The Tetherless World Weblog

The Linked Open Government Data group has worked hard to make available US Government data as RDF, and over the past year they’ve introduced some really great tools for exploring government data. One of those tools, the White House Visitors app, is now a native app for the iOS product family. The native app provides live querying of the LOGD triple stores, queries DBpedia for more information about important people, uses the New York Times linked open data to find the latest articles, and pulls images using Freebase. For travel, the application caches data so that existing queries can be viewed when Internet connectivity is intermittent.

See the White House Visitors App on iTunes Preview or download it from the App Store.

Screenshots:

White House Visitors app finds the top 25 visitees (configurable) and lists them decreasing order.

Visualize the visitors and visitees in graphical form.

Using linked data, the White House Visitors app can find out more about people listed in the original dataset.

The widget I introduced in my last post is a specific application of SPARQL-based views over ISWC SemWeb Dog Food data and streaming Twitter data.  However, many other applications are possible, such as dynamic visualisations.  The following are a few examples of raw SPARQL queries executed against the conference triple store.  The first three are the queries currently provided with the widget demo, while the other two focus on data about people.  Tweak the queries to create your own live views of the conference data.

• tweets about ISWC 2010 — tweets matching any of the conference hashtags
• tweets about sub-events of ISWC 2010 — tweets matching any of the hashtags of any of the sub-events of the conference
• tweets by ISWC authors — tweets by inividuals who have published a paper in the main conference
• ISWC twitterers and where they are based — like it says
• ISWC authors and the topics they have tweeted about — pay no attention to the occasional weird hashtag. They’re the slime trail of an already-squashed bug.

The URL of the AllegroGraph SPARQL endpoint is: http://flux.franz.com/catalogs/demos/repositories/iswc2010.  It supports XML and JSON results through content negotiation.

I attended a seminar at MIT last Friday (for this year, I’m a part-time member of the DIG group there). Edward Benson gave an impressive demo on Datapress, an extension of WordPress that can enable non-geeks to import and visualize data in their blogs.

Since our TW blog is based on WordPress, I installed the extension and began to try. The installation was surprisingly smooth, just a few clicks and it’s done in 30 seconds!

The first thing I want to try is to visualize the ISWC 2010 dataset I recently built. Since Datapress does not yet support importing from RDF, I created a spreadsheet using a SPARQL query in TopBraid Composer:

SELECT distinct ?l ?lat ?long
WHERE {
?s swc:isSubEventOf iswc2010:research-track .
?s swc:isSuperEventOf ?p .
?p swc:hasRelatedDocument ?d .
?d foaf:maker ?m.
?m swrc:affiliation ?o.
?o rdfs:label ?l .
?o foaf:based_near ?b .
?b geo:lat ?lat.
?b geo:long ?long .
}

There are some minor format requirements (I didn’t get it right in the first try -Ted helped me to identify the problem)
* the first line of the spreadsheet should be headers, and the “key” line should have “{{label}}”
* To show on a map, coordinates should be shown as Lat,Lng. Hence, I need to combine the last two columns into one, separated with a comma.

The next step is to upload it to Google Docs, and share it as a public document (can be viewed here)

Then, I can go back to the blog post that I’m writing, click a button on Datapress toolbar in the editing interface, add the data by giving it the URL to the Google Docs spreadsheet, and select Map visualization. The process is very user friendly.

You can add multiple visualizations to one post. This is a very handy way to generate visualization using Exhibit. Actually, I have thought about visualizing ISWC data using Exhibit, but didn’t get time (or too lazy) to program. Datapress saved me.

Ted will give the presentation about Datapress at ISWC next week. Don’t miss it if you will also be at Shanghai!

(The map shows locations of research track authors at ISWC 2010.)

• datapress - Google Docs

Check out the tagCloud below, can you see why it is interesting? Please compare the two tag clouds generated from the same text (a text corpus from the title of about 2000 data.gov datasets), and see why they are different.

Novel Multi-word TagCloud

Conventional Single-word TagCloud

Highlights

• Meaningful Visualization. As you may see from the caption, the first one a “MultiWord TagCloud” while the other is the conventional single-word  TagCloud. The former joints individual words into popular multi-word phrases. With the former tag cloud, I can have a better overview on what data was published at data.gov.
• Automated Process. The MultiWord TagCloud was not created by human users, but automatically generated by computer program, powered by Microsoft Web N-gram service. We can generate such tag cloud for all existing text document
• Cloud+Crowd. Broadly, this demo shows the value of the crowd and the cloud I mentioned in my earlier blog, now big data can be tackled by the crowd (text from the entire Web) and the cloud (the high performance computational Web N-gram service).

Behind the Scene

The WWW2010 is really inspiring – making me a productive “engineer” although I came as a researcher. Today I picked up Microsoft Visual Studio and write my first C# program. I was an excellent C++ programmer back to my college time (I wrote ton of code using Visual C++ 4.0 10+ years ago). However, today is not about me being a programmer, but rather announce something that is really cool!  I would also like to thank researchers, Evelyne and Paul from Microsoft Research for their great support. My demo on data.gov data is powered by Microsoft Web N-gram Service.

Cheers,

Li Ding @ RPI,  April 29, 2010

I recently build a proof-of-concept demo on getting Facebook data (public data only) into LOD   their recently announced Graph API. The demo is available at http://sam.tw.rpi.edu/ws/face_lod.html.

It is fairly straightforward to convert the JSON object into RDF and make the URI dereferenceable. Now the data are linkable, but not yet linked to other LOD data.

I did see some issues when I was assigning rdf properties. Here is an example JSON from http://graph.facebook.com/cocacola

{
   "id": "40796308305",
   "name": "Coca-Cola",
   "picture": "http://profile.ak.fbcdn.net/object3/1853/100/s40796308305_2334.jpg",
   "link": "http://www.facebook.com/coca-cola",
   "category": "Consumer_products",
   "username": "coca-cola",
   "products": "Coca-Cola is the most popular and biggest-selling soft drink in history, as well as the best-known product in the world.\n\nCreated in Atlanta, Georgia, by Dr. John S. Pemberton, Coca-Cola was first offered as a fountain beverage by mixing Coca-Cola syrup with carbonated water. Coca-Cola was introduced in 1886, patented in 1887, registered as a trademark in 1893 and by 1895 it was being sold in every state and territory in the United States. In 1899, The Coca-Cola Company began franchised bottling operations in the United States.\n\nCoca-Cola might owe its origins to the United States, but its popularity has made it truly universal. Today, you can find Coca-Cola in virtually every part of the world.",
   "fan_count": 5425800
}

1. The JSON file from Facebook is not using the exact Open Graph Protocol terms -below is the mapping

   name  => og:title
   category => og:type
   picture  => og:image
   link  => og:url

2.2 we can reuse FOAF and DCTerms to cover some terms used in Facebook data – below is the mapping

  picture => foaf:depiction
  name => foaf:name
  from => dcterms:source
  id => dcterms:identifier
  created_time => dcterms:created
  updated_time => dcterms:modified
  category => dcterms:type
  link => foaf:homepage

Li Ding@RPI April 28, 2010