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

Authors:
Li Ding;Dominic DiFranzo;Alvaro Graves;James R. Michaelis;Xian Li;Deborah L. McGuinness;James A. Hendler
Affiliations:
Rensselaer Polytechnic Institute, Troy, NY, USA;Rensselaer Polytechnic Institute, Troy, NY, USA;Rensselaer Polytechnic Institute, Troy, NY, USA;Rensselaer Polytechnic Institute, Troy, NY, USA;Rensselaer Polytechnic Institute, Troy, NY, USA;Rensselaer Polytechnic Institute, Troy, NY, USA;Rensselaer Polytechnic Institute, Troy, NY, USA
Venue:
Proceedings of the 19th international conference on World wide web
Year:
2010

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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 trade-off 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 mash-ups by declaratively reusing cross-dataset mappings which usually are hard-coded in applications.