BioPortal, the open repository of biomedical ontologies, represents one of the most popular portals for both researchers and practitioners in the Linked Data environment. The BioPortal ontologies contain concepts, relationships, rules and functions to infer the knowledge from various data resources. Solutions of complex biomedical queries is based on the interplay between three types of ontologies: (i) clinical, modelled by SNOMED CT, (ii) pharmacological, modelled by RxNORM, and (iii) genetic, modelled by GO. To explore the degree of integration of BioPortal Ontologies with SNOMED CT, RxNORM and GO ontologies, we collected the BioPortal links and analyzed their connections by descriptive statistics, graphical analysis and agglomerative hierarchical clustering. Whilst nearly all the BioPortal ontologies share links with SNOMED CT, only a quarter out of total share links with RxNORM and only a third out of total share links with GO. A fraction of 3.5% of BioPortal ontologies share links with both RxNORM and GO. Cluster analysis revealed the pattern of ontologies relationships with respect to their links to the SNOMED CT, RxNORM and GO triptych. The NIH, cell biology, pharmacology and chemistry, medical diagnostic and procedure, as well as bibliographic ontologies are clustering together into different subgroups. Collectively, our data suggest, the need for development or enrichment of ontologies connecting all three SNOMED CT, RxNORM and GO. We proposed the usefulness of cluster analysis of linked data to facilitate the selection of closely related ontologies for reuse by the developers.