A significant proportion of biomedical resources carries information that cross references to anatomical structures across multiple scales. To improve the visualization of such resources in their anatomical context, we developed an automated methodology that produces anatomy schematics in a consistent manner,and provides for the overlay of anatomy-related resource information onto the same diagram. This methodology, called ApiNATOMY, draws upon the topology of ontology graphs to automatically lay out treemaps representing body parts as well as semantic metadata linking to such ontologies. More generally, ApiNATOMY treemaps provide an efficient and manageable way to visualize large biomedical ontologies in a meaningful and consistent manner. In the anatomy domain, such treemaps will allow epidemiologists, clinicians, and biomedical scientists to review, and interact with, anatomically aggregated heterogeneous data and model resources. Such an approach supports the visual identification of functional relations between anatomically colocalized resources that may not be immediately amenable to automation by ontology-based inferencing. We also describe the application of ApiNATOMY schematics to integrate, and add value to, human phenotype-related information—results are found at http://apinatomy.org. The long-term goal for the ApiNATOMY toolkit is to support clinical and scientific graphical user interfaces and dashboards for biomedical resource management and data analytics.