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The knowledge organization domain has recently been turning its attention increasingly to problems of interdisciplinarity.  Recently we have attempted to explore the approaches to interdisciplinarity represented by the synthetic and faceted Universal Decimal Classification (UDC) and that of the phenomenon-based Basic Concepts Classification (BCC). Both are large-scale general classifications capable of expressing complex concepts with precision and subtlety. The questions for research are: how do both classifications express the same sets of concepts, what are the specific advantages or disadvantages of disciplinary versus phenomenon-based classification in the gathering of concepts, and how can these classifications be used to generate interdisciplinary ontologies for the Semantic Web? The study reported here takes an empirical approach to the comparison of the UDC and the BCC assigned to a set of documents found in the OCLC WorldCat. We drew 400 cases at random from a WorldCat dump of UDC strings. We deciphered each UDC string, deconstructing each element of it and citing an example from the WorldCat of a resource to which the particular string had been assigned. We then provided BCC classification to match each UDC string. The present study demonstrates both the greater economy and greater conceptual precision in the phenomenon-based BCC. The network analysis suggests that there is great navigational strength in both approaches.