Automatic detection of semantic inconsistency between BPMN process model and SBVR rule model

Abstract

Business Process and Model Notation (BPMN) is a graphical model of business processes and Semantic of Business Vocabulary and Business Rules (SBVR) provide business rules corresponding to that business process. Though both these formalism should have no inconsistencies, but that is often present. Our research interest lies in converting SBVR rules to graphical representation and using sub graph-isomorphism to detect instances of inconsistencies between BPMN and SBVR model. We propose a framework and multi-step process to identify the instances of inconsistencies between the two models. We first generated XML of BPMN diagram and apply parsing and extracted tags relevant to us. We then used Stanford NLP Parser to generate parse tree of Rules and detailed information about the parse tree is stored in the form of Typed Dependency which represent grammatical relation between words of a sentence. We utilized this grammatical relation to extract triplet (actor-action-object) of a sentence. We fi nd node-induced sub graph of all possible length of nodes of a graph and apply VF2 Algorithm to detect instances of inconsistency between sub graphs. Finally we evaluate the proposed research framework by conducting experiments on a synthetic dataset to validate the accuracy and effectiveness of our approach.