EPCs Annotated with Lexical and Semantic Labels to Bridge the Gap between Human Understandability and Machine Interpretability

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Abstract

Labels of EPC functions and events are the key to understanding EPC models by humans and by machines. Empirical studies show that the current labeling practice of model elements is conducted rather arbitrarily which inherently causes potential threats for understanding by humans. Thus, refactoring of model element labels is suggested either human-driven or with automated support while semantic annotation using domain-ontologies is well-recognized to approach the understanding of model elements by machines. Current research either focuses on improving the quality of labels or on semantic annotation to facilitate machine interpretability. To the best of our knowledge, there is a significant lack of approaches that facilitate to exploit the potentials and benefits arising from bridging the gap between approaches that improve human understandability and that facilitate machine interpretability. This work introduces a comprehensive, formalized approach that enables the modeling tasks automated refactoring of model elements and automated semantic annotation by bridging the gap between informal and formal representation of model elements.