Improving Discovery of Open Civic Data

We describe a method and system design for improved data discovery in an integrated network of open geospatial data that supports collaborative policy development between governments and local constituents. Metadata about civic data (such as thematic categories, user-generated tags, geo-references, or attribute schemata) primarily rely on technical vocabularies that reflect scientific or organizational hierarchies. By contrast, public consumers of data often search for information using colloquial terminology that does not align with official metadata vocabularies. For example, citizens searching for data about bicycle collisions in an area are unlikely to use the search terms with which organizations like Departments of Transportation describe relevant data. Users may also search with broad terms, such as “traffic safety”, and will then not discover data tagged with narrower official terms, such as “vehicular crash”. This mismatch raises the question of how to bridge the users’ ways of talking and searching with the language of technical metadata. In similar situations, it has been beneficial to augment official metadata with semantic annotations that expand the discoverability and relevance recommendations of data, supporting more inclusive access. Adopting this strategy, we develop a method for automated semantic annotation, which aggregates similar thematic and geographic information. A novelty of our approach is the development and application of a crosscutting base vocabulary that supports the description of geospatial themes. The resulting annotation method is integrated into a novel open access collaboration platform (Esri’s ArcGIS Hub) that supports public dissemination of civic data and is in use by thousands of government agencies. Our semantic annotation method improves data discovery for users across organizational repositories and has the potential to facilitate the coordination of community and organizational work, improving the transparency and efficacy of government policies. 2012 ACM Subject Classification Information systems → Digital libraries and archives