Abstract
Ontologies support automatic sharing, combination and analysis of life sciences data. They undergo regular curation and enrichment. We studied the impact of an ontology evolution on its structural complexity. As a case study we used the sixty monthly releases between January 2008 and December 2012 of the Gene Ontology and its three independent branches, i.e. biological processes (BP), cellular components (CC) and molecular functions (MF). For each case, we measured complexity by computing metrics related to the size, the nodes connectivity and the hierarchical structure.The number of classes and relations increased monotonously for each branch, with different growth rates. BP and CC had similar connectivity, superior to that of MF. Connectivity increased monotonously for BP, decreased for CC and remained stable for MF, with a marked increase for the three branches in November and December 2012. Hierarchy-related measures showed that CC and MF had similar proportions of leaves, average depths and average heights. BP had a lower proportion of leaves, and a higher average depth and average height. For BP and MF, the late 2012 increase of connectivity resulted in an increase of the average depth and average height and a decrease of the proportion of leaves, indicating that a major enrichment effort of the intermediate-level hierarchy occurred.The variation of the number of classes and relations in an ontology does not provide enough information about the evolution of its complexity. However, connectivity and hierarchy-related metrics revealed different patterns of values as well as of evolution for the three branches of the Gene Ontology. CC was similar to BP in terms of connectivity, and similar to MF in terms of hierarchy. Overall, BP complexity increased, CC was refined with the addition of leaves providing a finer level of annotations but decreasing slightly its complexity, and MF complexity remained stable.
Highlights
The problem of ontology quality variation Ontologies are instrumental for sharing, combining and analyzing life sciences data [1]
S1-geneOntology-complexityEvolution-monthly.ods contains the analysis of the sixty Gene Ontology monthly releases between January 2008 and December 2012
It should be noted that their approach focuses on the analysis of the features of the new classes, whereas we studied Biological process (BP), Cellular components (CC) and Molecular functions (MF) globally and focused on the consequences of the changes on the ontology itself
Summary
The problem of ontology quality variation Ontologies are instrumental for sharing, combining and analyzing life sciences data [1]. Ontologies evolve through regular modifications related to curation or to enrichment [2]. Existing metrics quantifying the changes rely on the variation of the number of classes, of the number of properties, or for the most sophisticated, of the number of restrictions [3]. The Ontology Evolution Explorer OnEX provides access to approximately 560 versions of 16 life science ontologies. It allows a systematic exploration of the changes by generating evolution trend charts and inspection of the added, deleted, fused and obsolete concepts [4]. The underlying assumption of these approaches is that for ontologies, the more classes and properties, the better
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