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Growing Triples on Trees: an XML-RDF Hybrid Model for Annotated Documents

François Goasdoué 1 Konstantinos Karanasos 2 Yannis Katsis 3 Julien Leblay 1, 4, * Ioana Manolescu 4 Stamatis Zampetakis 1, 4
* Corresponding author
4 OAK - Database optimizations and architectures for complex large data
Inria Saclay - Ile de France, LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, CNRS - Centre National de la Recherche Scientifique : UMR8623
Abstract : Since the beginning of the Semantic Web initiative, significant efforts have been invested in finding efficient ways to publish, store and query metadata on the Web. RDF and SPARQL have become the standard data model and query language, respectively, to describe resources on the Web. Large amounts of RDF data are now available either as stand-alone datasets or as metadata over semi-structured (typically XML) documents. The ability to apply RDF annotations over XML data emphasizes the need to represent and query data and metadata simultaneously. We propose XR, a novel hybrid data model capturing the structural aspects of XML data and the semantics of RDF, also enabling us to reason about XML data. Our model is general enough to describe pure XML or RDF datasets, as well as RDF-annotated XML data, where any XML node can act as a resource. This data model comes with the XRQ query language that combines features of both XQuery and SPARQL. To demonstrate the feasibility of this hybrid XML-RDF data management setting, and to validate its interest, we have developed an XR platform on top of well-known data management systems for XML and RDF. In particular, the platform features several XRQ query processing algorithms, whose performance is experimentally compared.
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Submitted on : Friday, May 31, 2013 - 5:14:14 PM
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  • HAL Id : hal-00828906, version 1



François Goasdoué, Konstantinos Karanasos, Yannis Katsis, Julien Leblay, Ioana Manolescu, et al.. Growing Triples on Trees: an XML-RDF Hybrid Model for Annotated Documents. The VLDB Journal, Springer, 2013, Special Issue on Structured, Social and Crowd-sourced Data on the Web, 22 (5), pp.589-613. ⟨hal-00828906⟩



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