Semantic Process Retrieval with iSPARQL

  • Christoph Kiefer
  • Abraham Bernstein
  • Hong Joo Lee
  • Mark Klein
  • Markus Stocker
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4519)


The vision of semantic business processes is to enable the integration and inter-operability of business processes across organizational boundaries. Since different organizations model their processes differently, the discovery and retrieval of similar semantic business processes is necessary in order to foster inter-organizational collaborations. This paper presents our approach of using iSPARQL – our imprecise query engine based on iSPARQL – to query the OWL MIT Process Handbook – a large collection of over 5000 semantic business processes. We particularly show how easy it is to use iSPARQL to perform the presented process retrieval task. Furthermore, since choosing the best performing similarity strategy is a non-trivial, data-, and context-dependent task, we evaluate the performance of three simple and two human-engineered similarity strategies. In addition, we conduct machine learning experiments to learn similarity measures showing that complementary information contained in the different notions of similarity strategies provide a very high retrieval accuracy. Our preliminary results indicate that iSPARQL is indeed useful for extending the reach of queries and that it, therefore, is an enabler for inter- and intra-organizational collaborations.


Similarity Measure Business Process Similarity Strategy SPARQL Query 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 Berlin Heidelberg 2007

Authors and Affiliations

  • Christoph Kiefer
    • 1
  • Abraham Bernstein
    • 1
  • Hong Joo Lee
    • 2
  • Mark Klein
    • 2
  • Markus Stocker
    • 1
  1. 1.Department of Informatics, University of ZurichSwitzerland
  2. 2.Center for Collective Intelligence, Massachusetts Institute of TechnologyUSA

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