=Paper=
{{Paper
|id=None
|storemode=property
|title=DiscOU: A Flexible Discovery Engine for Open Educational Resources Using Semantic Indexing and Relationship Summaries
|pdfUrl=https://ceur-ws.org/Vol-914/paper_20.pdf
|volume=Vol-914
|dblpUrl=https://dblp.org/rec/conf/semweb/dAquinAC12
}}
==DiscOU: A Flexible Discovery Engine for Open Educational Resources Using Semantic Indexing and Relationship Summaries==
https://ceur-ws.org/Vol-914/paper_20.pdf
DiscOU: A Flexible Discovery Engine for Open
Educational Resources Using Semantic Indexing
and Relationship Summaries
Mathieu d’Aquin, Carlo Allocca and Trevor Collins
Knowledge Media Institute, The Open University, Milton Keynes, UK
{m.daquin, c.allocca, t.d.collins}@open.ac.uk
Abstract. We demonstrate the DiscOU engine implementing a resource
discovery approach where the textual components of open educational
resources are automatically annotated with relevant entities (using a
named entity recognition system), so that these rich annotations can
be searched by similarity, based on existing resources of interest.
1 Introduction / Motivation
There is a growing base of open educational content being made available online.
At the Open University, this currently includes 650 units of course material on
OpenLearn and 3,800 audio and video podcasts1 . With such content available,
discoverability of educational resources becomes a major challenge. The exposure
of the metadata for such resources as linked data (see data.open.ac.uk and [1]) is
expected to make these resources more directly addressable, together with their
general description and the subjects they are covering (see e.g. [2]). Accordingly,
linked data is increasingly being adopted in open and distance learning scenarios
where discoverability is a main requirement (see [3]). However, relying purely on
metadata requires either to stay at a high level of description of the content of
resources (through the general topics being covered) or to richly annotate these
resources with all the dimensions relevant to their content. Another common
approach is therefore to search by similarity based on existing resources of in-
terest (i.e. finding things that are “more like this”). This is however generally
limited to the comparison of the textual components of the resources, with obvi-
ous limitations, as similarity between texts does not necessarily reflect a useful
relationship between resources in a discovery scenario.
Here, we demonstrate an engine implementing a hybrid approach, where the
textual components of open educational resources are automatically annotated
with relevant entities (using a named entity recognition system), so that these
rich annotations can be searched by similarity. This allows us to discover re-
sources based on relationships that are not necessarily explicitly described in
their metadata, and to characterise semantically these relationships based on
shared entities. This also provides us with a more flexible workflow, compared
to typical recommendation engines, where the user can act upon the search for
resources, through customising the semantic annotations realised prior to simi-
larity search. We demonstrate a prototype application of the developed services
to discover open educational content from the Open University, based on the
content of programmes broadcasted by the BBC.
1
see http://podcast.open.ac.uk and http://openlearn.open.ac.uk
2 The DiscOU Approach
Figure 1 summarises the architecture of the DiscOU system, which describes the
workflow implemented in four RESTful services2 for 1- extracting semantic enti-
ties from an online resource, 2- indexing these entities, 3- searching by similarity
in the index and 4- summarising the relationships between resources.
Fig. 1. Overview of the architecture of the DiscOU system.
Semantically Indexing Online Resources. As described in the previous section,
the main idea behind DiscOU is to take a hybrid approach where resources
can be searched “by similarity” with another existing resource, but where the
comparison of resources is based on rich semantic annotations. To generate such
rich semantic annotations, we make use of a named entity recognition system,
namely DBpedia Spotlight [4]. Textual components are first extracted from the
resources to be indexed, based on their description on data.open.ac.uk (using the
metadata directly for the title and abstract, and links to the textual content, as
online documents for OpenLearn units and PDFs of transcripts for podcasts).
The online service provided by DBpedia Spotlight is then used to obtain a list of
DBpedia entities with confidence/relevance scores for each of these components.
To index these semantic descriptions, we use the Lucene open source search
engine library3 . Lucene is however designed to index documents and texts and
is based on term-occurrence measures for searching and ranking results (i.e.,
TF.IDF). The indexing of semantic annotations is therefore realised in such a
way that these mechanisms can be used to obtain relevant results when searching
on the basis of semantic entities rather than of text. This is achieved simply by
2
see http://discou.info
3
http://lucene.apache.org/core/
transforming the relevance score provided by DBpedia Spotlight into a number
of occurrences for the entity, therefore repeating the mention of an entity in
the index of a given resource depending on its relevance for the resource. In this
way, when searching based on semantic entities, Lucene should return in priority
resources for which these entities are highly relevant.
Searching by Similarity. Lucene provides the base technique to search by sim-
ilarity through a mechanism called “MoreLikeThis”. This mechanism takes as
input an indexed document and generates a query that is expected to return
other resources having similar indexes. We apply this mechanism through first
indexing the external resource used as starting point for the discovery process
(in the next section, we use BBC programme webpages) using the same process
as described above. Because of the way the index is constructed, resources are
returned that share a large part of their semantic annotation.
Summarising Relationships Between Resources. One major advantage of our
approach is that the similarity relationship between resources being discovered
and the original ‘query resource’ is characterised by the semantic entities shared
in their content. Depending on the richness of the considered content however,
such lists of shared entities can be too large to be useful summaries of this
relationship. To tackle this issue, we developed a mechanism to summarise lists
of DPpedia entities. It uses DBpedia links between entities in a list (using a
local index of DBpedia, optimised for this specific task) to generate a set of
connected graphs. Each of these graphs is expected to represent one major topic
of the resources being considered. We therefore select the one which contains the
most entities with the highest relevance and, within this graph, the entity that
appears to be the most connected and the most relevant.
3 Demonstrator: Finding Open University Content Based
on BBC programmes
We implemented a demonstrator using the above mechanisms for a scenario in
which a user, having found a BBC programme interesting, wants to obtain links
to open educational resources to learn more about the topics covered by the
programme (see Figure 2). The interface of the demonstrator is implemented in
Javascript, using a bookmarklet to trigger the search. In other terms, being on a
BBC programme page (in the example Figure 2, “The Secret Life of Chaos”4 ),
the user can click on the DiscOU bookmark to make appear the results of search-
ing for similar resources in Open University content.
This is realised by extracting textual content from the BBC programme page
(out of its RDF description on the BBC website), running the semantic indexing
service on this content and searching by similarity. The results show the titles and
descriptions of the retrieved resources (obtained using the SPARQL endpoint
of data.open.ac.uk) as well as the summary of the relationship between each
resource and the BBC programme (here, mostly that they are about Chaos
Theory and Economy). One interesting aspect of this demonstrator is that the
4
http://www.bbc.co.uk/programmes/b00pv1c3
Fig. 2. Default results obtained with the BBC programme “The Secret Life of Chaos”
(left) and interface to customise the semantic annotations for this programme (right).
user can customise the ‘query’ by changing the weights of the entities extracted as
semantic annotations for the BBC programme (see right part of Figure 2). Once
the weights are customised, the search is triggered again, showing results that
are related to the personalised semantic annotations of the BBC programme.
While the results obtained are not always relevant, the fact that some level
of explanation is provided together with the ability to refine the automatically
generated ‘query’ makes the issue of incorrect results less critical. It is worth
mentioning in particular that only a very small part of the system is specific to
BBC programmes. The engine is used by the demonstrator as a set of RESTful
services, with its functionalities being highly reusable in other scenarios.
References
1. d’Aquin, M.: Putting linked data to use in a large higher-education organisation.
In: Interacting with Linked Data workshop. (2012)
2. Heath, T., Singer, R., Shabir, N., Clarke, C., Leavesley, J.: Assembling and applying
an education graph based on learning resources in universities. In: Linked Learning
(LILE) Workshop. (2012)
3. d’Aquin, M.: Linked data for open and distance learning. Commonwealth of Learn-
ing report (2012)
4. Mendes, P., Jakob, M., Garcı́a-Silva, A., Bizer, C.: Dbpedia spotlight: shedding
light on the web of documents. In: International Conference on Semantic Systems.
(2011)