TWC data-gov corpus: incrementally generating linked government data from data.gov

Authors
Ding, Li
DiFranzo, Dominic
Graves, Alvaro
Michaelis, James
Li, Xian
McGuinness, Deborah L.
Hendler, James A.
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Issue Date
2010-04-30
Keywords
Linking Open Government Data
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Abstract
The Open Government Directive is making US government data available via websites such as Data.gov for public access. In this paper, we present a Semantic Web based approach that incrementally generates Linked Government Data (LGD) for the US government. In focusing on the tradeoff between high quality LGD generation (requiring non-trivial human expert input) and massive LGD generation (requiring low human processing cost), our work is highlighted by the following features: (i) supporting low-cost and extensible LGD publishing for massive government data; (ii) using Social Semantic Web (Web3.0) technologies to incrementally enhance published LGD via crowdsourcing, and (iii) facilitating mashups by declaratively reusing cross-dataset mappings which usually are hardcoded in applications.
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https://tw.rpi.edu/project/LOGD
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