The document discusses using linked data in a large higher education organization. It describes building a linked data platform for the Open University containing course, publication, media, and other university data. Several applications were developed using this linked data including a study tool, research evaluation support, and community/media analytics. Key lessons learned include the potential for simple yet useful applications, rapid development, and challenges of dealing with incomplete or heterogeneous data without application-specific assumptions. Overall, the experiences highlight both opportunities and common pitfalls of interacting with linked data at scale in a large organization.

1. Putting Linked Data to Use in a
Large Higher-Education
Organisation
Mathieu d’Aquin
Knowledge Media Institute (KMi),
The Open University, UK
@mdaquin

2. Motivation
• Many works fosus on the publication of
linked data
• But what do we do once its published
• We have built a full linked data platform
for our university (the Open
University, data.open.ac.uk)
• And built a lot of applications to
demonstrate what we could do with it
• What do we learn from getting people
to, unknowingly, use linked data?
• What experience can we reuse for the
development of interactive tools relying
on linked data?

3. Linked Data at the Open University
data.open.ac.uk

4. Linked Data at the Open University
• Course information:
– 580 modules/ description of the course, information about the levels and number of
credits associated with it, topics, and conditions of enrolment.
• Research publications:
– 16,000 academic articles / information about authors, dates, abstract and venue of the
publication.
• Podcasts:
– 2220 video podcasts and 1500 audio podcats / short description, topics, link to a
representative image and to a transscript if available, information about the course the
podcast might relate to and license information regarding the content of the podcast.
• Open Educational Resources:
– 640 OpenLearn Units / short description, topics, tags used to annotate the resource, its
language, the course it might relate to, and the license that applies to the content.
• Youtube videos:
– 900 videos / short description of the video, tags that were used to annotate the
video, collection it might be part of and link to the related course if relevant.
• University buildings:
– 100 buildings / address, a picture of the building and the sub-divisions of the building
into floors and spaces.
• Library catalogue:
– 12,000 books/ topics, authors, publisher and ISBN, as well as the course related.
• Others…

5. Applications Mobile and
Personal
Semantics
Social
Resource Discovery
Research
Exploration

6. Application 1: Study at the OU
That’s where linked data is

7. Application 1: What we learned
• From the users’ perspective
– Useful functionality can be very simple
– Combining information from different
sources
– Transparent/Seamless
• From the developers’ perspective
– Development time: from months to Looked at it in rage for
minutes hours… just didn’t think
– Interacting directly with the data, rather it wouldn’t give me an
than multiple different systems error if I mispelled the
– Lack of awareness of Semantic Web name of a property
technologies
– Correspondance with other, more common
technologies (e.g., SQL and relational
DBs) misleading
– Performance: large number of SPARQL
queries not easy to handle. Requires
caching of pre-canned queries. Contradict
the idea of open and unexpected reuse

8. Application 2: Supporting the REF
* Combining public and semi-private data
* Read/write

9. Application 2: What we learned
• From the users’ perspective Really? This uses
– No additional or duplicated output required from users: linked data? I thought
reusing what was collected in multiple systems we bought it from
– Again transparent/seemless technology some company…
– Still some confusion related to consistancy across
systems/representations
– Assumptions hard to conform with when data is drawn from
multiple systems with “unwritten conventions”
• From the developers’ perspective
– Again, rapide development
– Extensibility and flexibility
– SPARQL Query / SPARQL Update duo very powerful for
lightweight interfaces (even client side) Can you add a
– Dealing with incomplete data is tricky (we don’t know when it new field?
is incomplete)
– No “meta-properties” of the data (i.e., all IDs are unique and
non redundant)
– Assumption made are specific to the application, not generic
– Where is the problem? In the application, linked data, the
original data?

10. Application 3:
Research communities
Generic vs Specific
Interface

11. Application 3: What we learned
• From the users’ perspective Shouldn’t that be here
– Generic: more knowledge = more in that case?
functionalisties
– Generic: homogeneous interface to
heterogenesous data
– Generic: more demanding for users
– Application-driven vs data-driven navigation
– Specific interface allows for more complexity
• From the developers’ perspective
– Generic is harder: can’t make assumptions
related to the specific data/application
– Specific is less customisable/extensible:
adding new features requires custom code

12. Application 4: The OU in the media
Academics in “Arts and Humanities” Topics most commonly mentioned by
most often involved with the media (in news outlets own by the BBC (in
number of news items) number of news items)

13. Application 4: What we learned
• From the users’ perspective I would like this
chart for my
– Easy understandable outputs: embedable blog…
charts What do you
mean by “give me
– Customisable: build a dynamics dashboard in 3 minutes”?
minutes
– Benefits of linked data: bring external data
that can be jointly queried with you own
• From the developers’ perspective
– Requires a good understanding of the data
and the technology (especially SPARQL)
– Generic component to build specific interfaces
(best of both words?)
– But again cannot rely on application/data
specific assumptions (meta-properties
regarding redundancy, completeness, etc.)

14. Discussion
• Linked data should be hidden from the users
– Obvious? Yes… but is it really happening?
– Requires some aspects of the data tto be persent, eg. Huamn readbale labels
– Many lapplicatoins of linked data are still linked data applications
– Higher level concpets, such as data0integration from multiple sources, are harder to
hide
• Generic vs Specigic
– Reuse of software components is good
– But forces to addopt a specifi form of interatction witch is driven by the technicallities
and the data
– Trade-off to be found: generic + customisable
• Openess and flexibility
– … are not always easy to deal with
– Building interfaces fro the unknown.
– No assumption can be made on the data, regarding redundance and complete ness
– Need for meta-properties that can guide the building of applications (see what is
applicable)

15. Conclusion
• Applications in an large
organisations used to more
common technologies raise
challenges that help understanding
the common pitfalls of interactions
with linked data
• Important to share experiences in Thank you!
addition to techniques/tools
• To build better systems and
approaches for interaction
Any question?

16. Images (others are mine)
• Broadcast:
http://commons.wikimedia.org/wiki/File:Ibaraki_Broadcast_System_he
adquater01.jpg
• Don’t know:
http://commons.wikimedia.org/wiki/File:I_Don%27t_Know_ANY_of_Th
is!.jpg
• Development: http://commons.wikimedia.org/wiki/File:Applications-
development.svg
• Learning:
http://www.flickr.com/photos/vivacomopuder/3122401239/
• Course / degree:
http://commons.wikimedia.org/wiki/File:Degree.svg
• Article :
http://commons.wikimedia.org/wiki/File:Articles.JPG
• Open Learning: http://commons.wikimedia.org/wiki/File:Colearn_-
_learning_together.jpg
• Youtube:
http://commons.wikimedia.org/wiki/File:Logo_YouTube_por_Hernando
.svg
• Open University building :
http://www.flickr.com/photos/rattyfied/3011643690/
• Library:
http://commons.wikimedia.org/wiki/File:SteacieLibrary.jpg