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Please use this identifier to cite or link to this item: http://hdl.handle.net/UCSP/15861
Title: Toward RDF Normalization
Authors: Ticona Herrera, Regina
Tekli, Joe
Chbeir, Richard
Laborie, Sébastien
Dongo, Irvin
Guzman, Renato
Keywords: Data mining;Experimental test;Linked open datum;Loading time;New york time;RDF graph;RDF triples;Serialization;Target application;Redundancy
Issue Date: 2015
Publisher: Springer Verlag
metadata.dc.relation.uri: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84951829415&doi=10.1007%2f978-3-319-25264-3_19&partnerID=40&md5=fb6741a815b6499d83ebd44ce68ecf07
Abstract: Billions of RDF triples are currently available on the Web through the Linked Open Data cloud (e.g., DBpedia, LinkedGeoData and New York Times). Governments, universities as well as companies (e.g., BBC, CNN) are also producing huge collections of RDF triples and exchanging them through different serialization formats (e.g., RDF/XML, Turtle, N-Triple, etc.). However, RDF descriptions (i.e., graphs and serializations) are verbose in syntax, often contain redundancies, and could be generated differently even when describing the same resources, which would have a negative impact on their processing. Hence, we propose here an approach to clean and eliminate redundancies from such RDF descriptions as a means of transforming different descriptions of the same information into one representation, which can then be tuned, depending on the target application (information retrieval, compression, etc.). Experimental tests show significant improvements, namely in reducing RDF description loading time and file size. © Springer International Publishing Switzerland 2015.
URI: http://repositorio.ucsp.edu.pe/handle/UCSP/15861
ISBN: 9783319252636
ISSN: 3029743
Appears in Collections:Artículos de investigación

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