Non-Empirical Metrics for Ontology Visualizations Evaluation and Comparing

Ildar Baimuratov, Than Nguyen
2020 Proceedings of the 30th International Conference on Computer Graphics and Machine Vision (GraphiCon 2020). Part 2  
There are numerous ontology visualization systems, however, the choice of a visualization system is non-trivial, as there is no method for evaluation and comparing them, except for empirical experiments, that are subjective and costly. In this research, we aim to develop non- empirical metrics for ontology visualizations evaluation and comparing. First, we propose several half-formal metrics that require expert evaluation. These metrics are completeness, semanticity, and conservativeness. We
more » ... ervativeness. We apply the proposed metrics to evaluate and compare VOWL and Logic Graphs visualization systems. And second, we develop a com- pletely computable measure for the complexity of ontology visualizations, based on graph theory and information theory. In particular, ontology visualizations are considered as hypergraphs and the information mea- sure is derived from the Hartley function. The usage of the proposed information measure is exemplified by the evaluation of visualizations of the sample of axioms from the DoCO ontology in Logic Graphs and Graphol. These results can be practically applied for choosing ontology visualization systems in general and regarding a particular ontology.
doi:10.51130/graphicon-2020-2-3-25 fatcat:amh2vwzsgjexjm5ijrjzqlt7xm