Author
Ding, Li; DiFranzo, Dominic; Graves, Alvaro; Michaelis, James; Li, Xian; McGuinness, Deborah; Hendler, Jim
Other Contributors
Date Issued
2010-04-30
Subject
Linking Open Government Data
Degree
Terms of Use
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.;
Department
Relationships
https://tw.rpi.edu/project/LOGD;
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