Abstract
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 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.
Highlights
Visualization of an ontology improves comprehension of knowledge it contains
The choice of a visualization system is non-trivial, as there is no method for evaluation and comparing of ontology visualization systems present at the literature, except for empirical experiments, that are subjective and costly
The outline of the paper is as follows: in Section 2 we propose several metrics for expert evaluation and in Section 3 we derive the information measure for ontology visualizations complexity
Summary
Visualization of an ontology improves comprehension of knowledge it contains. There are numerous ontology visualization systems, the reviews are presented in [1,2,3]. In [6] several new shapebased metrics are proposed for large graphs All these metrics are based on empirical experiments, i.e. on human assessments. Some metrics we propose require external knowledge of the language being visualized, its semantics, and knowledge of other visualization systems, they are half-formal and require expert evaluation. Another criterion is based on graph theory and information theory and is fully computable. We propose to consider several features of visualization systems that, though related to the formal properties, like completeness, still require expert evaluation, as they involve external knowledge
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