<p>Understanding the molecular biology and development of disease plays a key role in drug development. Integrating evidence from different experimental approaches with data available from public resources (such as gene expression level changes and reaction pathways affected by pathogenic mutations) can be a powerful approach for evaluating different aspects of target-disease associations. The application of ontologies is of fundamental importance to effective integration. The Target Validation Platform is a user-friendly interface that integrates such evidences from various resources with the aim of assisting scientists to identify and prioritise drug targets. Currently, the EFO is used as the reference ontology for diseases in the platform, importing terms from existing disease ontologies such as the Human Phenotype Ontology as required. In order to generalize the use of EFO from key target-diseases for wider use, we need to compare the target associated disease coverage in EFO with the scope of other available disease terminology resources. In this study, we address this issue by using text mining and present our initial results.</p>