oai-ore post baltimore thoughts

The recent OAI-ORE meeting was just up the road in Baltimore, so it was easy for a bunch of us from the Library of Congress to attend. I work on a team at LC that is specifically looking at the role that repositories play at the library; I’ve implemented OAI-PMH data providers and harvesters, and in the past couple of years I’ve gotten increasingly interested in semantic web technologies – so OAI-ORE is of particular interest to me. I’ve commented a bit about OAI-ORE on here before, but I figure it can’t hurt to follow in my coworker’s footsteps and summarize my thoughts after the meeting.

(BTW, above is an image of some constellations I nabbed off of wikipedia. I included it here because the repeated analogy (during the meeting) of OAI-ORE resource maps as constellations was really compelling – and quite poetic.)

The Vocabulary

It seems to me that the real innovation of the OAI-ORE effort is that it provides a lightweight RDF vocabulary for talking about aggregated resources on the web. Unfortunately I think that this kernel gets a little bit lost in the 6 specification documents that were released en masse a few months ago.

The ORE vocabulary essentially consists of three new resource types: ore:ResourceMap, ore:Aggregation, ore:AggregatedResource ; and 5 new properties to use with those types: ore:describes, ore:isDescribedBy, ore:aggregates, ore:isAggregatedBy, ore:analogousTo. In addition, the Vocabulary document
provides guidance on how to use a few terms from the DublinCore vocabulary: dc:creator, dc:rights, dcterms:modified, dcterms:created.

The vocabulary is small, so if I were them I would publish the vocabulary elements using hash URIs, instead of slash URIs. The reason for this is that you don’t have to jigger the web server to do a httpRange-14 style 303 correctly:

  • http://www.openarchives.org/ore/0.2/terms#Aggregation
  • http://www.openarchives.org/ore/0.2/terms#AggregatedResource
  • http://www.openarchives.org/ore/0.2/terms#ResourceMap
  • http://www.openarchives.org/ore/0.2/terms#describes
  • http://www.openarchives.org/ore/0.2/terms#isDescribedBy
  • http://www.openarchives.org/ore/0.2/terms#aggregates
  • http://www.openarchives.org/ore/0.2/terms#isAggregatedBy
  • http://www.openarchives.org/ore/0.2/terms#analogousTo

Also, I think ore:AggregatedResource is currently missing from the rdf/xml vocabulary, so it should be added. Also ore:isDescribedBy seems to be commented out.

There is a lot of redundancy between the Abstract Data Model and the Vocabulary documents–so I would recommend collapsing them down into a single, succinct document. This is in keeping with the DRY principle and will have the added benefit of making it easier for newbies to hit the ground running (not having to wade through multiple docs and mentally reconcile them). I could understand having a separate Abstract Data Model document if it were totally divorced from the web and semantic web technologies like RDF, but it’s not.

The Graph

The OAI-ORE effort seemed to be mostly driven by a desire to take harvesting agents the last mile to the actual repository resources themselves–enabling digital library objects (in addition to their metadata) to be harvested from repositories (using HTTP) ; and to be referenced from other contexts (say objects in other repositories). This desire was born out of real, hard won experience with harvesting metadata records, and marked a shift from metadata-centric harvesting to resource-centric harvesting.

In addition OAI-ORE marks a departure from predictable and mind-numbing arguments about SIP formats (METS, DIDL, FOXML, IEEE LOM, XFDU, etc). Yet as soon as we have our shiny new OAI-ORE vocabulary we have to learn yet-another-packaging-format, this time one built on top of Atom.

First, let me just say I’m a big fan of RFC 4287, in particular how it is used in the RESTful Atom Publishing Protocol (RFC 5023). I also think it makes sense to have an Atom serialization for OAI-ORE resource maps – assuming there is a GRDDL transform for turning it into RDF. But the workshop in Baltimore seemed to stress that the Atom serialization was the only way to do OAI-ORE, and didn’t emphasize that there are in fact lots of ways of representing RDF graphs on the web. For example GRDDL allows you to associate arbitrary XML with an XSLT transform to extract a RDF graph. And you could encode your RDF graph directly with RDFa, N3, Turtle, ntriples, or RDF/XML.

Perhaps there is a feeling that stressing the RDF graph too much will alienate some people who are more familiar with XML technologies. Or perhaps all these graph serialization choices could be perceived as being too overwhelming. But I think the opposite extreme of making it look like you can only use an overloaded Atom document as a means to publishing ORE resource maps is misguided, and will ultimately slow adoption. Why not encourage people to publish GRDDL transforms for METS, DIDL or mark up their “splash pages” with RDFa? This would bring the true value of the OAI-ORE work home–it’s not about yet-another-packaging format, it’s about what the various packaging formats have in common on the web.

Release Early, Release Often

In hindsight I think it would’ve been helpful for the OAI-ORE group to privately build consensus about the core OAI-ORE vocabulary (if necessary), then release that into the world wild web for discussion. Then once the kinks were worked out, and there was general understanding, moving on to issues such as discovery and serialization. As it stands the various documents were all dumped at the same time, and seem somewhat fragmented, and in places redundant. Clearly a lot of conversations have gone on that aren’t happening on the public discussion list.

I expressed interest in being part of the OAI-ORE and was politely turned down. I’m actually kind of glad really because I also don’t want to be part of some cabal of digital library practitioners. Maybe I should’ve titled this post “Sour Grapes” :-) Seriously though, the digital library needs good practical solutions and communities of users that encourage widespread adoption and tool support. We don’t need research-ware. Having secret discussions and occasional public events that feel more like lectures than meetings isn’t a good way to encourage adoption.

Anyhow, I hope that this isn’t all seen as being too harsh. Everyone’s a critic eh? All in all there is a lot in OAI-ORE to be proud of. The effort to integrate Web Architecture into Digital Library practices is most welcome indeed. Keep up the good work y’all.

pymarc PEP-8 cleanup

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 we moved the repository to LaunchPad.

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

  % bzr branch lp: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 :-)

oai-ore and the shadow web

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.

calais and ocr newspaper data

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

sicp reading

If you’ve ever harbored any interest in reading (or re-reading) The Structure and Interpretation of Computer Programs please consider joining some of the books4code folks as we work through the SICP MIT OpenCourseWare (free) course. Chris McAvoy has set up a wiki-page with details, and a calendar to subscribe to, to keep us honest. The book is available for free, and so are video lectures, notes, exercise answers, etc … Thanks Jason for getting us to take this up again :-)

lcsh, thesauri and skos

Simon Spero has an interesting post on why LCSH cannot be considered a thesaurus. At $work I’ve been working on mapping LCSH/MARC to SKOS, so Simon’s efforts in both collecting and analyzing LCSH authority data have been extremely valuable. In particular Simon and Leonard Willpower’s involvement with SKOS alerted me relatively early on to some of the problems that lie in store when thinking of LCSH in terms of a thesaurus.

The problem stems from very specific (standardized) notions of what thesauri are. Z39-19-2005 defines broader relationships in thesauri as being transitive. So if a has the broader term b, and b has the broader term c, then you can infer a has the broader term c.

Now consider the broader relationships (BT for those of you w/ the red books handy, or care to browse authorities.loc.gov from the comfort of your chair) from the heading “Non-alcoholic cocktails”:

If broader relationships are to be considered transitive one is obliged to treat Alcoholic beverages as a broader term for Non-alcoholic cocktails. But clearly it’s nonsense to consider a non-alcoholic cocktail a specialization of an alcoholic beverage. As Simon pointed out the problem was recognized by Mary Dykstra soon after LCSH adopted terminology from the thesauri world (BT, NT, RT) in 1986. Her article, LC Subject Headings Disguised as a Thesaurus describes the many difficulties of treating LCSH as a thesaurus. In the example above from LCSH the broader (BT) relationship is used for both hierarchical (IS-A) relationships, as well as part/whole (HAS-A) relationships. According to thesauri folks this is a no-no.

LCSH aside, the semantics of broader/narrower have been an issue for SKOS for a fair amount of time. Guus Schreiber proposed a resolution, which was just accepted at yesterday’s SWD telecon. SKOS is trying to straddle several different worlds, enabling the representation of a range of knowledge organization systems from thesauri and taxonomies to subject heading lists, folksonomy and other controlled vocabularies. To remain flexible in this way, while still appealing to the thesaurus world a compromise was reached where the skos:broader and skos:narrower semantic relations were declared to be sub-properties of two new properties: skos:broaderTransitive and skos:narrowerTransitive (respectively). Since transitivity is not inherited, SKOS can still be used by people who want to represent loose broader relationships (LCSH, and others). At the same time SKOS will allow vocabulary owners to infer transitive broader/narrower relationships across concepts. Incidentally the SKOS Reference was just approved yesterday as a W3C Working Draft, which is its first step along the way to hopefully becoming a Recommendation.

My pottering about with LCSH and SKOS has also illustrated the value in making links between concepts explicit. Modeling LCSH as a graph data structure (SKOS), where each concept has a unique identifier has been a simple and yet powerful step in working with the data. For example to generate the image above, I simply wrote a script that transformed the subgraph related to “Non-alcoholic cocktails” to a graphviz dot file:

digraph G {
  rankdir = "BT"
  "Non-alcoholic cocktails" -> "Cocktails";
  "Alcoholic beverages" -> "Beverages";
  "Non-alcoholic beverages" -> "Beverages";
  "Cocktails" -> "Alcoholic beverages";
  "Non-alcoholic cocktails" -> "Non-alcoholic beverages";
  "Non-alcoholic beer" -> "Non-alcoholic beverages";

And then ran that through the graphviz dot utility:

% dot -T png non-alcoholic-cocktails.dot > non-alcoholic-cocktails.png

to generate the PNG file you see. It’s my hope that making a richly linked graph like LCSH/SKOS available will enable not only enhanced use of the vocabulary, but also aid in creative, collaborative refactoring of the graph. I know that these issues are not new to LC, however tools that enable refactoring along the lines of what Margherita Sini proposed for the cocktail problem above will only be possible in a world where the graph can easily be manipulated and, downstream applications (library catalogs, etc) can easily adapt to the changing concept scheme.


Recently there was a bit of interesting news around a MARBI Discussion Paper 2008-DP04 regarding semweb technologies at LC.

Related to this work are RDF/OWL representations and models for MODS and MARC, which we are also developing. Several representations of MODS in RDF/OWL, such as the one from the SIMILE project, have been made available as part of various projects and we have found they useful for our analysis and to inform our design process. We want to bring them together into one easily downloaded and maintained RDF/OWL file for use in community experimentation with RDF applications. Our time line is to have the MODS RDF ready for community comment by June.

WoGroFuBiCo cloud

access accessible addition al american analysis application applications appropriate archives areas association authority available based benefit benefits bibliographic broad broader catalog catalogers cataloging catalogs cataloguing chain change changes classification code collaboration collections committee communities community congress consequences consider considered content continue control controlled cooperative cost costs create created creating creation current data databases dc description descriptive desired develop developed development different digital discovery distribution dublin ed education effort encourage enhance environment et evidence exchange exist findings focus format formats frameworks frbr future greater group headings hidden identifiers identify ifla impact include including increase increasingly information institution institutions international knowledge language lc lcs lcsh libraries limited lis maintaining make management marc materials metadata model national need needs networks new number oclc online organization organizations outcomes outside participants particular pcc possible potential practice practices primary principles process processes production program programs provide public publishers quo range rare rda recommendations records reference relationships report require requirements research resource resources responsibility results role rules search serve service services share shared sharing sources special specific standards states status subject supply support systems technology terms time today tools types unique united university use used users using value variety various vendors vocabularies washington ways web working works

same stats as before, but the top 200 this time, and as a cloud. It’s crying out for some kind of stemming to collapse some terms together I suppose…but it’s also 3:17AM.

WoGroFuBiCo wc

word count
library 263
bibliographic 236
data 170
libraries 144
lc 127
control 109
information 98
cataloging 91
records 88
subject 82
materials 81
standards 81
use 80
congress 79
work 76
record 73
community 67
users 61
working 59
group 58
access 57
recommendations 56
resources 53
authority 52
metadata 47
future 46
new 40
environment 37
development 37
web 36
collections 35
systems 35
available 35
creation 35
services 34
headings 32
national 31
findings 30
research 30
unique 29
sharing 29
oclc 28
model 28
catalog 28
international 27
develop 27
value 27
lcsh 26
pcc 26
user 26
need 26
report 25
make 25
practices 25
rda 25
used 25
time 24
needs 24
rare 24
including 24
provide 23
discovery 23
communities 23
special 23
frbr 23
current 22
resource 22
rules 22
digital 21
cooperative 21
program 21
participants 21
management 21
service 20
dc 20
programs 20
online 20
costs 20
washington 20
standard 19
support 19
knowledge 19
different 19
appropriate 19
effort 18
applications 18
marc 18
shared 18
exchange 18
process 18
changes 17
lcs 17
increase 16
public 16
search 16
creating 16
broader 16
catalogs 16
controlled 16

I converted the pdf to text file called ‘lc’ with xpdf and then wrote a little python:

#!/usr/bin/env python

from urllib import urlopen
from re import sub

stop_words = urlopen('http://www.dcs.gla.ac.uk/idom/ir_resources/linguistic_utils/stop_words').read().split()
text = file('lc').read()

counts = {}
for word in text.split():
    word = word.lower()
    word = sub(r'\W', '', word)
    word = sub(r'\d+', '', word)
    if word == ''  or word in stop_words: continue
    counts[word] = counts.get(word,0) + 1

words = counts.keys()
words.sort(lambda a,b: cmp(counts[b], counts[a]))
for word in words[0:100]:
    print "%20s %i" % (word, counts[word])

Does me writing code to read the report count as reading the report? …

following your nose to the web of data

This is a draft of a column that’s slated to be published some time in Information Standards Quarterly. Jay was kind enough to let me post it here in this form before it goes to press. It seems timely to put it out there. Please feel free to leave comments to point out inaccuracies, errors, tips, suggestions, etc.

It’s hard to imagine today that in 1991 the entire World Wide Web existed on a single server at CERN in Switzerland. By the end of that year the first web server outside of Europe was set up at Stanford. The archives of the www-talk discussion list bear witness to the grassroots community effort that grew the early web–one document and one server at a time.

Fast forward to 2007 when 24.7 billion web pages are estimated to exist. The rapid and continued growth of the Web of Documents can partly be attributed to the elegant simplicity of the hypertext link enabled by two of Tim Berners-Lee’s creations: the HyperText Markup Language (HTML) and the Uniform Resource Locator (URL). There is a similar movement afoot today to build a new kind of web using this same linking technology, the so called Web of Data.

The Web of Data has its beginnings in the vision of a Semantic Web articulated by Tim Berners-Lee in 2001. The basic idea of the Semantic Web is to enable intelligent machine agents by augmenting the web of HTML documents with a web of machine processable information. A recent follow up article covers the “layer cake” of standards that have been created since, and how they are being successfully used today to enable data integration in research, government, and business. However the repositories of data associated with these success stories are largely found behind closed doors. As a result there is little large scale integration happening across organizational boundries on the World Wide Web.

The Web of Data represents a distillation and simplification of the Semantic Web vision. It de-emphasizes the automated reasoning aspects of Semantic Web research and focuses instead on the actual linking of data across organizational boundaries. To make things even simpler the linking mechanism relies on already deployed web technologies: the HyperText Transfer Protocol (HTTP), Uniform Resource Identifiers (URI), and Resource Description Framework (RDF). Tim Berners-Lee has called this technique Linked Data, and summarized it as a short set of guidelines for publishing data on the web:

  1. Use URIs as names for things.
  2. Use HTTP URIs so that people can look up those things.
  3. When someone looks up a URI, provide useful information.
  4. Include links to other URIs, so that they can discover more things.

The Linking Open Data community project of the W3C Semantic Web Education and Outreach Group has published two additional documents Cool URIs for the Semantic Web and How to Publish Linked Data on the Web that help IT professionals understand what it means to publish their assets as linked data. The goal of the Linking Open Data Project is to

extend the Web with a data commons by publishing various open datasets as RDF on the Web and by setting RDF links between data items from different sources.

Central to the Linked Data concept is the publication of RDF on the World Wide Web. The essence of RDF is the “triple” which is a statement about a resource in three parts: a subject, predicate and object. The RDF triple provides a way of modeling statements about resources and it can have multiple serialization formats including XML and some more human readable formats such as notation3. For example to represent a statement that the website at http://niso.org has the title “NISO - National Information Standards Organization” one can create the following triple:

<http://niso.org> <http://purl.org/dc/elements/1.1/title> "NISO - National Information Standards Organization" .

The subject is the URL for the website, the predicate is “has title” represented as a URI from the Dublin Core vocabulary, and the object is the literal “NISO - National Information Standards Organization”. The Linked Data movement encourages the extensive interlinking of your data with other people’s data: so for example by creating another triple such as:

<http://niso.org> <http://purl.org/dc/elements/1.1/creator> <http://dbpedia.org/resource/National_Information_Standards_Organization> .

This indicates that the website was created by NISO which is identified using URI from the dbpedia (a Linked Data version of the Wikipedia). One of the benefits of linking data in this way is the “follow your nose” effect. When a person in their browser or an automated agent runs across the creator in the above triple they are able to dereference the URL and retrieve more information about this creator. For example when a software agent dereferences a URL for NISO


24 additional RDF triples are returned including one like:

<http://dbpedia.org/resource/National_Information_Standards_Organization> <http://www.w3.org/2004/02/skos/core#subject> <http://dbpedia.org/resource/Category:Standards_organizations> .

This triple says that NISO belongs to a class of resources that are standards organizations. A human or agent can follow their nose to the dbpedia URL for standards organizations:


and retrieve 156 triples describing other standards organizations are returned such as:

<http://dbpedia.org/resource/World_Wide_Web_Consortium> <http://www.w4.org/2004/02/skos/core#subject> <http://dbpedia.org/resource/Category:Standards_organizations> .

And so on. This ability for humans and automated crawlers to follow their noses in this way makes for a powerfully simple data discovery heuristic. The philosophy is quite different from other data discovery methods, such as the typical web2.0 APIs of Flickr, Amazon, YouTube, Facebook, Google, etc., which all differ in their implementation details and require you to digest their API documentation before you can do anything useful. Contrast this with the Web of Data which uses the ubiquitous technologies of URIs and HTTP plus the secret sauce of the RDF triple.

As with the initial growth of the web over 10 years ago the creation of the Web of Data is happening at a grassroots level by individuals around the world. Much of the work takes place on an open discussion list at MIT where people share their experiences of making data sets available, discuss technical problems/solutions, and announce the availability of resources. At this time some 27 different data sets have been published including Wikipedia, the US Census, the CIA World Fact Book, Geonames, MusicBrainz, WordNet, OpenCyc. The data and relationships between the data are by definition distributed around the web and harvestable by anyone by anyone with a web browser or HTTP client. Contrast this openness with the relationships that Google extracts from the Web of Documents and locks up on their own private network.

Various services aggregate Linked Data and provide services on top of it such as dbpedia which has an estimated 3 million RDF links, and over 2 billion RDF triples. It’s quite possible that the emerging set of Linked Data will serve as a data test bed for intiatives like the Billion Triple Challenge which aims to foster creative approaches to data mining and Semantic Web research by making large sets of real data available. In much the same way that Tim Berners-Lee could not have predicted the impact of Google’s PageRank algorithm, or the improbable success of Wikipedia’s collaborative editing while creating the Web of Documents, it may be that simply building links between data sets on the Web of Data will bootstrap a new class of technologies we cannot begin to imagine today.

So if you are in the business of making data available on the web and have a bit more time to spare, have a look at Tim Berners-Lee’s Linked Data document and familiarize yourself with the simple web publishing techniques behind the Web of Data: HTTP, URI and RDF. If you catch the Linked Data bug join the discussion list and the conversation, and try publishing some of your data as a pilot project using the tutorials. Who knows what might happen–you might just help build a new kind of web, and rest assured you’ll definitely have some fun.

Thanks to Jay Luker, Paul Miller, Danny Ayers and Dan Chudnov for their contributions and suggestions.