A pan-ontology view of machine-derived knowledge representations and feedback mechanisms for curation

Abstract

Biomedical ontologies are established tools that organize knowledge in specialized research areas. They can also be used to train machine-learning models. However, it is unclear to what extent representations of ontology concepts learned by machine-learning models capture the relationships intended by ontology curators. It is also unclear whether the representations can provide insights to improve the curation process. Here, we investigate ontologies from across the spectrum of biological research and assess the concordance of formal ontology hierarchies with representations based on plain-text definitions. By comparing the internal properties of each ontology, we describe general patterns across the pan-ontology landscape and pinpoint areas with discrepancies in individual domains. We suggest specific mechanisms through which machine-learning approaches can lead to clarifications of ontology definitions. Synchronizing patterns in machine-derived representations with those intended by the ontology curators will likely streamline the use of ontologies in downstream applications.