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.

SEEK ID: https://workflowhub.eu/publications/22

DOI: 10.1007/978-3-319-49157-8_29

Teams: yPublish - Bioinfo tools

Publication type: Journal

Journal: Metadata and Semantics Research

Book Title: Metadata and Semantics Research

Editors: Emmanouel Garoufallou and Imma Subirats Coll and Armando Stellato and Jane Greenberg

Publisher: Springer International Publishing

Citation: Metadata and Semantics Research 672:333-344,Springer International Publishing

Date Published: 2016

Registered Mode: by DOI

Authors: Yasmmin Cortes Martins, Fábio Faria da Mota, Maria Cláudia Cavalcanti

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Martins, Y. C., da Mota, F. F., & Cavalcanti, M. C. (2016). DSCrank: A Method for Selection and Ranking of Datasets. In Communications in Computer and Information Science (pp. 333–344). Springer International Publishing. https://doi.org/10.1007/978-3-319-49157-8_29

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Created: 23rd Oct 2023 at 14:59

Last updated: 23rd Oct 2023 at 15:04

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