INTELLIGENT TECHNOLOGY FOR SEMANTIC COMPLETENESS ASSESSMENT OF BUSINESS PROCESS MODELS

Abstract

In this paper, we present a method for comparing business process models with their textual descriptions, using a semantic-based approach based on the SBERT (Sentence-Bidirectional Encoder Representations from Transformers) model. Business process models, especially those created with the BPMN (Business Process Model and Notation) standard, are crucial for optimizing organizational activities. Ensuring the alignment between these models and their textual descriptions is essential for improving business process accuracy and clarity. Traditional set similarity methods, which rely on tokenization and basic word matching, fail to capture deeper semantic relationships, leading to lower accuracy in comparison. Our approach addresses this issue by leveraging the SBERT model to evaluate the semantic similarity between the text description and the BPMN business process model. The experimental results demonstrate that the SBERT-based method outperforms traditional methods, based on similarity measures, by an average of 31%, offering more reliable and contextually relevant comparisons. The ability of SBERT to capture semantic similarity, including identifying synonyms and contextually relevant terms, provides a significant advantage over simple token-based approaches, which often overlook nuanced language variations. The experimental results demonstrate that the SBERT-based approach, proposed in this study, improves the alignment between textual descriptions and corresponding business process models. This advancement is allowing to improve the overall quality and accuracy of business process documentation, leading to fewer errors, introducing better clarity in business process descriptions, and better communication between all the stakeholders. The overall results obtained in this study contribute to enhancing the quality and consistency of BPMN business process models and related documentation.