RDF Querying: Language Constructs and Evaluation Methods Compared

  • Tim Furche
  • Benedikt Linse
  • François Bry
  • Dimitris Plexousakis
  • Georg Gottlob
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4126)


This article is firstly an introduction into query languages for the Semantic Web, secondly an in-depth comparison of the languages introduced. Only RDF query languages are considered because, as of the writing of this paper, query languages for other Semantic Web data modeling formalisms, especially OWL, are still an open research issue, and only a very small number of, furthermore incomplete, proposals for querying Semantic Web data modeled after other formalisms than RDF exist. The limitation to a few RDF query languages is motivated both by the objective of an in-depth comparison of the languages addressed and by space limitations. During the three years before the writing of this article, more than three dozen proposals for RDF query languages have been published! Not only such a large number, but also the often immature nature of the proposals makes the focus on few, but representative languages a necessary condition for a non-trivial comparison.

For this article, the following RDF query languages have been, admittedly subjectively, selected: Firstly, the “relational” or “pattern-based” query languages SPARQL, RQL, TRIPLE, and Xcerpt; secondly the reactive rule query language Algae; thirdly and last the “navigational access” query language Versa. Although subjective, this choice is arguably a good coverage of the diverse language paradigms considered for querying RDF data. It is the authors’ hope and expectation, that this comparison will motivate and trigger further similar studies, thus completing the present article and overcoming its limitation.


Resource Description Framework Query Language SPARQL Query Language Construct Triple Pattern 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Tim Furche
    • 1
  • Benedikt Linse
    • 1
  • François Bry
    • 1
  • Dimitris Plexousakis
    • 2
    • 3
  • Georg Gottlob
    • 4
  1. 1.Institute for InformaticsUniversity of MunichMünchenGermany
  2. 2.Department of Computer ScienceUniversity of Crete, Vassilika VoutonHeraklion, CreteGreece
  3. 3.Information Systems Laboratory, Institute of Computer ScienceFORTH, Vassilika VoutonHeraklion, CreteGreece
  4. 4.Oxford University Computing LaboratoryOxfordEngland

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