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Linking Open Government Data (TWC LOGD) Li Ding, Jim Hendler and Deborah L. McGuinness Tetherless World Constellation Rensselaer Polytechnic Institute.

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Presentation on theme: "Linking Open Government Data (TWC LOGD) Li Ding, Jim Hendler and Deborah L. McGuinness Tetherless World Constellation Rensselaer Polytechnic Institute."— Presentation transcript:

1 Linking Open Government Data (TWC LOGD) Li Ding, Jim Hendler and Deborah L. McGuinness Tetherless World Constellation Rensselaer Polytechnic Institute September 30, 2010

2 2 Opening government data world-wide January 1, 2009 “Openness will strengthen our democracy and promote efficiency and effectiveness in Government.” --- President Obama Putting Government Data online May 21, 2009 January 19, 2010 data.gov.uk online May 21, 2010 data.gov online data.gov relaunch with semantic web featured June30,2009 2009 2010 … Many countries US UK Australia New Zealand …

3 3 Semantic Web featured at data.gov http://www.data.gov/semantic/ data.gov adopted Semantic Web Technolgoies Web-based Mashups Downloadable RDF data - 6.4 billions of triples

4 4 Data-gov Wiki: Innovations at RPI The Data-gov Wiki explores and educates the use of semantic web technologies, esp. linked data, in producing, processing and utilizing government data from data.gov. The Data-gov Wiki is run by the Tetherless World Constellation at RPI, headed by Professors Jim Hendler and Deborah McGuinness and led by Li Ding. Other student team members include: Dominic DiFranzo, Sarah Magidson,James Michaelis, Alvaro Graves, Adam Bell, Jin Guang Zheng, Xian Li, Tim Lebo, Gregory Todd Williams, Peter Coons, Zhenning Shangguan, Devin Gaffney, William Cooper, Brian Zaik, and Johanna Flores. 40+ Demos 400+ Datasets Tutorials & Videos

5 5 A Typical Mashup: CASTNET (Clean Air Status and Trends Network) Exhibit Visualization API Data.gov CASTNET Ozone (CSV) epa.gov CASTNET Site (CSV) Convert raw dataset into linkable RDF Data MashupWeb Application Mashup Visualization Mashup query multiple RDF dataset via SPARQL end point surf to EPA applications 1 2 drill down for details 3 4 Created by Dominic DiFranzo, PhD student at RPI, http://www.data.gov/semantic/Castnet/html/exhibithttp://www.data.gov/semantic/Castnet/html/exhibit

6 6 US and UK Foreign AID FY2007: Integrating data from two countries AIDMajor aids from USMajor aids from UK BrazilUS >UKDevelopment AssistanceGov & civil society, Economic IndiaUK > USChild Survival and HealthHealth, Economic Created by James Michaelis, RPI, http://data-gov.tw.rpi.edu/demo/linked/aidviz-1554-10030.htmlhttp://data-gov.tw.rpi.edu/demo/linked/aidviz-1554-10030.html Data Sources: [Spatial Mashup] Data.gov (USAID) + Data.gov.uk (DFID)

7 7 Gov Data for Pop Science: Integrating different sources for discovery Created by Sarah Magidson, U. Chicago. http://data-gov.tw.rpi.edu/demo/stable/tobacco-smoker/demo-state-10026-smoke-rate-statevarsapi.htmlhttp://data-gov.tw.rpi.edu/demo/stable/tobacco-smoker/demo-state-10026-smoke-rate-statevarsapi.html [Spatial Mashup] Data.gov (Population) + NIH (Tobacco Tax, Smoking rate) Gov data provides knowledge for poplation science study

8 8 Social Mashup - US Wildland Fire: Linking to Wikipedia (socially contributed) Wildland fire (NIFC) Budget on wildfire “DOI” and “USDA” (OMB) Category:Wildfires In The United States Created by Li Ding, RPI, http://data-gov.tw.rpi.edu/demo/stable/demo-1187-40x-wildfire-budget.htmlhttp://data-gov.tw.rpi.edu/demo/stable/demo-1187-40x-wildfire-budget.html [Temporal Mashup] Data.gov (statistics+ budget) + Wikipedia (famous fires)

9 9 Social Mashup - White House Visitor Search: Linking social network data using semantic wiki “POTUS” dbpedia:Barack_Obama Created by Dominic DiFranzo, Evan Patton, RPI, http://data-gov.tw.rpi.edu/demo/stable/white-house-visitor/top100-visitees.phphttp://data-gov.tw.rpi.edu/demo/stable/white-house-visitor/top100-visitees.php [Person Mashup (via Data-gov Wiki)] Data.gov (statistics) + Wikipedia (personal profiles) The White House Semantic Wiki Wikipedia NYTimes

10 10 Using Web Tools Information networks can be integrated via the Semantic Wiki and visualized a number of different ways: social networks, human- language technology, workflows, …

11 11 Conclusion and Future Work Now –6.4 8.5 billions of triples –“data + visualization + mashup” –Low-cost solutions –Education New LOGD site is coming –More raw data, catalog, links, –More technologies, RDFa –More tools, services –More demos, tutorials, videos –More domain applications Future Research –Data integration, link, search –Social machine –Provenance, versions, trust –Usability and data quality –Scalability scalable http://logd.tw.rpi.edu


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