The Fundamentals of iSPARQL: A Virtual Triple Approach for Similarity-Based Semantic Web Tasks

  • Christoph Kiefer
  • Abraham Bernstein
  • Markus Stocker
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4825)


This research explores three SPARQL-based techniques to solve Semantic Web tasks that often require similarity measures, such as semantic data integration, ontology mapping, and Semantic Web service matchmaking. Our aim is to see how far it is possible to integrate customized similarity functions (CSF) into SPARQL to achieve good results for these tasks. Our first approach exploits virtual triples calling property functions to establish virtual relations among resources under comparison; the second approach uses extension functions to filter out resources that do not meet the requested similarity criteria; finally, our third technique applies new solution modifiers to post-process a SPARQL solution sequence. The semantics of the three approaches are formally elaborated and discussed. We close the paper with a demonstration of the usefulness of our iSPARQL framework in the context of a data integration and an ontology mapping experiment.


Similarity Measure Similarity Score Solution Mapping Aggregation Scheme Graph 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 2007

Authors and Affiliations

  • Christoph Kiefer
    • 1
  • Abraham Bernstein
    • 1
  • Markus Stocker
    • 1
  1. 1.Department of Informatics, University of ZurichSwitzerland

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