The Gene Ontology (GO) is used extensively in the field of genomics. Like other large and complex ontologies, GO is difficult to maintain. In particular, quality assurance (QA) efforts for GO's content can be laborious and time consuming. Abstraction networks are summarization networks that reveal and highlight high-level structural and hierarchical aggregation patterns in an ontology. They have been shown to successfully support QA work in the context of OWL-format ontologies and SNOMED CT. A kind of abstraction network, called a partial-area taxonomy, is developed for GO hierarchies. The Biological process (BP) taxonomy is derived. Within this framework, several QA heuristics based on the identification of anomalous groups of terms are introduced. Such groups are expected to have higher error rates compared to the general population of terms. The results of a preliminary QA review, based on the BP taxonomy, are presented. It is observed that various inconsistencies in the modeling of GO are exposed with the use of the taxonomy-based QA heuristics. Some anomalies repeatedly revealed errors in the underlying GO.