Contributing evidence to data-driven ontology evaluation workflow ontologies perspective

Ontologies have established themselves as the single most important semantic web technology. They have attracted widespread interest from both academic and industrial domains. This has led to an increase in ontologies created. It has become apparent that more than one ontology may model the same domain yet they can be very different. The question then is, how do you determine which ontology best fits your purposes? This paper endeavours to answer this question by reviewing relevant literature and instantiating the data-driven ontology evaluation methodology in the context of workflow ontologies. This evaluation methodology is then evaluated through statistical means particularly the Kruskal-Wallis test and further post hoc testing using the Mann-Whiteny U test.