Ontology-based heuristics for process behavior: Formalizing false positive scenarios

Abstract

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.