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The Semantic Web provides a framework for the integration of resources on the web, which facilitates information integration and interoperability. RDF is the main representation format for Linked Open Data (LOD). However, datasets are not always made available in RDF by their producers and the Semantic Web community has had to convert some of these datasets to RDF in order for these datasets to participate in the LOD cloud. As a result, the LOD cloud sometimes contains outdated, partial and even inaccurate RDF datasets. We review the LOD landscape for one of these resources, MeSH, and analyze the characteristics of six existing representations in order to identify desirable features for an authoritative version, for which we create a prototype. We illustrate the suitability of this prototype on three common use cases. NLM intends to release an authoritative representation of MeSH in RDF (beta version) in the Fall of 2014.

Objective: To assess the quality of value sets in clinical quality measures, both individually and as a population of value sets., Materials and Methods: The concepts from a given value set are expected to be rooted by one or few ancestor concepts and the value set is expected to contain all the descendants of its root concepts and only these descendants. (1) We assessed the completeness and correctness of individual value sets by comparison to the extension derived from their roots. (2) We assessed the non-redundancy of value sets for the entire population of value sets (within a given code system) using the Jaccard similarity measure., Results: We demonstrated the utility of our approach on some cases of inconsistent value sets and produced a list of 58 potentially duplicate value sets from the current set of clinical quality measures for the 2014 Meaningful Use criteria., Conclusion: These metrics are easy to compute and provide compact indicators of the completeness, correctness, and non-redundancy of value sets.

Objective: To develop methods for assessing the validity, consistency and currency of value sets for clinical quality measures, in order to support the developers of quality measures in which such value sets are used., Methods: We assessed the well-formedness of the codes (in a given code system), the existence and currency of the codes in the corresponding code system, using the UMLS and RxNorm terminology services. We also investigated the overlap among value sets using the Jaccard similarity measure., Results: We extracted 163,788 codes (76,062 unique codes) from 1463 unique value sets in the 113 quality measures published by the National Quality Forum (NQF) in December 2011. Overall, 5% of the codes are invalid (4% of the unique codes). We also found 67 duplicate value sets and 10 pairs of value sets exhibiting a high degree of similarity (Jaccard > .9)., Conclusion: Invalid codes affect a large proportion of the value sets (19%). 79% of the quality Measures have at least one value set exhibiting errors. However, 50% of the quality measures exhibit errors in less than 10 % of their value sets. The existence of duplicate and highly-similar value sets suggests the need for an authoritative repository of value sets and related tooling in order to support the development of quality measures.