Research and applications Figure 3 A portion of a common data element (CDE) example in the transformed resource description framework (RDF) format. access all integrated data resources through standard query services of a built-in SPARQL end point. We then deﬁned SPARQL queries that: (1) extracted the IDs and names of all the data elements; (2) extracted the IDs and names of all of the enumerated value domains; (3) extracted all the NCIt concept codes linked to the permissible values for the enumerated value domain; and (4) extracted the semantic type and group infor- mation for each of the NCIt concept codes isolated in step 3. Figure 4 shows a sample query that selects the permissible values and corresponding concept codes for a speciﬁc data element. Figure 5 shows a query example that extracts the semantic type and group information for a speciﬁc NCIt concept code. Figure 4 A SPARQL query example that extracts data element name, value domain name, valid value, value meaning, and meaning concepts (ie, terminological annotations by National Cancer Institute (NCI) concept codes) for a speciﬁc data element ID (ie, 2429490). e132 Figure 5 A SPARQL query example that extracts the semantic type and group information for a speciﬁc National Cancer Institute Thesaurus (NCIt) concept code (ie, C12392|liver). The code P106 denotes semantic type in the NCIt. Quality evaluation of CDE value set components The goal of the evaluation is to develop a mechanism using UMLS semantic types to audit the semantic consistency of the terminological annotations on enumerated CDE value set components. We started by isolating the set of CDE-permissible values that have corresponding meanings in external controlled terminologies. Next, we linked the corresponding value mean- ings against their NCI- or UMLS- generated SN mapping to determine whether all of the meanings fell within the same semantic group. We posited that if the meaning concepts fell into more than one group, this might be an indication of a curation problem and could be used as a trigger for an auditing process. To test this hypothesis, we randomly selected a subset of failing CDEs and manually examined their permissible values and the corresponding asserted terminological annotations. RESULTS We converted and loaded three data sources, comprising the caDSR CDEs, the NCIt concepts, and UMLS semantic groups, into the RDF store. For the caDSR source, there were 47 312 CDEs as of April 14, 2011. The XML2RDF transformation of the caDSR CDE source produced about 11.55 million RDF triples. For the NCIt source, 97 354 concepts were represented as approximately 2.27 million RDF triples. For the UMLS SN, 15 semantic groups were aggregated from 116 semantic types. The system produced 562 RDF triples for the data source. Of the 47 312 CDEs from the caDSR source, 16 888 (35.7%) were of the type ‘enumerated’, which were associated with 6534 distinct enumerated value domains comprising 153 435 distinct permis- sible values, of which 74 686 (48.7%) had associated value meanings. These 74 686 permissible values with asserted NCIt concept codes corresponded to 11 883 distinct NCI concept code groups. Of these 11 883 distinct groups, 8859 contained only a single NCIt concept code, whereas 3024 contained multiple NCIt concept codes. Using SPARQL, we mapped each asserted NCIt concept code with its semantic type(s) and group(s). Of the 16 888 CDEs, 11 798 (69.9%) had at least two asserted NCI concept codes mapped to their semantic types and groups, 1587 had only one asserted NCI concept code mapped, and 3503 had no asserted NCIt concept codes mapped. Then we focused on the 11 798 CDEs and screened them using the single semantic group criteria described above. We found that 8705 (73.8%) CDEs had a unique J Am Med Inform Assoc 2012;19:e129ee136. doi:10.1136/amiajnl-2011-000739