papers and data (semantic correlation) to obtain a better data discovery. In fact, data metadata are poor, and the identification of a correlation distance between a paper (richer) and data allows to propagate the context (for example abstract) from the richer object to the other oneFile in questo prodotto:
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Descrizione: Analysis of DataCite for Paper - Dataset context propagation
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