Semantic-Based Method for Annotation and Ontological Modelling of Real-time Processes: Learning Process Domain Case Study

Tracking #: 1952-3165

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Kingsley Okoye
Syed Islam
Usman Naeem
Mhd Saeed Sharif

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Guest Editors Knowledge Graphs 2018

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Semantic technologies aim to represent specific information and models in formats that are not just machine-readable but also machine-understandable. This is done by providing the outputs of such systems in a structured manner, or better still, knowledge graph. To this end, this paper proposes an approach that illustrates how semantic concepts can be layered on top of extracted information assets or knowledge-bases to provide further understanding and enhancement of the resultant process models through the conceptualization method (i.e a system that integrates the semantic methods or technologies). This paper illustrates this notion using the case study of a learning process domain. Practically, the proposed method involves the augmentation of informative value of the resulting learning process models (or graphs) by semantically annotating the process elements with concepts they represent in real-time, and then linking them to an ontology in order to allow for a more abstract analysis of the extracted events log and models. The experimentations and sets of semantically motivated algorithms prove that the method is useful towards the extraction, semantical preparation, and transformation of events data log about any domain process into a formal or minable executable format – in order to support the process models discovery and analysis, structural representations and enhancement of the real-time processes. Evidently, the experimental results conclude that a system that is formally encoded with semantic labelling (annotation), semantic representation (ontology) and semantic reasoning (reasoner) has the capacity to lift the real-time process mining and analysis methods from the syntactic level to a much more conceptual level.
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