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
M Baglioni
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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