=Paper=
{{Paper
|id=None
|storemode=property
|title=Consuming Linked Data within a Large Educational Organization
|pdfUrl=https://ceur-ws.org/Vol-782/ZablithEtAl_COLD2011.pdf
|volume=Vol-782
|dblpUrl=https://dblp.org/rec/conf/semweb/ZablithdBG11
}}
==Consuming Linked Data within a Large Educational Organization==
https://ceur-ws.org/Vol-782/ZablithEtAl_COLD2011.pdf
Consuming Linked Data within a Large
Educational Organization
Fouad Zablith1 , Mathieu d’Aquin1 , Stuart Brown2 and Liam Green-Hughes3
1
Knowledge Media Institute (KMi)
2
Communications
3
Information Technology
The Open University, Walton Hall, Milton Keynes, MK7 6AA, United Kingdom
{f.zablith, m.daquin, s.a.brown, l.e.green-hughes}@open.ac.uk
Abstract. Large universities tend to spread their services across several
departments to serve their substantial student base. It is very common
for this to result in developing different systems, which end up in creat-
ing many disconnected data silos within the organization. Data isolation
is one of the main bottlenecks that prevent unlocking the full potential
behind exploiting such data, to provide a better experience at the level
of application deployment and data analysis. The Open University is in
the process of connecting their data silos by relying on the Linked Data
principles within the LUCERO project. We discuss in this paper three
use-cases through which we consume Linked Data produced at the Open
University: (1) a student services use-case showing how we exploit data
connections to deliver learning material related to courses through the
university’s main course information website; (2) a mobile course appli-
cation that enables students to easily explore courses by subject, quali-
fication or research topic; and (3) a Leanback TV service that provides
students the ability to watch, with a degree of control, a set of podcasts
grouped in different channels. Through these use cases, we highlight in
this paper the advantages and effects of consuming Linked Data within
an organization.
1 Introduction
Today’s organizations generate a significant amount of information as a result of
their daily operational activities. The Open University is a large higher and fur-
ther education institution in the UK which is dedicated to distance learning. It
delivers courses and qualifications to more than 200,000 students across the UK,
as well as in other countries. To realise this, the OU’s information repositories
are among its core assets. However, as it is the case of most large organizations,
different systems tend to be developed throughout the years to perform specific
tasks, which result in different disconnected data backbones. The absence of con-
nections and links at the data level often makes data interchangeability between
systems difficult and costly, which often leads to preventing the organization in
fully exploiting their data.
Through the LUCERO project4 , the OU has put in place a workflow to
connect their various data repositories following the Linked Data principles [6,
7]. The current published data include for example course information (among
others), with links to items internal to the OU, such as the library catalogue
and other learning materials, as well as to external entities, such as countries in
GeoNames5 or other entities in DBpedia6 .
We aim in this paper to give an overview of the LUCERO project, and the
workflow to generate Linked Data (Section 3). Then we discuss the consumption
of Linked Data that we illustrate through three use-cases: (1) the student services
use-case (Section 4), where we show how Linked Data helped discovering related
learning material to courses, and displaying them on mainstream courses’ web
pages of the OU; (2) the mobile course browser (Section 5), through which
we show how links between academic qualifications, subjects and courses helped
delivering a better course browsing application to students; and (3) the Leanback
TV webapp (Section 6) which provides a Leanback TV experience by relying on
video material from the OU’s Linked Data set.
2 Related Work
In today’s push towards creating semantics behind the data exposed over the
web, we are witnessing an increase in the publishing side of Linked Data from
educational bodies, following the original effort realised at the OU7 . For example,
the University of Southampton have made their data available as Linked Data8 .
There are also plans from the University of Oxford to do the same9 , as well as
the University of Lincoln10 , among others.
While more and more data is being exposed (as can be inferred from the
Linking Open Data cloud [1]), one aspect that has not yet been fully explored
is the consumption side of Linked Data. We believe that the effect of Linked
Data on the development effort, as well as on the potential applications that
can be done with the data is a long term process. We argue that there is no
one killer app to be discovered, but many small applications that will introduce
substantial benefits at various levels to an organization and its data.
3 LUCERO Overview
The LUCERO project aims to transform and expose institutional repositories
within the Open University as Linked Data. We present in this section the work-
4
http://lucero-project.info
5
http://www.geonames.org
6
http://www.dbpedia.org
7
http://www.jisc.ac.uk/news/stories/2010/11/linkeddata.aspx
8
http://data.southampton.ac.uk/
9
http://data.ox.ac.uk/
10
http://data.lincoln.ac.uk/
flow involved in generating the OU Linked Data, in addition to the data already
available.
3.1 The Workflow for Generating Linked Data
As part of the LUCERO’s main contributions, a workflow was put in place to
handle the extraction and exposure of RDF elements from existing repositories.
Figure 1 highlights the steps involved in this workflow, which we briefly describe
in this section.
Fig. 1. The LUCERO workflow.
In the first step, we Collect the information that resides in the original reposi-
tories across the various departments of the Open University. We set up different
extractors to handle the extraction of the required elements from the sources.
For example, the RSS extractor is customized to process video or audio Pod-
casts11 and publication data (i.e., Open Research Online12 ). The XML updater
is used where the data is available in XML formal such as course descriptions. A
scheduler handles an automatic daily process to check the changes that occurred
at the data level, and run the workflow accordingly.
11
http://podcast.open.ac.uk
12
http://oro.open.ac.uk
Based on the identified items, we Extract the relevant RDF elements with
the appropriate vocabulary to use. For example, in the case of a course item,
we identify the need to model the course title, availability, cost, etc. In terms
of the choice of vocabularies, our approach was to mostly reuse available on-
tologies, resulting in having in some cases more than one to apply. For ex-
ample, AIISO13 and Courseware14 are both used, among others, to represent
courses. An important component of the workflow is the Entity Name System,
which ensures that each entity extracted gets a unique identifier across all our
datasets, independently from its repository of origin. This guarantees for ex-
ample that, when a book coming from the library catalogue dataset is part of
course M36615 and a podcast is also related to course M366, both the book and
podcast are pointing to the same course with a unique identifier. To achieve this,
we design our code to apply a set of specific patterns per entity type to follow
predefined URI prefixes (e.g. “http://data.open.ac.uk/course/” for Courses, or
“http://data.open.ac.uk/library/” for library material), coupled with a unique
identifier originating from the source.
Once the RDF data to add (or delete) are identified, we store them in a triple
store, and expose them through a web portal accessible at http://data.open.
ac.uk. This portal provides a SPARQL endpoint, as well as information about
the datasets available, sample queries and applications.
3.2 The Data Available
The OU has already extracted and made available Linked Data from various
sources across the university. The process is continuous, with more data made
available whenever new resources are made available. The current set of data
that can be used include:
– Courses Information: In addition to the ability of getting the informa-
tion through the OU website, courses information were also accessible in an
XML format, which made the transformation into Linked Data easier. This
dataset provides access to courses information including title, description,
availability by date and location, prices along with related books and other
material.
– Podcasts: This dataset provides access to the OU podcast items, with their
corresponding title, related courses, subjects, video duration, etc. This set
has been extracted from the OU podcast RSS feeds16 .
– Library Catalogue: The OU library catalogue has been transformed from
MARC records [2] into Linked Data. This dataset provides access to library
items such as books, AV materials, reports among others. Such items were
linked back to OU courses, and held information related to subjects, authors,
publisher, Library of Congress Subject Headings [3], etc.
13
http://vocab.org/aiiso/schema
14
http://courseware.rkbexplorer.com/ontologies/courseware
15
http://data.open.ac.uk/page/course/m366.html
16
see for example http://podcast.open.ac.uk/feeds/l314-spanish/rss2.xml
– Research Publications: This dataset provides OU research publications
with information about the authors, publication type (e.g., conference pa-
pers, theses, books, etc.), date of publication, among others.
– OU YouTube Channel: This dataset exposes the OU YouTube Channel17
as Linked Data, with information about the title, tags, download URL and
thumbnails.
– OpenLearn: The free online courses provided by the OU through its Open-
Learn system18 are also available now as Linked Data. This includes the title
of OpenLearn units, topics, related courses, tags, among others.
– Reading Experience Database: Reading experiences from the Reading
Experiences Database19 are also available as Linked Data. This dataset con-
tains information about readers, their gender, their reading objects and their
experience details.
– The Open Arts Archive Events: Events from the Open Arts Archive
website20 are made accessible through data.open.ac.uk. This includes infor-
mation about the events’ collaborators, artists, speakers and related podcasts
among others.
– KMi News and People: These datasets present information about research
staff21 at the Knowledge Media Institute along with related news articles22 .
– Estates Buildings: This dataset contains information about the OU build-
ings located across the UK. The buildings’ address, floor details and depic-
tion are all accessible as Linked Data.
This wealth of data has been exploited in various scenarios (see for exam-
ple [7] and [4]). A list of available applications is available online23 . We present
next our three use-cases through which we show the potentials of consuming
Linked Data at the OU.
4 Student Services Use-Case
Students are among the main consumers of data at the Open University. Pro-
viding them with a good experience in reaching the required information with
the least effort needed is key to the university’s success. The first use-case we
present here is in the context of student services. Students are constantly look-
ing for appropriate courses to enrol in at the Open University. Currently this is
done through the main “Study at the OU” website24 , where students can browse
courses by subject, and get further information related to the courses and quali-
fications they are counted towards. The Open University produces a substantial
17
http://www.youtube.com/user/TheOpenUniversity
18
http://openlearn.open.ac.uk
19
http://www.open.ac.uk/Arts/RED/index.html
20
http://www.openartsarchive.org
21
http://kmi.open.ac.uk/people/
22
http://kmi.open.ac.uk/news/
23
http://data.open.ac.uk/applications/
24
http://www3.open.ac.uk/study
amount of courses related material, for example through the OU YouTube Chan-
nel or podcast website. Such material can be of great benefit for students to form
a better understanding of what to expect from a course, and helping them make
a decision about what course to take. However the problem is that traditionally,
explicit links between courses and related material are not available. Moreover,
modifying the underlying data infrastructure (e.g., databases) or application to
expose such links can be very costly in terms of development time and resources.
With the availability of the Linked Data portal at the Open University
through data.open.ac.uk where items and their corresponding links are explicitly
defined, it became possible to easily get the entities related to specific courses.
Having courses, podcasts and OU YouTube material (among others) in a co-
herent representation following well defined vocabularies, enabled a seamless ex-
traction of needed entities through the available SPARQL endpoint. With this in
place, we are in the process of extending the current courses’ pages at the Open
University, to offer students the functionality to explore learning material when
visiting a course page. Figure 2 shows an example of related media, displayed
on the right of the screen.
Fig. 2. Related media to the “AA100 – Arts Past and Present” course.
The formal “Study at the OU” site currently only describes Open University
modules in text and yet there are other opportunities to demonstrate module
content to prospective students through multimedia (podcast/iTunesU content
and YouTube) or through the OU’s Open Educational Resources project Open-
Learn. Before the metadata associated with this content was represented as
Linked Data the inherent links between the OU’s OER, multimedia content
and modules were not exploitable by machines. This meant that any attempt
to supplement the OU’s online prospectus with related content represented an
unrealistic requirement in terms of man hours, especially considering that such
content quickly becomes out-of-date.
The data used through this application are the Podcast and iTunesU datasets,
in addition to the OpenLearn and YouTube data. Data is provided to this appli-
cation through SPARQL queries returning all multimedia / OpenLearn content
related to a module is made available as XML, and will be incorporated into the
XML files that draw together data form other institutional OU sources before
being rendered as HTML.
5 Mobile Course Application
The Open University currently supports an iPhone and iPad application making
content from its online prospectus available as an app. This application works
well but there was a desire to improve it in two ways: (1) to make the applica-
tion available across more platforms, and (2) to increase interactivity by making
available material related to OU subject areas (e.g., Arts and Humanities, Maths
and Computing, etc.) available on description pages as well as making more spe-
cific related content available at module description level (e.g., content related
more specifically to “A101 - Introduction to Humanities”). Before LUCERO
made these repositories available in a Linked Data format it was not possible for
the OU to programmatically combine the data that creates the online prospec-
tus with the content that it publishes in iTunesU, YouTube and OpenLearn.
Although at a human level it was known that these resources related to mod-
ules (indeed frequently they are derived from module production process) this
relationship was not expressed in a machine-readable way.
The newly-developed Study at the OU application is an HTML5 build made
available in Android and iOS through thin client applications which serve to
supplement a view onto the site through the devices browser (e.g., some of the
navigation is handled through native application functionality whilst the content
is a real-time web view). A similar approach is taken to the way the content will
be surfaced via a Chrome add-on. Figure 3 shows a page through which students
can explore courses and related content by qualifications, subjects, research, etc.
The data consumed in this app are from the Podcast, iTunesU, OpenLearn
and YouTube datasets. The application reads (selected) data from the XML
that is used to build the desktop online prospectus and combines this with data
from data.open.ac.uk. When using the application a user can (whether they are
at subject area level or module level) opt to view related resources. Podcast
Fig. 3. Mobile course application.
and YouTube content are handled through the devices native functionality and
links to related OpenLearn content (webpages) are made available through the
browser. The content read from data.open.ac.uk is dynamic and up-to-date so
the application shows only the latest 3 resources from each area. This serves to
keep the pages relevant and fresh and also maintains a good level of usability.
6 Leanback TV Webapp
The OU has a plethora of video material spanning over various topics and sub-
jects. With these hundreds of hours of interesting recordings, the question is:
how do we engage the students in exploiting this vast resource of information,
and exploring new areas of interests that might trigger new learning needs? With
the current podcast portal, the student has to know what he/she is looking for in
advance, before searching or going through the topics for the podcast of interest.
The purpose of Leanback TV applications is to provide viewers with a series
of automatically selected set of video material that can be played one after the
other, coupled with a degree of control over what to watch. We are witnessing an
increase in Leanback TV services, provided for example by YouTube Leanback25 ,
Redux TV26 and Vimeo Couchmode27 . In these applications, a set of videos are
preselected for viewers, with the ability to skip or choose among the set of videos,
25
http://www.youtube.com/leanback
26
http://redux.com/tv
27
http://vimeo.com/couchmode
as well as choosing different channels. Such channels group the videos to watch
into themes, such as YouTube’s Trends and Best of YouTube.
In our context, we provide a Leanback TV experience over the set of learn-
ing podcast material. However, podcast information can be extracted from the
current systems through its RSS feed, which doesn’t provide the flexibility we
needed to manipulate and extract the data. We rely on the Linked Data provided
through data.open.ac.uk, which we access through SPARQL queries to generate
the set of videos to play through an HTML5 webapp interface. We used differ-
ent queries to populate the channels with podcasts related to: OU research, OU
learn and OU life. This is highlighted in Figure 4. Videos are also filtered based
on their length, and only the ones that are below 10 minutes are shown to the
users. Short videos have a higher chance to be watched by students, who know
that something else will come up soon, in case such podcasts are of little interest
to them. This will encourage students to explore new subjects, while relaxing in
front of their TV or computer without having to choose what to watch next.
Fig. 4. Screenshot of the Leanback TV webapp.
With the presence of SPARQL, it is possible to create a fine grained selection
of videos. For example, one can create a channel about videos related to a specific
course, or a channel about the renaissance architecture. This is much harder to
achieve through RSS feeds without further data customization at the source.
Once the system is put in place, the options of creating new channels is endless,
and very easy to extend. In the current demo28 , the selected podcasts are mainly
28
http://labs.greenhughes.com/ldleanback/ – this application currently only works
with the Google Chrome browser
of general interest, where very specialized videos such as one hour lectures on
mathematics are not needed. However, in other contexts, this could exactly be
what’s needed for a mathematics course webpage. Such different channels can
now be easily created through just creating a different SPARQL channel. This
is a very useful for scenarios where university teachers can have custom made
channels for their classrooms. For example, this is the SPARQL query used to
generate the OU Life podcast channel:
PREFIX dc:
PREFIX wm:
PREFIX rdfs:
SELECT ?thumb ?desc ?title ?source
WHERE {
?podcast ?thumb .
?podcast dc:title ?title .
?podcast wm:description ?desc .
?podcast ?source .
?podcast wm:createDate ?createDate .
?podcast dc:subject .
?podcast rdfs:type .
}
ORDER BY DESC(?createDate)
LIMIT 25
Through this query, we are getting the thumbnail, description, title and source
of the podcasts limited to the OU Life subject (i.e., < http : //data.open.ac.uk/
topic/ou lif e >), ordered by their creation date. Figure 5 displays a snippet
of the results that can be processed through data.open.ac.uk. The application
is based on Google TV Resources29 , and the full implementation details are
available online30 .
7 Discussion
Linked Data is transforming the way we access and consume data. From our
three use-cases, we observe that the effects on the organizations and the way
they consume and provide their data can be at the following levels:
Application development effort: Linked Data reduces the cost involved to
pull data out of repositories. In all the three use-cases, data was retrieved
from one common endpoint using common tools and standards. Once the de-
veloper masters the way to extract Linked Data, and apply them in his/her
29
http://code.google.com/p/gtv-resources/
30
http://www.greenhughes.com/content/html5-leanback-tv-webapp-brings-sparql-
living-room
Fig. 5. SPARQL query results.
development environment, it will be very easy to apply the same methods
in other applications. This will encourage developers to focus on the data
usage, rather than on the technical difficulties involved. In the case of the
three applications described in this paper, the development has been per-
formed without having to go back to the original data maintainers. This,
by itself, is considered a substantial decrease in the cost involved within the
organization.
User experience: We observe that Linked Data enabled the development of
applications that will enhance user experience. This observation surely needs
to be backed by some kind of survey once the applications are fully deployed
and accessed by a substantial amount of users. However we foresee that,
by for example simply aiding the users by decreasing the required effort in
finding relevant material while looking for courses, or interesting podcasts,
our applications will be positively embraced by our end-users.
Connecting to others: In this paper, we have mostly focused on the benefit of
connecting data silos internal to the organization. However, we also provide
links to external datasets, including for example to the countries in which
courses are available in GeoNames, to the description of the Open University
in http://education.data.gov.uk and to the postcode units of OU buildings
in http://data.ordnancesurvey.co.uk. Providing such external links not only
supports the enrichment of our own data, but also provides users with ad-
ditional entry points into it, which are not dictated by the university’s view
on the provided information. We are for example investigating scenarios in
which resources at the OU could be linked to TV programs from the BBC,
providing ways to discover study material relating to the topics treated in a
TV program.
8 Conclusion
In this paper, we have presented three use-cases that demonstrate how Linked
Data can be consumed in a large organization such as the Open University. The
goal was, through these examples, to discuss the benefits, issues and effects of
integrating Linked Data practices in such an organization. Indeed, based on our
experience in building the Linked Data platform of the OU and supporting other
departments in consuming such data, we believe that the success of Linked Data
will not be based on the development of “killer apps”, but on solving in a cost
effective way numerous problems that organizations, data managers and devel-
opers are facing. We also wanted to emphasis the importance of organizational
aspects in the adoption of Linked Data in the context of a large organization.
We selected the three presented use cases as they represent clear examples where
Linked Data is consumed in projects led by non-academic services of the Uni-
versity (communications/online services, student services and IT), being clearly
driven by the “business needs” of the organization, rather than by the applica-
tion of a particular technological solution.
As we mentioned at the beginning of the paper, many other universities are
now following the same approach as we have developed within the LUCERO
project, to expose public university resources as Linked Data. While this trend
is still at an early stage, interesting cases and ways of consuming linked data are
expected to emerge from it. Being able to discover educational resources related
to a topic or a course independently from the way and place in which they
were produced seems an obvious scenario. It however generates new challenges
related to the aggregation of data from different institutions31 , as well as to the
availability of common classification schemes for these resources [5].
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31
See http://linkeduniversities.org