A Roadmap towards Tuneable Random Ontology Generation Via Probabilistic Generative Models

As the sophistication of the tools available for manipulating ontologies increases, so does the need for novel and rich ontologies to use for purposes such as benchmarking, testing and validation. Ontology repositories are not ideally suited for this need, as the ontologies they contain are limited in number, may not generally have required properties (e.g., inconsistency), and may present unwelcome correlations between features. In order to better match this need, we hold that a highly tuneable, language-agnostic, theoretically principled tool for the automated generation of random ontologies is needed. In this position paper we describe how a probabilistic generative model (based on features obtained via the analysis of real ontologies) should be developed for use as the theoretical back-end for such an enterprise, and discuss the role of the DOL metalanguage in it.