=Paper=
{{Paper
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|storemode=property
|title=The OU Linked Open Data: Production and Consumption
|pdfUrl=https://ceur-ws.org/Vol-717/paper1.pdf
|volume=Vol-717
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==The OU Linked Open Data: Production and Consumption==
https://ceur-ws.org/Vol-717/paper1.pdf
The OU Linked Open Data:
Production and Consumption
Fouad Zablith, Miriam Fernandez and Matthew Rowe
Knowledge Media Institute (KMi), The Open University
Walton Hall, Milton Keynes, MK7 6AA, United Kingdom
{f.zablith, m.fernandez, m.c.rowe}@open.ac.uk
Abstract. The aim of this paper is to introduce the current efforts to-
ward the release and exploitation of The Open University’s (OU) Linked
Open Data (LOD). We introduce the work that has been done within
the LUCERO project in order to select, extract and structure subsets
of information contained within the OU data sources and migrate and
expose this information as part of the LOD cloud. To show the potential
of such exposure we also introduce three different prototypes that exploit
this new educational resource: (1) the OU expert search system, a tool
focused on finding the best experts for a certain topic within the OU
staff; (2) the Buddy Study system, a tool that relies on Facebook infor-
mation to identify common interest among friends and recommend po-
tential courses within the OU that ‘buddies’ can study together, and; (3)
Linked OpenLearn, an application that enables exploring linked courses,
Podcasts and tags to OpenLearn units. Its aim is to enhance the brows-
ing experience for students, by detecting relevant educational resources
on the fly while reading an OpenLearn unit.
Keywords: Linked Open Data, education, expert search, social net-
works.
1 Introduction
The explosion of the Linked Open Data (LOD) movement in the last few years
has produced a large number of interconnected datasets containing information
about a large variety of topics, including geography, music and research publi-
cations among others. [2]
The movement is receiving worldwide support from public and private sectors
like the UK1 and US2 governments, international media outlets, such as the
BBC [5] or the New York Times [1], and companies with a social base like
Facebook.3 Such organisations are supporting the movement either by releasing
1
http://data.gov.uk
2
http://www.data.gov/semantic/index
3
http://developers.facebook.com/docs/opengraph
large datasets of information or by generating applications that exploit it to
connect data across different locations.
Despite its relevance and the support received in the last few years, very few
pieces of work have either released or exploited LOD in the context of education.
One of these few examples is the DBLP Bibliography Server Berlin,4 which
provides bibliographic information about scientific papers. However, education is
principally one of the main sectors where the application of the LOD technologies
can provoke a higher impact.
When performing learning and investigation tasks, students and academics
have to go through the tedious and laborious task of browsing different infor-
mation resources, analysing them, extracting their key concepts and mentally
linking data across resources to generate their own conceptual schema about the
topic. Educational resources are generally duplicated and dispersed among dif-
ferent systems and databases, and the key concepts within these resources as well
as their inter and intra connections are not explicitly shown to users. We believe
that the application of LOD technologies within and across educational insti-
tutions can explicitly generate the necessary structure and connections among
educational resources, providing better support to users in their learning and
investigation tasks.
In this context, the paper presents the work that has been done within The
Open University (OU) towards the release and exploitation of several educational
and institutional resources as part of the LOD cloud. First, we introduce the
work that has been done within the LUCERO project to select, extract and
structure subsets of OU information as LOD. Second, we present the potential
of this data exposure and interlinking by presenting three different prototypes:
(1) the OU expert search system, a tool focused on finding the best experts for a
certain topic within the OU staff; (2) the Buddy Study system, a tool focused on
exploiting Facebook information to identify common interests among friends and
recommend potential courses within the OU that ‘buddies’ can study together,
and; (3) Linked Open Learn, an application that enables exploring linked courses,
Podcasts and tags to OpenLearn units.
The rest of the paper is organised as follows: Section 2 presents the state of the
art in the areas of LOD within the education context. Section 3 presents the work
that has been done within the LUCERO project to expose OU data as part of
the LOD cloud. Sections 4, 5 and 6 present example prototype applications that
consume the OU’s LOD for Expert Search, Buddy Study and Linked OpenLearn
respectively. Section 7 describes the conclusions that we have drawn from this
work, and section 8 presents our plans for future work.
2 Related Work
While LOD is being embraced in various sectors as mentioned in the previous sec-
tion, we are currently witnessing a substantial increase in universities adopting
4
http://www4.wiwiss.fu-berlin.de/dblp/
the Linked Data initiative. For example, the University of Sheffield’s Depart-
ment of Computer Science5 provides a Linked Data service describing research
groups, staff and publications, all semantically linked together[6]. Similarly the
University of Southampton has recently announced the release of their LOD por-
tal (http://data.southampton.ac.uk), where more data will become available in
the near future. Furthermore, the University of Manchester’s library catalogue
records can now be accessed in RDF format6 . In addition, other universities are
currently working on transforming and linking their data: University of Bris-
tol,7 Edinburgh (e.g., the university’s buildings information is now generated
in LOD8 ), and Oxford9 . Furthermore the University of Muenster announced
a funded project, LODUM, the aim of which is to release the university’s re-
search information as Linked Data. This includes information related to people,
projects, publications, prizes and patents.10
With the increase of the adoption of LOD publishing standards, the exchange
of data will be much easier, not only within one university, but also across the
LOD ready ones. This enables, for example, the comparison of specific qualifi-
cations offered by different universities in terms of courses required, pricing and
availability.
3 The Open University Linked Open Data
The Open University is the first UK University to expose and publish its orga-
nizational information in LOD.11 This is accomplished as part of the LUCERO
project (Linking University Content for Education and Research Online)12 , where
the data extraction, transformation and maintenance are performed. This en-
ables having multiple hybrid datasets accessible in an open way through the
online access point: http://data.open.ac.uk.
The main purpose of releasing all this data as part of the LOD cloud is that
members of the public, students, researchers and organisations will be able to
easily search, extract and, more importantly, reuse the OU’s information and
data.
3.1 Creating the OU LOD
Detailed information about the process of LOD generation within the OU is
available at the LUCERO project website.12 We briefly discuss in this section
5
http://data.dcs.shef.ac.uk
6
http://prism.talis.com/manchester-ac
7
https://mmb.ilrt.bris.ac.uk/display/ldw2011/University+of+Bristol+data
8
http://ldfocus.blogs.edina.ac.uk/2011/03/03/university-buildings-as-linked-data-
with-scraperwiki
9
http://data.ox.ac.uk
10
http://www.lodum.de
11
http://www3.open.ac.uk/media/fullstory.aspx?id=20073
12
http://lucero-project.info
the steps involved in the creation of Linked Data. To achieve that, the main
requirement is to have a set of tools that generate RDF data from existing data
sources, load such RDF into a triple store, and make it accessible through a web
access point.
Given the fact that the OU’s data repositories are scattered across many
departments, using different platforms, and subject to constant update, a well-
defined overflow needs to be put in place. The initial workflow is depicted in
Figure 1, and is designed to be efficient in terms of time, flexibility and reusabil-
ity. The workflow is component based, and the datasets characteristics played
a major role in the implementation and setup of the components. For exam-
ple, when the data sources are available in XML format, the XML updater will
handle the process of identifying new XML entities and pass them to the RDF
extractor, where the RDF data is generated, and ready to be added to (or re-
moved from) the triple store. Finally the data is exposed to the web, and can be
queried through a SPARQL endpoint.13
The scheduler component takes care of initiating the extraction/update pro-
cess at specific time intervals. This update process is responsible for checking
what was added, modified, or removed from the dataset, and accordingly ap-
plies to the triple store the appropriate action. Having such a process in place
is important in the OU scenario where the data sources are continuously chang-
ing. Another point worth mentioning is the linking process that links entities
coming from different OU datasets (e.g., courses mentioned in Podcast data and
library records), in addition to linking external entities (e.g., course offerings in
a GeoNames defined location14 ). To achieve interlinking OU entities, indepen-
dently from which dataset the extraction is done, we rely on an Entity Named
System, which generates a unique URI (e.g., based on a course code) depend-
ing on the specified entity (this idea was inspired from the Okkam project15 ) .
Such unique URIs enable a seamless integration and extraction of linked entities
within common objects that exist in the triple store and beyond, one of the core
Linked Data requirements [3].
3.2 The Data
Data about the OU courses, Podcasts and academic publications is already
available to be queried and explored, and the team is now working to bring
together educational and research content from the university’s campus infor-
mation, OpenLearn (already available for testing purposes) and library mate-
rial. More concretely, data.open.ac.uk offers a simple browsing mechanism, and
a SPARQL endpoint to access the following data:
13
http://data.open.ac.uk/query
14
http://www.geonames.org
15
http://www.okkam.org
Fig. 1. The LUCERO Workflow
– The Open Research Online (ORO) system16 , which contains information
about academic publications of OU research. For that, the Bibliographic
Ontology (bibo)17 is mainly used to model the data.
– The OU Podcasts,18 which contain Podcast material related to courses and
research interests. A variety of ontologies are used to model this data, in-
cluding the W3C Media Ontology,19 in addition to a specialised SKOS20
representation of the iTunesU topic categories.
– A subset of the courses from the Study at the OU website,21 which provides
courses information and registration details for students. We model this data
by relying on the Courseware,22 AIISO23 and GoodRelations ontologies [4],
in addition to extensions that reflect OU specific information (e.g., course
assessment types).
Furthermore, there are other sources of data that are currently being pro-
cessed. This includes for example the OU list of provided publications, the
16
http://oro.open.ac.uk
17
http://bibliontology.com/specification
18
http://podcast.open.ac.uk
19
http://www.w3.org/TR/mediaont-10
20
http://www.w3.org/2004/02/skos
21
http://www3.open.ac.uk/study
22
http://courseware.rkbexplorer.com/ontologies/courseware
23
http://vocab.org/aiiso/schema
library catalogue, and public information about locations on the OU campus
(e.g., buildings) and university staff.
4 The OU Expert Search
Expert search can be defined as the task of identifying people who have relevant
expertise in a topic of interest. This task is key for every enterprise, but especially
for universities, where interdisciplinary collaborations among research areas is
considered a high success factor. Typical user scenarios in which expert search is
needed within the university context include: a) finding colleagues from whom
to learn, or with whom to discuss ideas about a particular subject; b) assembling
a consortium with the necessary range of skills for a project proposal, and; c)
finding the most adequate reviewers to establish a program committee.
As discussed by Yimam-Seid and Kobsa [7], developing and manually updat-
ing an expert system database is time consuming and hard to maintain. How-
ever, valuable information can be identified from documents generated within
an organisation [8]. Automating expert finding from such documents provides
an efficient and sustainable approach to expertise discovery.
OU researchers, students and lecturers constantly produce a plethora of doc-
uments, including for example conference articles, journal papers, thesis, books,
reports and project proposals. As part of the LUCERO project, these docu-
ments have been pre-processed and made accessible as LOD. The purpose of
this application is therefore to exploit such information so that OU students
and researchers can find the most appropriate experts starting from a topic of
interest.24
4.1 Consumed Data
This application is based on two main sources of information: (a) LOD from the
Open Research Online system, and (b) additional information extracted from
the OU staff directory. The first information source is exploited in order to
extract the most suitable experts about a certain topic. The second information
source complements the previous recommended set of experts by providing their
corresponding contact information within the OU. Note that sometimes, ex-OU
members and external collaborators or OU researchers may appear in the ranking
of recommended experts. However, for those individuals, no contact information
is provided, indicating that those experts are not part of the OU staff.
As previously mentioned, the information provided by Open Research On-
line contains data that describe publications originating from OU researchers.
In particular, among the properties provided for each publication, this system
exploits the following ones: a) the title, b) the abstract, c) the date, d) the au-
thors and, e) the type of publication, i.e., conference paper, book, thesis, journal
paper, etc.
24
The OU Expert Search is accessible to OU staff at: http://kmi-
web15.open.ac.uk:8080/ExpertSearchClient
To exploit this information the system performs two main steps. Firstly when
the system receives the user’s query, i.e., the area of expertise where a set of
experts need to be found (e.g., “semantic search”), the system uses the title and
abstract of the publications to find the top-n documents related to that area of
expertise. At the moment n has been empirically set to 10.
Secondly, once the top-n documents have been selected, the authors of these
documents are extracted and ranked according to five different criteria: (a) orig-
inal score of their publications, (b) number of publications, (c) type of publica-
tions, (d) date of the publications and, (e) other authors of the publication.
The initial score of the publications is obtained by matching the user’s key-
word query against the title and the abstract of the OU publications. Publica-
tions that provide a better match within their title and abstract against the key-
words of the query are ranked higher. This matching is performed and computed
using the Lucene25 text search engine. Regarding the number of publications,
authors with a higher number of publications (among the top-n previously re-
trieved) are ranked higher. Regarding the type of publication, theses are ranked
first, then books, then journal papers, and finally conference articles. The ratio-
nality behind this is that an author writing a thesis or a book holds a higher level
of expertise than an author who has only written conference papers. Regarding
the date of the publication, we consider the ‘freshness’ of the publications and
continuity of an author’s publications within the same area. More recent publi-
cations are ranked higher than older ones, and authors publishing in consecutive
years about a certain topic are also ranked higher than authors that have spo-
radic publications about the topic. Regarding other authors, experts sharing a
publication with fewer colleagues are ranked higher. The rationality behind this
is that the total knowledge of a publication should be divided among the exper-
tise brought into it, i.e., the number of authors. Additionally we also consider
the order of authors in the publication. Main authors are considered to have a
higher level of expertise and are therefore ranked higher.
To perform the first step (i.e., retrieving the top-n documents related to
the user’s query) we could have used the SPARQL endpoint and, at run-time,
searched for those keywords within the title and abstract properties of the pub-
lications. However, to speed the search process up, and to enhance the query-
document matching process, we have decided to pre-process and index the title
and abstract information of the publications using the popular Lucene search
engine. In this way, the fuzzy and spelling check query processing and rank-
ing capabilities of the Lucene search engine are exploited to optimise the initial
document search process.
To perform the second step, once the top-n documents have been selected,
the rest of the properties of the document (authors, type, and date) are obtained
at run-time using the SPARQL endpoint.
Finally, once the set of authors have been ranked, we look for them in the OU
staff directory (using the information about their first name and last name). If the
author is included in the directory, the system provides related information about
25
http://lucene.apache.org/java/docs/index.html
the job title, department within the OU, e-mail address and phone number.
By exploiting the OU staff directory we are able to identify which experts are
members of the OU and which of them are external collaborators, or old members
not further working for the institution.
Without the structure and conceptual information provided by the OU LOD,
the implementation of the previously described ranking criteria, as well as the
interlinking of data with the OU staff directory, would have required a huge
data pre-processing effort. The OU LOD provides the information with a fine-
grained structure that facilitates the design of ranking criteria based on multiple
concepts, as well as the interlinking of information with other repositories.
4.2 System Implementation
The system is based on lightweight client server architecture. The back end
(or server side) is implemented as a Java Servlet, and accesses the OU LOD
information by means of HTTP requests to the SPARQL endpoint. Some of
the properties provided by the LOD information (more particularity the title
and the abstract of the publications) are periodically indexed using Lucene to
speed-up and enhance the search process by means of the exploitation of its
fuzzy and spell checker query processing, and ranking capabilities. The rest of
the properties (authors, date, and type of publications) are accessed at run time,
once the top-n publications have been selected.
The front end is a thin client implemented as a web application using only
HTML, CSS and Javascript (jQuery).26 The client doesn’t handle any processing
of the data, it only takes care of the visualisation of the search results and the
search input. It communicates with the back-end by means of an HTTP request
that passes as a parameter the user’s query and retrieves the ranking of authors
and their corresponding associated information by means of a JSON object.
4.3 Example and Screenshots
In this section, we provide an example of how to use the OU expert search
system. As shown in Figure 2, the system receives as a keyword query input
“semantic search”, with the topic for which the user aims to find an expert. As
a result, the system provides a list of authors (“Enrico Motta”, “Vanessa Lopez ”,
etc), who are considered to be the top OU experts in the topic. For each expert,
if available, the system provides the contact details (department, e-mail, phone
extension) and the top publications about the topic. For each publication, the
system shows its title, the type of document, and its date. If the user passes the
cursor on the top of the title of the publication, the summary is also visualised
(see the example in Figure 2 for the publication “Reflections of five years of
evaluating semantic search systems”). In addition the title of the publication
also constitutes a link to its information in the open.ac.uk domain.
26
http://www.jquery.com
Fig. 2. The OU Expert Search system
5 Buddy Study
The Open University is a well-established institution in the United Kingdom, of-
fering distance-learning courses covering a plethora of subject areas. A key factor
in enabling learning and understanding of course materials is support for stu-
dents, provided in the form of an on-hand tutor for each studied module, where
interactions with the tutor are facilitated via the Web and/or email exchanges.
An alternative method of support could be provided through peers, in a similar
manner to a classroom environment, where working together and explanations
of problems from disparate viewpoints enhances understanding.
Based on this thesis, Buddy Study27 combines the popular social networking
platform Facebook with the OU Linked Data service, the goal being to suggest
learning partners – so called ‘Study Buddies’ – from a person’s social network
on the site together with possible courses that could be pursued together.
5.1 Consumed Data
Buddy Study combines information extracted from Facebook with Linked Data
offered by The Open University, where the former contains ‘wall posts’ – mes-
sages posted publicly on a person’s profile page – and comments on such wall
posts, while the latter contains structured, machine-readable information de-
scribing courses offered by The Open University.
27
http://www.matthew-rowe.com/BuddyStudy
Combining the two information sources, in the form of a ‘mashup’, is per-
formed using the following approach. First the user logs into the application –
using Facebook Connect – and grants access to their information. The appli-
cation then extracts the most recent n wall posts and the comments on those
posts – n can be varied, thereby affecting the later recommendations. Given the
extracted content, cleaning is then performed by removing all the stop words,
thus reducing the wall posts and comments to their basic terms.
A bag of words model is compiled for each person in the user’s social network
as follows: for each wall post or comment posted by a given person all the terms
are placed in the bag, maintaining duplicates and therefore frequencies. This
model maintains information of the association between a user and his/her social
network members in the form of shared terms. A bag of words model is then
compiled for each OU course in a similar manner: first we query the SPARQL
endpoint of the OU’s Linked Data asking for the title and description for each
course. For the returned information, stop words are removed and the title and
description – containing the remaining terms – are then used to build the bag
of words model for the course.
The goal of Buddy Study is to recommend study partners to support course
learning. Therefore we compare the bag of words model of each person with
the bag of words model of each course, recording the frequency and terms that
overlap. The user’s social network members are then ranked based on the number
of overlapping terms – the intuition being that the greater the number of common
terms with courses, the greater the likelihood of a course being correlated with
the user. Variance of n will therefore affect this ranking, given that the inclusion
of a greater number of posts will increase the number of possible study partners,
while smaller values for n will yield more recently interacted with social network
members. Variance of this parameter is provided in the application.
The application is not finished yet; we still need to recommend possible
courses that could be studied with each possible study buddy. This is performed
in a similar fashion, by comparing the bag of words model of the social network
member with the model of each course, counting the frequencies of overlapping
terms for each course, and then ranking accordingly. Due to space restrictions,
and to avoid information overload, we only show the top-10 courses. For each
social network user, and for each course that is suggested, Buddy Study displays
the common terms, thereby providing the reasons for the course suggestion.
If for a moment we assume a scenario where Linked Data is not provided by
the OU, then the function of Buddy Study could, in theory continue, by con-
suming information provided in an alternative form. However, this application
forms the prototype upon which for future work – explained in greater detail
within the conclusions of this paper – is to be based. Such advancements will
utilise concepts for study partner recommendation rather than merely terms,
the reasoning behind this extension is to alleviate the noisy form that terms
take. By leveraging concepts from collections of terms, recommendations would
be generated that are more accurate and better suited to the user in question.
Without Linked Data, this is not possible.
5.2 System Implementation
The application is live and available online at the previously cited URL. It is built
using PHP, and uses the Facebook PHP Software Development Kit (SDK)28 .
Authentication is provided via Facebook Connect,29 enabling access to Facebook
information via the Graph API. The ARC2 framework30 is implemented to query
the remote SPARQL endpoint containing The Open University’s Linked Data,
and parse the returned information accordingly.
5.3 Example and Screenshots
To ground the use of Buddy Study, Figure 3 shows an example screenshot from
the application when recommending study partners for Matthew Rowe – one
of the authors of this paper. At this rank position in the results, the possible
study mate is shown together with the courses that could be studied together.
The courses are hyperlinked to their resource within the OU Linked Open Data
service, and in the proceeding brackets the terms that correlate with the courses
are shown. In this instance the top-ranked course is identified by the common
terms ‘API’ and ‘Info’.
Fig. 3. Buddy Study showing the 7th ranked social network member
6 Linked OpenLearn
The Open University offers a set of free learning material through the OpenLearn
website.31 Such material cover various topics ranging from Arts32 , to Sciences
and Engineering.33 In addition to that, the OU has other learning resources pub-
lished in the form of Podcasts, along with courses offered at specific presentations
during the year. While all these resources are accessible online, connections are
28
https://github.com/facebook/php-sdk
29
http://developers.facebook.com/docs/authentication
30
http://arc.semsol.org
31
http://openlearn.open.ac.uk
32
OpenLearn unit example in Arts: http://data.open.ac.uk/page/openlearn/a216 1
33
A list of units and topics is available at: http://openlearn.open.ac.uk/course
not always explicitly available, making it hard for students to easily exploit all
the available resources. For example, while there exists a link between specific
Podcasts and related courses, such links do not exist between OpenLearn units
and Podcasts. This leaves it to the user to infer and find the appropriate and
relevant material to the topic of interest.
Linked OpenLearn34 is an application that enables exploring linked courses,
Podcasts and tags to OpenLearn units. It aims to facilitate the browsing ex-
perience for students, who can identify on the spot relevant material without
leaving the OpenLearn page. With this in place, students are able, for example,
to easily find a linked Podcast, and play it directly without having to go through
the Podcast website.
6.1 Consumed Data
Linked OpenLearn relies on The Open University’s Linked Data to achieve what
was previously considered very costly to do. Within large organizations, it’s very
common to have systems developed by different departments, creating a set of
disconnected data silos. This was the case of Podcasts and OpenLearn units at
the OU. While courses were initially linked to both Podcasts and OpenLearn in
their original repositories, it was practically hard to generate the links between
Podcasts and OpenLearn material. However, with the deployment of Linked
Data, such links are made possible through the use of coherent and common
URIs of represented entities.
To achieve our goals of generating relevant learning material, we make use
of the courses, Podcasts, and OpenLearn datasets in data.open.ac.uk. As a first
step, while the user is browsing an OpenLearn unit, the system identifies the
unique reference number of the unit from the URL. Then this unique num-
ber is used in the query passed to the OU Linked Data SPARQL endpoint
(http://data.open.ac.uk/query), to generate the list of related courses including
their titles and links to the study at the OU pages.
In the second step, another query is sent to retrieve the list of Podcasts related
to the courses fetched above. At this level we get the Podcasts’ titles, as well
as their corresponding downloadable media material (e.g., video or audio files),
which enable users to play the content directly within the application. Finally
the list of related tags are fetched, along with an embedded query that generates
the set of related OpenLearn units, displayed in a separate window. The user at
this level has the option to explore a new unit, and the corresponding related
entities will be updated accordingly. The application is still a prototype, and
there is surely room for further data to extract. For example, once the library
catalogue is made available, a much richer interface can be explored by students
with related books, recordings, computer files, etc.
34
http://fouad.zablith.org/apps/openlearnlinkeddata
6.2 System Implementation
We implemented the Linked OpenLearn application in PHP, and used the ARC2
library to query the OU Linked Data endpoint. To visualise the data on top of
the web page, we relied on the jQuery User Interface library,35 and used the
dialog windows for displaying the parsed SPARQL results. The application is
operational at present, and is launched through a Javascript bookmarklet, which
detects the OpenLearn unit that the user is currently browsing, and opens it in
a new iFrame, along with the linked entities visualised in the jQuery boxes.
6.3 Example and Screenshot
To install the application, the user has to drag the applications’ bookmarklet36
to the browser’s toolbar. Then, whenever viewing an OpenLearn unit, the user
clicks on the bookmarklet to have the related entities displayed on top of the unit
page. Figure 4 illustrates one arts related OpenLearn unit, with the connected
entities displayed on the right, and a running Podcast selected from the “Linked
Podcasts” window. The user has the option to click on the related course to
go directly to the course described in the Study at the OU webpage, or click
on linked tags to see the list of other related OpenLearn units, which can be
browsed within the same window.
Fig. 4. Linked OpenLearn Screenshot
35
http://www.jqueryui.com
36
The bookmarklet is available at: http://fouad.zablith.org/apps/openlearnlinkeddata,
and has been tested in Firefox, Safari and Google Chrome
7 Conclusions
In this section we report on our experiences when generating and exploiting LOD
within the context of an educational institution. Regarding our experience on
transforming information distributed in several OU repositories and exposing it
as LOD, the process complexity was mainly dependent on the datasets in terms
of type, structure and cleanliness. Initially, before any data transformation can
be done, it was required to decide on the vocabulary to use. This is where the
type of data to model plays a major role. With the goal to reuse, as much as
possible, already existing ontologies, it was challenging to find the adequate ones
for all our data. While some vocabularies are already available, for example to
represent courses, it required more effort to model OU specific terminologies
(e.g., at the qualifications level). To assure maximum interoperability, we chose
to use multiple terminologies (when available) to represent the same entities.
For example, courses are represented as modules from the AIISO ontology, and
at the same time as courses from the Courseware ontology. Other factors that
affected the transformation of the data are the structure and cleanliness of the
data sources. During the transformation process, we faced many cases where du-
plication, and information not abiding to the imposed data structure, hampered
the transformation stage. However, this initiated the need to generate the data
following well-defined patterns and standards, in order to get easily processable
data to add to the LOD.
Regarding our experiences exploiting the data, we have identified three main
advantages of relying on the LOD platform within the context of education.
Firstly the exposure of all these material as free Web resources have open oppor-
tunities for the development of novel and interesting applications like the three
presented in this paper. The second main advantage is the structure provided by
the data. This is apparent in the OU Expert Search system, where the different
properties of articles are exploited to generate different ranking criteria, which
when combined, provide much stronger support when finding the appropriate
expertise. Finally, the links generated across the different educational resources
have provided a new dimension to the way users can access, browse and use the
provided educational resources. A clear example of this is the exploitation of
LOD technology within the OpenLearn system, where OpenLearn units are now
linked to courses and Podcasts, allowing students to easily find in a single site,
all the information they are looking for.
We believe that universities need to evolve the way they expose knowledge,
share content and engage with learners. We see LOD as an exciting opportunity
that can be exploited within the education community, especially by interlinking
people and educational resources within and across institutions. This interlink-
ing of information will facilitate the learning and investigation process of stu-
dents and research staff, enhancing the global productivity and satisfaction of
the academic community. We hope that, in the near future, more researchers
and developers will embrace LOD approach, by creating new applications and
learning from previous experiences to expose more and more educational data
in a way that is directly linkable and reusable.
8 Future Work
The application of Linked Data within the OU has opened multiple research
paths. Regarding the production of Linked Data, in addition to transforming
the library records to LOD, the LUCERO team is currently working on con-
necting the OU’s Reading Experience Database (RED)37 to the Web of Data.
Such database aims to provide access and information about reading experiences
around the world. It helps the readership for books issued in new editions for
new audiences in different countries to be tracked. Its publication as LOD is an
interesting example about how the integration of Linked Data technology can
open new investigation paths to different research areas, in this case humanities.
Regarding the consumption of LOD, we envision, on the one hand, to en-
hance the three previously mentioned applications and, on the other hand to
generate new applications as soon as more information is available and intercon-
nected. As example of the former, for the Buddy Study application we plan to
extend the current approach for identifying common terms between social net-
work members and courses to instead utilise common concepts. At present the
use of online messages results in the inclusion of abbreviated and slang terms,
resulting in recommendations that are generated from noise. By instead using
concepts, we believe that the suggested courses would be more accurate and
suitable for studying. As an example of the latter, we aim to generate a search
application over the RED database, able to display search results on an interac-
tive map and link them not just to relevant records within the RED database,
but also with relevant objects of the LOD cloud.
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