Ontology-Based Heuristics for Process Behavior: Formalizing False Positive Scenarios

  • Jorge Roa
  • Emiliano Reynares
  • María Laura Caliusco
  • Pablo Villarreal
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 281)


Verification methods to detect errors in the behavior of process models can be formal or informal. The former are based on formal languages, whereas the latter are based on heuristics. The main advantage of informal methods with respect to the formal ones is their short run-time. However, heuristics may lead to false positives, i.e. they may detect errors in a process model even though such model is correct. In this work, we propose using ontologies to formalize heuristics that avoid false positive scenarios. With ontologies it is possible to avoid ambiguities in heuristics that may lead to inaccurate implementations and to enable their execution by ontology reasoners. To this aim, we propose a set of false positive scenarios and define SWRL rules and SPARQL queries to formalize heuristics for such scenarios by means of ontologies. In addition, we identified three requirements that should be met in order to formalize heuristics and their false positive scenarios.


Business process model Anti-patterns Verification SPARQL 


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Jorge Roa
    • 1
  • Emiliano Reynares
    • 1
  • María Laura Caliusco
    • 1
  • Pablo Villarreal
    • 1
  1. 1.Universidad Tecnológica Nacional - Facultad Regional Santa Fe - CONICETSanta FeArgentina

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