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dc.contributor.authorDing, Li
dc.contributor.authorDiFranzo, Dominic
dc.contributor.authorGraves, Alvaro
dc.contributor.authorMichaelis, James
dc.contributor.authorLi, Xian
dc.contributor.authorMcGuinness, Deborah
dc.contributor.authorHendler, Jim
dc.date.accessioned2022-02-18T02:38:34Z
dc.date.available2022-02-18T02:38:34Z
dc.date.issued2010-03-22
dc.identifier.other234
dc.identifier.urihttp://archive.tw.rpi.edu/media/latest/ding2010datagov.pdf
dc.identifier.urihttps://hdl.handle.net/20.500.13015/4636
dc.description.abstractData.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).
dc.relation.urihttps://tw.rpi.edu/project/InferenceWeb
dc.subjectInference Web
dc.titleData-govWiki: Towards Linking Government Data


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