
doi: 10.1007/11891451_20
Ontology evolution becomes extremely important with the tremendous application of ontology. Ontology's size and complexity change a lot during its evolution. Thus it's important for ontology developers to analyze and try to control ontology's complexity to ensure the ontology is useable. In this paper, an evaluation method for analyzing ontology complexity is suggested. First, we sort all the concepts of an ontology according to their importance degree (a definition we will give below), then by using a well-defined metrics suite which mainly examines the concepts and their hierarchy and the quantity, ratio of concepts and relationships, we analyze the evolution and distribution of ontology complexity. In the study, we analyzed different versions of GO ontology by using our evaluation method and found it works well. The results indicate that the majority of GO's complexity is distributed on the minority of GO's concepts, which we call “important concepts” and the time when GO's complexity changed greatly is also the time when its “important concepts” changed greatly.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 7 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
