‘Republic of Letters’ in R / Custom Widgets for Second Screen TV navigation trails

As ever, I write one post that perhaps should’ve been two. This is about the use and linking of datasets that aid ‘second screen’ (smartphone, tablet) TV remotes, and it takes as a quick example a navigation widget and underlying dataset that show us how we might expect to navigate TV archives, in some future age when TV lives more fully in the World Wide Web. I argue that access to the ‘raw data‘ and frameworks for embedding visualisation apps are of equal importance when thinking about innovative ways of exploring the ever-growing archives. All of this comes from many discussions with my NoTube colleagues and other collaborators; rambling scribblyness is all my own.

Ben Hammersley points us at a lovely Flash visualization http://www.stanford.edu/group/toolingup/rplviz/”>Mapping the Republic of Letters”.

From the YouTube overview, “Researchers map thousands of letters exchanged in the 18th century’s “Republic of Letters” and learn at a glance what it once took a lifetime of study to comprehend.”


Mapping the Republic of Letters has at its center a multidimensional data set which spans 300 years and nearly 100,000 letters. We use computing tools that help us to measure and analyze data quantitatively, though that will not take us to our goal. While we use software and computing techniques that were designed for scientific and statistical methods, we are seeking to develop computing tools to enhance humanistic methods, to help us to explore qualitative aspects of the Republic of Letters. The subject of our study and the nature of the material require it. The collections of correspondence and records of travel from this period are incomplete. Of that incomplete material only a fraction has been digitized and is available to us. Making connections and resolving ambiguities in the data is something that can only be done with the help of computing, but cannot be done by computing alone. (from ‘methods and philosophy‘)


screenshot of Republic of Letters app, showing social network links superimposed on map of historical western Europe


See their detailed writeup for more on this fascinating and quite beautiful work. As I’m working lately on linking TV content more deeply into the Web, and on ‘second screen’ navigation, this struck me as just the kind of interface which it ought to be possible to re-use on a tablet PC to explore TV archives. Forgetting for the moment difficulties with Flash on iPads and so on, the idea roughly is that it would be great to embed such a visualization within a TV watching environment, such that when the ‘republic of letters’ widget is focussed on some person, place, or topic, we should have the opportunity to scan the available TV archives for related materials to show.

So a glance at Chrome’s ‘developer tools’ panel gave me a link to the underlying data used by the visualisation. I don’t know exactly whose it is, nor how they want it used, so please treat it with respect. Still, there it is, sat in the Web, in tab-separated format, begging to be used. There’s a lot you can do with the Flash application that I’ve barely touched, but I’m intrigued by the underlying dataset. In particular, where they have the string “Tonson, Jacob”, the data linker in me wants to see a Wikipedia or DBpedia link, since they provide explanation, context, related people, places and themes; all precious assets when trying to scrape together related TV materials to inform, educate or entertain someone with. From a few test searches, it turns out that (many? most?) the correspondents are quite easily matched to Wikipedia: William Congreve, Montagu, 1st earl of Halifax, CharlesHough, bishop of Worcester, John; Stanyan, Abraham;  … Voltaire and others. But what about the data?

Lately I’ve been learning just a little about R, a language used mainly for statistics and related analysis. Here’s what it’ll do ‘out of the box’, in untrained hands:

letters<-read.csv('data.txt',sep='\t', header=TRUE)
v_author = letters$Author=="Voltaire"
v_letters = letters[v_author, ]
Where were Voltaire’s letters sent?
> cbind(summary(v_letters$dest_country))
[,1]
Austria            2
Belgium            6
Canada             0
Denmark            0
England           26
France          1312
Germany           97
India              0
Ireland            0
Italy             68
Netherlands       22
Portugal           0
Russia             5
Scotland           0
Spain              1
Sweden             0
Switzerland      342
The Netherlands    1
Turkey             0
United States      0
Wales              0
As the overview and video in the ‘Republic of Letters‘ site points out (“Tracking 18th-century “social network” through letters”), the patterns of correspondence eg. between Voltaire and e.g. England, Scotland and Ireland jumps out of the data (and more so its visualisation). There are countless ways this information could be explored, presented, sliced-and-diced. Only a custom app can really make the most of it, and the Republic of Letters work goes a long way in that direction. They also note that
The requirements of our project are very much in sync with current work being done in the linked-data/ semantic web community and in the data visualization community, which is why collaboration with computer science has been critical to our project from the start.
So the raw data in the Web here is a simple table; while we could spend time arguing about whether it would better be expressed in JSON, XML or an RDF notation, I’d rather see some discussion around what we can do with this information. In particular, I’m intrigued by the possibilities of R alongside the data-linking habits that come with RDF. If anyone manages to tease anything interesting from this dataset, perhaps mixed in with DBpedia, do post your results.
And of course there are always other datasets to examine; for example see the Darwin correspondence archives, or the Open Knowledge Foundation’s Open Correspondence project which has a Dickens-based pilot. While it is wonderful having UI that is tuned to the particulars of some dataset, it is also great when we can re-use UI code to explore similarly structured data from elsewhere. On both the data side and the UI side, this is expensive, tough work to do well. My current concern is to maximise re-use of both UI and data for the particular circumstances of second-screen TV navigation, a scenario rarely a first priority for anyone!
My hope is that custom navigation widgets for this sort of data will be natural components of next-generation TV remote controls, and that TV archives (and other collections) will open up enough of their metadata to draw in (possibly paying) viewers. To achieve this, we need the raw data on both sides to be as connectable as possible, so that application authors can spend their time thinking about what their users really need and can use, rather than on whether they’ve got the ‘right’ Henry Newton.
If we get it right, there’s a central role for librarianship and archivists in curating the public, linked datasets that tell us about the people, places and topics that will allow us to make new navigation trails through Web-connected television, literature and encyclopedia content. And we’ll also see new roles for custom visualizations, once we figure out an embedding framework for TV widgets that lets them communicate with a display system, with other users in the same room or community, and that is designed for cross-referencing datasets that talk about the same entities, topics, places etc.
As I mentioned regarding Lonclass and UDC, collaboration around open shared data often takes place in a furtive atmosphere of guilt and uncertainty. Is it OK to point to the underlying data behind a fantastic visualisation? How can we make sure the hard work that goes into that data curation is acknowledged and rewarded, even while its results flow more freely around the Web, and end up in places (your TV remote!) that may never have been anticipated?

Skosdex: SKOS utilities via jruby

I just announced this on the public-esw-thes and public-rdf-ruby lists. I started to make a Ruby API for SKOS.

Example code snippet from the readme.txt (see that link for the corresponding output):

require "src/jena_skos"
s1 = SKOS.new("http://norman.walsh.name/knows/taxonomy")
s1.read("http://www.wasab.dk/morten/blog/archives/author/mortenf/skos.rdf" )
s1.read("file:samples/archives.rdf")
s1.concepts.each_pair do |url,c|
  puts "SKOS: #{url} label: #{c.prefLabel}"
end

c1 = s1.concepts["http://www.ukat.org.uk/thesaurus/concept/1366"] # Agronomy
puts "test concept is "+ c1 + " " + c1.prefLabel
c1.narrower do |uri|
  c2 = s1.concepts[uri]
  puts "\tnarrower: "+ c2 + " " + c2.prefLabel
  c2.narrower do |uri|
    c3 = s1.concepts[uri]
    puts "\t\tnarrower: "+ c3 + " " + c3.prefLabel
  end
end

The idea here is to have a lightweight OO API for SKOS, couched in terms of a network of linked “Concepts”, with broader and narrower relations. But this is backed by a full RDF API (in our case Jena, via Java jruby magic). Eventually, entire apps could be built at the SKOS API level. For now, anything beyond broader/narrower and prefLabel is hidden away in the RDF (and so you’d need to dip into the Jena API to get to this data).

The distinguishing feature is that it uses jruby (a Ruby implementation in pure Java). As such it can call on the full powers of the Jena toolkit, which go far beyond anything available currently in Ruby. At the moment it doesn’t do much, I just parse SKOS and make a tiny object model which exposes little more than prefLabel and broader/narrower.

I think it’s worth exploring because Ruby is rather nice for scripting, but lacks things like OWL reasoners and the general maturity of Java RDF/OWL tools (parsers, databases, etc.).

If you’re interested just to see how Jena APIs look when called from jruby Ruby, see jena_skos.rb in svn. Excuse the mess.

I’m interested to hear if anyone else has explored this topic. Obviously there is a lot more to SKOS than broader/narrower, so I’m very interested to find collaborators or at least a sanity check before taking this beyond a rough demo.

Plans – well my main concern is nothing to do with java or ruby, … but to explore Lucene indexing of SKOS data. I am also very interested in the pragmatic question of where SKOS stops and RDFS/OWL starts, … and how exactly we bridge that gap. See flickr for my most recent sketch of this landscape, where I revisit the idea of an “it” property (skos:it, foaf:it, …) that links things described in SKOS to “the thing itself”. I hope to load up enough overlapping SKOS data to get some practical experience with the tradeoffs.

For query expansion, smarter tagging assistants, etc. So the next step is probably to try building a Lucene index similar to the contrib/wordnet utility that ships with Java lucene. This creates a Lucene index in which every “document” is really a word from Wordnet, with text labels for its synonyms as indexed properties. I also hope to look at the use of SKOS + Lucene for “did you mean?” and auto-completion utilities. It’s also worth noting that Jena ships with LARQ, a Lucene-aware extension to ARQ, Jena’s SPARQL engine.

Bouncin’ around

A couple weeks ago, in a pathetic and greedsome bid to monetize you all, I added Google Adsense ads to this blog. Needless to say, the jaded foaftards who read this thing aren’t the kind to go all clicky-buyey on adverts. When I finally earn a whole dollar, I’ll celebrate by having it converted to hard currency and spend it on, er I dunno, charity. Meanwhile, the ad targetting engine is entertaining if nothing else. I’ll leave the thing running on archive pages, but it should be gone from the main page now.

Thanks to Bob DuCharme for pointing out that I’m apparently now endorsing rebounders.com, your one stop solution for post-breakup adult dating social networking. Thanks, Google. Maybe they caught me looking at the Internet Dating Conference website (they’re still looking for speakers btw)? Well I think not – I’ve decided that the ad targetting reflects more on their knowledge of danbri.org’s readership than on its author. There’s some logic to that, so thanks everyone…

Seriously, I am really suprised at how weak the Adsense targetting is. Google too often are treated as infallible gods, but the ad targetting I’ve seen so far feels like it’s done with grep. But I don’t have anything better right now, so I should shut up with the complaining. Here’s that ad again for those that missed it.

google ad

Sample SPARQL query

PREFIX dc: <http://purl.org/dc/elements/1.1/>
SELECT ?book ?title ?authorname
WHERE { 
?book dc:creator ?author .
?author dc:type <http ://hoppa.com/Painters/> .
      ?author dc:title ?authorname .
      ?book dc:title ?title .
}

…works with rdf data describing some books by painters. I tested in Dave Beckett’s Redland-based online SPARQL demo. The query finds 5 results. Seems to have some encoding errors, but apart from that, is fine. There are more DawgShows in the ESW wiki. The sparql.org demo (using Jena) also works.