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dc.contributor.authorYumusak, S.
dc.contributor.authorAras, R.E.
dc.contributor.authorUysal, E.
dc.contributor.authorDogdu, E.
dc.contributor.authorKodaz, H.
dc.contributor.authorOztoprak, K.
dc.date.accessioned2020-08-07T12:54:54Z
dc.date.available2020-08-07T12:54:54Z
dc.date.issued2017
dc.identifier10.1109/BigData.2017.8258169
dc.identifier.issn9781538627143 (ISBN)
dc.identifier.urihttp://hdl.handle.net/20.500.12498/2948
dc.description.abstractWe present the project SpEnD, a complete SPARQL endpoint discovery and analysis portal. In a previous study, the SPARQL endpoint discovery and analysis steps of the SpEnD system were explained in detail. In the SpEnD portal, the SPARQL endpoints are extracted from the web by using web crawling techniques, monitored and analyzed by live querying the endpoints systematically. After many sustainability improvements in the SpEnD project, the SpEnD system is now online as a portal. SpEnD portal currently serves 1487 SPARQL endpoints, out of which 911 endpoints are uniquely found by SpEnD only when compared to the other existing SPARQL endpoint repositories. In this portal, the analytic results and the content information are shared for every SPARQL endpoint. The endpoints stored in the repository are monitored and updated continuously. © 2017 IEEE.
dc.language.isoEnglish
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.source5th IEEE International Conference on Big Data, Big Data 2017
dc.titleSpEnD portal: Linked data discovery using SPARQL endpoints
dc.typeConference Paper


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