Abstract
Process mining is concerned with deriving knowledge from process data as recorded in so-called event logs. The quality of event logs is a constraining factor in achieving reliable insights. Particular quality problems are posed by activity labels which are meant to be representative of organisational activities, but may take different manifestations (e.g. as a result of manual entry synonyms may be introduced). Ideally, such problems are remedied by domain experts, but they are time-poor and data cleaning is a time-consuming and tedious task. Ontologies provide a means to formalise domain knowledge and their use can provide a scalable solution to fixing activity label similarity problems, as they can be extended and reused over time. Existing approaches to activity label quality improvement use manually-generated ontologies or ontologies that are too general (e.g. WordNet). Limited attention has been paid to facilitating the development of purposeful ontologies in the field of process mining. This paper is concerned with the creation of activity ontologies by domain experts. For the first time in the field of process mining, their participation is facilitated and motivated through the application of techniques from crowdsourcing and gamification. Evaluation of our approach to the construction of activity ontologies by 35 participants shows that they found the method engaging and that its application results in high-quality ontologies.
Journal
Year
2021
Cite as
Sadeghianasl S., ter Hofstede A. H. M., Wynn M. T., Turkay S. and Myers T., “Process Activity Ontology Learning From Event Logs Through Gamification,” in IEEE Access, vol. 9, pp. 165865-165880, 2021, doi: 10.1109/ACCESS.2021.3134915.