Documenting Context-Based Quality Assessment of Controlled Vocabularies

Access to e-Government data is challenging due to the heterogeneity and complexity of the public information ecosystem. Controlled Vocabularies (CVs) provide a key to disclosing the potential of Open Government data, by supplying common terms for marking up metadata and data in a consistent and coherent way. However, quality information is needed to help public institutions decide whether to adopt an existing or newly created CV. The paper discusses how to evaluate and document CV quality thus facilitating a comparison of different controlled vocabularies based on contextual information. The Analytical Hierarchy Process (AHP) is adopted to assess the overall quality and rank of a controlled vocabulary, by integrating various quality dimensions according to the decision maker's needs. A set of e-Government controlled vocabularies that facilitate the semantic interoperability of e-Government data are selected as a testbed, and updated quality values are made available as Linked Data. Multi-step guidelines are also defined promoting and complementing the adoption of W3C recommendations to provide machine-readable quality metadata. This fosters reliability and re-usability by providing consumers with information on the assessment process carried out and the outcomes achieved. We illustrate the application of these guidelines by focusing on provenance and quality documentation.

Automated summary

The paper discusses evaluating and documenting the quality of controlled vocabularies to promote comparison and enhance semantic interoperability. Using the Analytical Hierarchy Process (AHP), overall quality and ranks of controlled vocabularies can be assessed by integrating different quality dimensions based on decision maker's needs. Selecting a set of e-Government controlled vocabularies as a testbed and providing updated quality values as Linked Data can facilitate interoperability while multi-step guidelines for W3C recommendations can ensure machine-readable quality metadata, promoting reliability and re-usability.