Making Sense of Open Government Data

Data.gov, a major distributor of raw US government data, has published thousands of raw datasets on the Web for public access. While these datasets provide useful information, their potential has not yet been fully realized due to usability-related issues. In this work, we investigate potential ways to make sense of existing open government data using semantic web technologies. In our study, we demonstrate strategies for turning open government data into linked government data and present several case studies to illustrate the role of linked government data in making sense of government data.

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The LOGD project investigates the role of Semantic Web technologies, especially Linked Data, in producing, enhancing and utilizing government data published on Data.gov and other websites. Large portion of government data published on the Web are not necessarily ready for mashups. The Tetherless World Constellation (TWC) is now publishing over 8 billions RDF triples converted from hundreds of government-related datasets from Data.gov and other sources (e.g.

The Inference Web is a Semantic Web based knowledge provenance infrastructure that supports interoperable explanations of sources, assumptions, learned information, and answers as an enabler for trust.

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