It is becoming impossible to contemplate successful bio-medical research without canonical data structures. The biomedical computation community finds itself grappling with hundreds of different knowledge bases, metadata formats, and database schemas. These include primary databases, such as those in GenBank and MEDLINE; metadata that describe the primary data, such as those in caBIO; and knowledge bases that codify biomedical concepts, such as the Gene Ontology and SNOMED-CT. These data structures are representable in languages such as DICOM and MAGE-ML. Many of these data elements and knowledge bases have emerged out of necessity from work that scientists, unfamiliar with data and knowledge representation standards, have done in isolation. Many of these resources fail to follow consistent modeling conventions, so computer programs cannot consistently interpret them. Semantic Web technology and languages such as RDF and OWL can rectify the problem somewhat by providing a common metadata and ontology language and Web-based tools for dealing with ontologies and knowledge structures. However, even if translation mechanisms exist between various biomedical resources and Semantic Web languages (which, by itself, is unlikely to happen for all resources), this translation is only part of the solution.