Hi Maged,
Thanks for sharing the paper. It's very interesting work incorporating
analysis and visualization into wiki. I also found the following demo video:
http://graphics.stanford.edu/projects/vispedia/vispedia-trailer-tr.mov
Just for fun, I searched for "Alzheimer" in Wikipedia and it brought me
to the Alzeihmer's disease page. In the Epidemiology section, there is a
table listing AD incidence rates after 65 years of age. I used vispedia
to visualize this table and create the following scatter plot:
http://vispedia.stanford.edu/vis/353/Scatterplot#/?cp0=0&f0=Age&cp1=0&f1=Incidence%20(new%20affected)%20%20per%20thousand%20%20person%C3%A2%C2%80%C2%93years&cp2=0&f2=&cp3=0&f3=&cp4=0&f4=
Cheers,
-Kei
Maged N.K. Boulos wrote:
Given the recent interests of some members of this list in Wiki
applications like WikiNeuron and novel information visualization
techniques, this paper might prove useful and inspiring:
Chan B, Wu L, Talbot J, Cammarano M, Hanrahan P.
<http://www.ncbi.nlm.nih.gov/pubmed/18988966?ordinalpos=1&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_DefaultReportPanel.Pubmed_RVDocSum>
*Vispedia: Interactive Visual Exploration of Wikipedia Data via
Search-Based Integration*.
/IEEE Trans Vis Comput Graph/. 2008 November-December;14(6):1213-1220.
Stanford University.
Wikipedia is an example of the collaborative, semi-structured data
sets emerging on the Web. These data sets have large, non-uniform
schema that require costly data integration into structured tables
before visualization can begin. We present Vispedia, a Web-based
visualization system that reduces the cost of this data
integration.
Users can browse Wikipedia, select an interesting
data table, then use a search interface to discover, integrate, and
visualize additional columns of data drawn from multiple Wikipedia
articles. This interaction is supported by a fast path search
algorithm over DBpedia, a semantic graph extracted from Wikipedia's
hyperlink structure. Vispedia can also export the augmented data
tables produced for use in traditional visualization systems. We
believe that these techniques begin to address the "long tail" of
visualization by allowing a wider audience to visualize a broader
class of data. We evaluated this system in a first-use formative lab
study. Study participants were able to quickly create effective
visualizations for a diverse set of domains, performing data
integration as needed.
PMID: 18988966 [PubMed - as supplied by publisher]
