inkdroid

pymarc v2.0 was released yesterday afternoon. I’m mentioning it here to give a big tip of the hat to Gabriel Farrell (gsf on #code4lib) who spent a significant amount of time cleaning up the code to be PEP-8 compliant.

If you are a current user of pymarc your code will most likely break, since methods like: addField() will now look like add_field(). This is a small price to pay for pythonistas who typically prefer clean, consistent and more coherent code (how’s that for alliteration?). It had to be done and I’m very grateful to gsf for taking the time to do it.

Another big thing is that we’ve switched from using subversion to bzr for revision control. Initially it seemed like a lightweight way for gsf and I to collaborate without monkeying with svn authentication (again)…and to learn the zen of distributed revision control. We both liked it so much that Gabe is hosting the pymarc repository at http://fruct.us/bzr/pymarc.

So if you like the latest/greatest/shiniest, and/or want to contribute some of your own changes to pymarc:

  % bzr branch http://fruct.us/bzr/pymarc
  % # hack, hack, hack, hackety, hack
  % bzr commit
  % bzr send --mail-to gsf@fruct.us --message "Gabe, I added a jammies method to the record object!"
  % # or publish your own repo and point us at it :-)

The OAI-ORE meeting is coming up, and in general I’ve been really impressed with the alpha specs that have come out. It’s not clear that there’s an established vocabulary for talking about aggregated resources on the web, so the Data Model and Vocabulary documents were of particular interest to me.

One thing I didn’t quite understand, and which I think may have some significance for implementors, is some language in the Discovery document on the subject of URI conflation:

The Data Model document [ORE Model] explicitly prohibits a URI of a ReM (URI-R) ever returning anything other than a ReM. This allows multiple representations to be associated with URI-R, such as using content negotiation to return ReMs in different languages, character sets, or compression encodings. But it does not allow URI-R to return a human readable “splash page”, either by HTTP content negotiation or redirection. For example, clients MUST NOT merge with content negotiation the following URI pair that would correspond to a ReM and a “splash page” for an object:

If I’m understanding right this would prohibit using technologies like microformats, eRDF, RDFa and GRDDL in a “splash page” to represent the resource map. It seems odd to me that you can represent a resource map in Atom, but not in HTML.

To illustrate what this might look like I took a splash page off of arXiv (hope that was ok!) and marked it up with oai-ore RDFa.

Take a look. So all I did is modify the existing XHTML at arxiv.org, and I’ve been able to represent an ORE Resource Map. This seems like a relatively simple, and powerful way for existing repositories to make their aggregated resources available.

RDFa just entered Last Call, but there are already multiple implementations. Try out the GetN3 bookmarklet on the splash page, and you should see some triples come back. I ran them through the validator at w3c and got the following graph (kinda too big to include here inline).

This kind of issue seem to be at the heart of what Ian Davis refers to when he asks “Is the Semantic Web Destined to be a Shadow?“. Andy Powell and Pete Johnston have also been strong voices for integrating digital library repositories and the web–and they are also involved with the oai-ore effort. It feels like some of the oia-ore language could be loosened a bit to allow machine readable and human readable information to commingle a bit more.

Like you I’ve been reading about the new Reuters Calais Web Service. The basic gist is you can send the service text and get back machine readable data about recognized entities (personal names, state/province names, city names, etc). The response format is kind of interesting because it’s RDF that uses a bunch of homespun vocabularies.

At work Dan, Brian and I have been working on ways to map document centric XML formats to intellectual models represented as OWL. At our last meeting one of our colleagues passed out the Calais documentation, and suggested we might want to take a look at it in the context of this work. It’s a very different approach in that Calais is doing natural language processing and we instead are looking for patterns in the structure of XML. But the end result is the same–an RDF graph. We essentially have large amounts of XML metadata for newspapers, but we also have large amounts of OCR for the newspaper pages themselves. Perfect fodder for nlp and calais…

To aid in the process I wrote a helper utility (calais.py) that bundles up the Calais web service into a function call that returns a rdf graph, courtesy of Dan’s rdflib:

  import calais
  graph = calais_graph(content)

This is dependent on you getting a calais license key and stashing it away in ~/.calais. I wrote a couple sample scripts that use calais.py to do stuff like output all the personal names found in the text. For example here’s the people script. note, the angly brackets are missing from the sparql prefixes intentionally, since they don’t render properly (yet) in wordpress.

  from calais import calais_graph
  from sys import argv
 
  filename = argv[1]
  content = file(filename).read()
  g = calais_graph(content)
 
  sparql = """
          PREFIX rdf: http://www.w3.org/1999/02/22-rdf-syntax-ns#
          PREFIX ct: http://s.opencalais.com/1/type/em/e/
          PREFIX cp: http://s.opencalais.com/1/pred/
          SELECT ?name
          WHERE {
            ?subject rdf:type ct:People .
            ?subject cp:name ?name .
          }
          """
 
  for row in g.query(sparql):
      print row[0]

Notice the content is sent to calais, the graph comes back, and then a SPARQL query is executed on it? Here’s what we get when I run this OCR data through (take a look at the linked OCR to see just how irregular this data is).

  ed@curry:~/bzr/calais$ ./people data/ndnp\:774348
  McKmley
  Edwin W. Joy
  A. Musto
  JOHN D. SPRECKELS
  George Dlxoh
  Le Roy
  Bryan
  Charles P. Braslan
  Siegerfs Angostura Bitters
  James Stafford
  Herbert Putnam
  H. G. Pond
  Charles F. Joy
  Santa Rosa
  Allen S. Qlmsted
  Pptter Palmer

Clearly there are some errors, but you could imagine ranked list of these as they occurred across a million pages, where the anomalies would fall off on the long tail somewhere. It could be really useful in faceted browse applications. And here’s the output of cities.

  ed@curry:~/bzr/calais$ ./cities data/ndnp:774348
  Valencia
  San Jose
  Seattle
  Newport
  Santa Clara
  St. Louis
  New York
  Haifa
  Venice
  Rochester
  Fremont
  San Francisco
  San Francisco
  Chicago
  Oakland
  Los Angeles
  Fresno
  Watsonville
  Philadelphia
  Washington
  CHICAGO

Not too shabby. If you want to try this out, install rdflib, and you can grab calais.py and the sample scripts and OCR samples from my bzr repo:

  bzr branch http://inkdroid.org/bzr/calais

If you do dive into calais.py you’ll notice that currently the REST interface is returning the RDF escaped in an XML envelope of some kind. I think this is a bug, but calais.py extracts and unescapes the RDF.