Exploiting label semantics for rule-based activity recommendation in business process modeling

Business process modeling is a crucial task in organizations. Yet, the creation of consistent and complete process models is challenging and necessitates the support of process modelers with their task. In previous work, we presented a rule-based activity-recommendation approach, which recommends appropriate labels for a new activity inserted by a modeler in a process model under development. While our method has shown to work well, it is limited by the fact that it only learns rules that describe the inter-relations between complete activity labels. In the case that the model's activities and the ones in the training repository are disjoint, the existing approach will thus not be able to provide any recommendations. In this paper, we overcome this restriction by additionally considering the natural language-based semantics of the process models. In particular, we propose a semantics-aware recommendation approach that extends the existing approach in both central phases, i.e., in the rule-learning phase and in the rule-application phase. We equip the rule learning with novel rule types, which capture action and business-object patterns in process models. For the rule application, we developed an optional similarity extension that allows rules to make recommendations even if the bodies of the rules are not exactly true for the given model. Through an evaluation on a large set of real-world process models, we demonstrate that the semantic extensions can improve the quality of recommendations. (c) 2022 Elsevier Ltd. All rights reserved.

Automated summary

Business process modeling is crucial in organizations, but creating consistent and complete process models is challenging. This paper proposes a rule-based and semantic-aware recommendation approach to improve the quality of activity label recommendations.