DSCrank: A Method for Selection and Ranking of Datasets
Considerable efforts have been made to build the Web of Data. One of the main challenges has to do with how to identify the most related datasets to connect to. Another challenge is to publish a local dataset into the Web of Data, following the Linked Data principles. The present work is based on the idea that a set of activities should guide the user on the publication of a new dataset into the Web of Data. It presents the specification and implementation of two initial activities, which correspond to the crawling and ranking of a selected set of existing published datasets. The proposed implementation is based on the focused crawling approach, adapting it to address the Linked Data principles. Moreover, the dataset ranking is based on a quick glimpse into the content of the selected datasets. Additionally, the paper presents a case study in the Biomedical area to validate the implemented approach, and it shows promising results with respect to scalability and performance.
KeywordsResource Description Framework SPARQL Query Publishing Dataset External Link Relevance Analysis
This work was partially funded by CAPES scholarship, CNPq (proc. 307647/2012-9) and FAPERJ (Proc.E-26/111.147/2011).
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