There is a growing awareness and interest in the issues of accountability and transparency in the pursuit of digital privacy. In previous work, we asserted that systems needed to be “policy aware” and able to compute the likely compliance of any digital transaction with the associated privacy policies (law, rule, or contract). This paper focuses on one critical step in respecting privacy in a digital environment, that of understanding the context associated with each digital transaction. For any individual transaction, the pivotal fact may be context information about the data, the party seeking to use it, the specific action to be taken, or the associated rules. We believe that the granularity of semantic web representation is well suited to this challenge and we support this position in the paper.

Data.gov is a website that provides US Government data to the general public to ensure better accountability and transparency. Our recent work on the Data-gov Wiki, which attempts to integrate the datasets published at Data.gov into the Linking Open Data (LOD) cloud (yielding "linked government data"), has produced 5 billion triples – covering a range of topics including: government spending, environmental records, and statistics on the cost and usage of public services. In this paper, we investigate the role of Semantic Web technologies in converting, enhancing and using linked government data. In particular, we show how government data can be (i) inter-linked by sharing the same terms and URIs, (ii) linked to existing data sources ranging from the LOD cloud (e.g. DBpedia) to the conventional web (e.g. the New York Times), and (iii) cross-linked by their knowledge provenance (which captures, among other things, derivation and revision histories).