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

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. Categories and Subject Descriptors H.4.m [Information Systems]: Miscellaneous Keywords Linked Government Data, Social Semantic Web, Data.gov

• Type: Conference
• Year: 2010
• Where: WWW
• Authors: Li Ding, Dominic DiFranzo, Alvaro Graves, James Michaelis, Xian Li, Deborah L. McGuinness, James A. Hendler
• Keywords: Government Data, Internet Technology, Semantic Web, US Government, WWW 2010