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SPedia: A Central Hub for the Linked Open Data of Scientific Publications

SPedia: A Central Hub for the Linked Open Data of Scientific Publications

Muhammad Ahtisham Aslam (Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia) and Naif Radi Aljohani (Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia)
Copyright: © 2017 |Volume: 13 |Issue: 1 |Pages: 20
ISSN: 1552-6283|EISSN: 1552-6291|EISBN13: 9781522511571|DOI: 10.4018/IJSWIS.2017010108
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MLA

Aslam, Muhammad Ahtisham, and Naif Radi Aljohani. "SPedia: A Central Hub for the Linked Open Data of Scientific Publications." IJSWIS vol.13, no.1 2017: pp.128-147. https://doi.org/10.4018/IJSWIS.2017010108

APA

Aslam, M. A. & Aljohani, N. R. (2017). SPedia: A Central Hub for the Linked Open Data of Scientific Publications. International Journal on Semantic Web and Information Systems (IJSWIS), 13(1), 128-147. https://doi.org/10.4018/IJSWIS.2017010108

Chicago

Aslam, Muhammad Ahtisham, and Naif Radi Aljohani. "SPedia: A Central Hub for the Linked Open Data of Scientific Publications," International Journal on Semantic Web and Information Systems (IJSWIS) 13, no.1: 128-147. https://doi.org/10.4018/IJSWIS.2017010108

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Abstract

Producing the Linked Open Data (LOD) is getting potential to publish high-quality interlinked data. Publishing such data facilitates intelligent searching from the Web of data. In the context of scientific publications, data about millions of scientific documents published by hundreds and thousands of publishers is in silence as it is not published as open data and ultimately is not linked to other datasets. In this paper the authors present SPedia: a semantically enriched knowledge base of data about scientific documents. SPedia knowledge base provides information on more than nine million scientific documents, consisting of more than three hundred million RDF triples. These extracted datasets, allow users to put sophisticated queries by employing semantic Web techniques instead of relying on keyword-based searches. This paper also shows the quality of extracted data by performing sample queries through SPedia SPARQL Endpoint and analyzing results. Finally, the authors describe that how SPedia can serve as central hub for the cloud of LOD of scientific publications.

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