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SPARQLeR: Extended Sparql for Semantic Association Discovery

  • Krys J. Kochut
  • Maciej Janik
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4519)

Abstract

Complex relationships, frequently referred to as semantic associa-tions, are the essence of the Semantic Web. Query and retrieval of semantic associations has been an important task in many analytical and scientific activities, such as detecting money laundering and querying for metabolic pathways in biochemistry. We believe that support for semantic path queries should be an integral component of RDF query languages. In this paper, we present SPARQLeR, a novel extension of the SPARQL query language which adds the support for semantic path queries. The proposed extension fits seamlessly within the overall syntax and semantics of SPARQL and allows easy and natural formulation of queries involving a wide variety of regular path patterns in RDF graphs. SPARQLeR’s path patterns can capture many low-level details of the queried associations. We also present an implementation of SPARQLeR and its initial performance results. Our implementation is built over BRAHMS, our own RDF storage system.

Keywords

Path Variable Semantic Association Path Query Triple Pattern Locate Path 
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 Berlin Heidelberg 2007

Authors and Affiliations

  • Krys J. Kochut
    • 1
  • Maciej Janik
    • 1
  1. 1.Department of Computer Science, University of Georgia, 415 Boyd Graduate Studies Research Center, Athens, GA 30602-7404 

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