Biomedical terminologies often contain composite concepts that cannot be translated into single unique synonymous concepts in a target controlled terminology. Such composite concepts need to be decomposed into sets of component concepts present in the target terminology that can serve as the proxy for applications in information retrieval, decision support or data analysis. Towards this goal, we use a "clustering coefficient" over the UMLS Metathesaurus to traverse the closely clustered neighbors of the composite source concept to generate a ranked list of possible component concepts. Using the MeSH Associated Expression mappings as the gold-standard, we show that the proposed approach generates relevant component concepts as compared to existing semantic locality based methods. The topological connectivity of the concepts in the UMLS Metathesaurus is a useful feature that can be coupled with existing lexical and semantic locality based approaches towards terminology translation.