What Do Users Think About Abstractions of Ontology-Driven Conceptual Models?

Proc. of the 17th Int. Conf. on Research Challenges in Information Science (RCIS 2023). Volume 476 of Lecture Notes in Business Information Processing. 2023.

In a previous paper, we proposed an algorithm for ontology-driven conceptual model abstractions. We have implemented and tested this algorithm over a FAIR Catalog of such models represented in the OntoUML language. This provided evidence for the correctness of the algorithm's implementation, i.e., that it correctly implements the model transformation rules prescribed by the algorithm, and its effectiveness, i.e., it is able to achieve high compression (summarization) rates over these models. However, in addition to these properties, it is fundamental to test the validity of this algorithm, i.e., that it achieves what it is intended to do, namely provide summarizing abstractions over these models whilst preserving the gist of the conceptualization being represented. We performed three user studies to evaluate the usefuness of the resulting abstractions as perceived by modelers. This paper reports on the findings of these user studies and reflects on how they can be exploited to improve the existing algorithm.

@inproceedings{RCIS-2023,
   title = "What Do Users Think About Abstractions of Ontology-Driven
Conceptual Models?",
   year = "2023",
  author = "Elena Romanenko and Diego Calvanese and Giancarlo Guizzardi",
   booktitle = "Proc. of the 17th Int. Conf. on Research Challenges in
Information Science (RCIS 2023)",
   pages = "53--68",
   volume = "476",
   publisher = "Springer",
   series = "Lecture Notes in Business Information Processing",
   doi = "10.1007/978-3-031-33080-3_4",
}