RDF/SPARQL Design Pattern for Contextual Metadata
Olivier Corby, Catherine Faron Zucker
Abstract
Olivier Corby, Catherine Faron Zucker
Abstract
The basic principle of the Semantic Web carried by the RDF data model is that a collection of RDF statements coexist all together and are universally true. However some case study imply contextual relevancy and truth. The SPARQL query language is provided with patterns enabling to choose the RDF dataset against which a query is executed. This is a first step to handle contextual metadata. Based on it, we present in this paper a design pattern to handle contextual metadata hierarchically organized. This is done by means of a subStateOf property reifying RDF entailment between contextual graphs. The subStateOf relation is modelled within RDF and therefore context hierarchies can be described and queried by means of SPARQL queries. We propose a slight syntactic extension to SPARQL to facilitate the query of context hierarchies, together with rewriting rules to return to standard SPARQL.
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The basic principle of the Semantic Web carried by the RDF data model is that a collection of RDF statements coexist all together and are universally true. However some case study imply contextual relevancy and truth. The SPARQL query language is provided with patterns enabling to choose the RDF dataset against which a query is executed. This is a first step to handle contextual metadata. Based on it, we present in this paper a design pattern to handle contextual metadata hierarchically organized. This is done by means of a subStateOf property reifying RDF entailment between contextual graphs. The subStateOf relation is modelled within RDF and therefore context hierarchies can be described and queried by means of SPARQL queries. We propose a slight syntactic extension to SPARQL to facilitate the query of context hierarchies, together with rewriting rules to return to standard SPARQL.
Key concepts: SPARQL, RDF, Computer science, RDF Schema, Named graph, RDF query language, Linked data, Information retrieval