Ontologies serve as a bridge between humans and computers. They encode domain knowledge to connect federated data sources or infer tacit knowledge. While the technology used in ontologies is widely adopted and can be considered mature, much has still to be learned about their evolution and development, as understanding ontology evolution could help ensure better quality control and provide knowledge engineers with helpful modeling and selection guidelines. This paper examines the evolution of computational ontologies using ontology metrics. The authors hypothesize that ontologies follow a similar development pattern. This assumption is broken down into five hypotheses and tested against historical metric data from 69 dormant ontologies and 7,053 versions. The research proved that the development processes of ontologies are highly diverse. While a slight majority of ontologies confirm some of the hypotheses, accepting them as a general rule is not possible. Further, the data revealed that most ontologies have disruptive change events for most measured attributes. These disruptive events are examined regarding their occurrences, combinations, and sizes.