abstract
- © 2020, ICIC International.Semantic technologies aim to represent information or models in formats that are not just machine-readable but also machine-understandable. To this effect, this paper shows how the semantic concepts can be layered on top of the derived models to provide a more contextual analysis of the models through the conceptualization method. Technically, the method involves augmentation of informative value of the resulting models by semantically annotating the process elements with concepts that they represent in real-time settings, and then linking them to an ontology in order to allow for a more abstract analysis of the extracted logs or models. The work illustrates the method using the case study of a learning process domain. Consequently, the results show that a system which is formally encoded with semantic labelling (annotation), semantic representation (ontology) and semantic reasoning (reasoner) has the capacity to lift the process mining and analysis from the syntactic to a more conceptual level.