Existing biomedical ontologies encode scientific knowledge that is exploited in ontology-based annotated entities, e.g., genes described using Gene Ontology (GO) annotations. Ontology-based annotations correspond to building blocks for computing relatedness between annotated entities, as well as for data mining techniques that attempt to discover domain patterns or suggest novel associations among annotated entities. However, effectiveness of these annotation-based approaches can be considerably impacted by the quality of the annotations, and models that allow for the description of the quality of the annotations are required to validate and explain the behavior of these approaches. We propose AnnEvol, a framework to describe datasets of ontology-based annotated entities in terms of evolutionary properties of the annotations of these entities over time. AnnEvol complements state-of-the-art approaches that perform an annotation-wise description of the datasets, and conducts an annotation set-wise description which characterizes the evolution of annotations into semantically similar annotations. We empirically evaluate the expressiveness power of AnnEvol in a set of proteins annotated with GO using UniProt-GOA and Swiss-Prot. Our experimental results suggest that AnnEvol captures evolutionary behavior of the studied GO annotations, and clearly differentiates patterns of annotations depending on both the annotation provider and on the model organism of the studied proteins.
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