=Paper=
{{Paper
|id=Vol-3005/sample-7col
|storemode=property
|title=Advancing on the Linked Open University Context: a Cuban linked Open University
|pdfUrl=https://ceur-ws.org/Vol-3005/07paper.pdf
|volume=Vol-3005
|authors=Yoan Antonio López Rodríguez
}}
==Advancing on the Linked Open University Context: a Cuban linked Open University==
https://ceur-ws.org/Vol-3005/07paper.pdf
Proceedings of the Doctoral Consortium at ISWC 2021 - ISWC-DC 2021
Advancing on the Linked Open University
Context: a Cuban linked Open University
Yoan Antonio López Rodrı́guez,1[0000−0001−5615−375X]
University of Informatics Sciences, Cuba. yalopez@uci.cu
Abstract. Linked Open Universities apply Linked Data to publish in-
formation about universities. In 2010, the Open University in the UK
was launched as the first initiative to expose public information from
the university in an accessible, open, integrated, and Web-based format.
Since then, universities around the world have been joining that initia-
tive by deploying their own linked open data platforms. However, during
the publication of their data using the linked data principles, universities
face challenges such as the lack of a unified, well-accepted vocabulary;
the need of coping with the heterogeneity of datasets; the high cost to
host the existing SPARQL endpoints; the performance shortcomings in
federated queries over current SPARQL endpoints and the incomplete-
ness of datasets. The aim of this Ph.D. research proposal is to advance on
the Linked Open University context with a proposal for the University
of Informatics Sciences from Cuba addressing some of these challenges.
Keywords: Knowledge graph · linked data · linked open university ·
ontologies
1 Problem Statement
Linked Open Universities apply Linked Data to publish information about uni-
versities [3,5,4]. Traditionally, universities produce large amounts of data, much
of which should be publicly available [3,5]. In this context, sharing and reusing
knowledge is a challenge where Linked Open Data (LOD) can play an important
role[3,5]. In 2010, the Open University in the UK was launched as the first ini-
tiative to expose public information from the university in an accessible, open,
integrated, and Web-based format[3]. Since then, universities around the world
have been joining that initiative by deploying their own LOD platforms[6,8,12].
The process of generating linked datasets in universities consists of the fol-
lowing stages[10]: i) raw data collection, ii) defining the vocabulary model based
on reusing existing ontologies and extend them when it is needed, iii) extracting
and generating RDF datasets according to the defined vocabulary, iv) achieving
interlinking among datasets internally and externally, v) storing the outcome
datasets and exposing them via SPARQL endpoints, vi) exploiting datasets by
developing applications and services on top, and, vii) providing optimization
and quality. In this sense, during the process, institutions have faced several
issues. Some of those issues are the following (without the intention of being
exhaustive)[10,8,1,13,7]:
Copyright © 2021 for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0).
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Proceedings of the Doctoral Consortium at ISWC 2021 - ISWC-DC 2021
– Lack of a unified, well-accepted vocabulary that satisfies all universities’ re-
quirements. Vocabularies (or ontologies, more strictly), define the concepts
and relations (also referred to as “terms”) used to describe and represent an
area of concern. A standard set of vocabularies provides a unified access to
data consumers [5]. That is why former works about linked open universi-
ties agree with reusing existing vocabularies as much as possible. However,
despite the fact that there are a lot of vocabularies/ontologies to describe
entities in this context, there is no a standard vocabulary/ontology (or group
of them) that meets the requirements of all universities.
– The need of coping with the heterogeneity of datasets. Open university repos-
itories look like a sea full of wealth resources, and the problem is how we
can reach the needed resources easily. Description of dataset metadata is a
crucial step to cope with the heterogeneity of datasets.
– The high cost of the existing SPARQL endpoint interfaces. Due to the high
expressive power of SPARQL, the processing of many kinds of queries on
SPARQL endpoints is very expensive in terms of server CPU time and mem-
ory consumption. Sometimes that consumption is unpredictable and thus,
SPARQL endpoints suffer from frequent downtime.
– Performance shortcomings of federated queries over current SPARQL end-
points. A 2013 survey revealed that the majority of public SPARQL end-
points had an uptime of less than 95%. This unavailability increases in fed-
erated queries where we query many knowledge graphs.
– Incomplete data is a problem that could complicate data querying and re-
trieving if it is not considered.
As of the previous problem statement, the aim of this Ph.D. research pro-
posal is to advance on the linked open university context (LOU context) with
a proposal for the University of Informatics Sciences from Cuba, applying novel
techniques developed by the Semantic Web community and our lab to address
previous challenges. The importance of this problem statement is described as
follows in Section 2.
2 Importance
Universities use LOD platforms to publish and access data about staff profiles,
courses, scholarly publications, and open educational resources[3]. Implementing
LOD can be summarized as using the web both as a channel to access data and,
as a platform for the representation and integration of data. Having university
linked datasets facilitates effective reuse of data and increases the opportunities
to build more effective, integrated, and innovative applications based on these
datasets[6]. It is also a trend revealing the contributions and achievements of
the universities and making them visible on the Web as LOD as it is a means
to measure the university’s reputation and standing among other international
universities and institutions[10,9].
Advancing on the LOU context means resolving some existing issues with the
objective of improving current and future LOD platforms. Aiming at a unified
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Proceedings of the Doctoral Consortium at ISWC 2021 - ISWC-DC 2021
vocabulary (or group of them) entails to get better current semantic modeling
processes in order to select the better vocabularies/ontologies and extend them
if it is needed. Current knowledge globalization makes the standardized access
to resources further relevant in the LOU context than in any other one. Besides
the data itself, it is also significant to describe datasets in order to increase their
discoverability and interoperability on the web via associated metadata. Related
work about semantic modeling in the LOU context is described in Section 3.
On the other hand, RDF datasets access and query in the LOU context are
extremely important for both, internal and external clients. A SPARQL endpoint
is the common interface to triple live queryable and its shortcomings are quite
well known[13,7]. To cope with the shortcomings of SPARQL endpoints, the
Semantic Web Community has created some technologies such as Triple Pattern
Fragment [13]. To advance on the LOU context this proposal takes into account
these technologies along with common interfaces to query RDF triples. Related
work about dataset´s access and query in the LOU context is described in Section
3.
Regarding incomplete data, the open word assumption of the Semantic Web
assumes incomplete information by default, and thus, the possibility of finding
missing facts contributes to dataset optimization. Related work about knowledge
graph completion in the LOU context is described as follows in Section 3.
3 Related work
On the semantic modeling process, according to[3], the process to choose the
terms is based on the following process: i) identify the concept to be expressed;
ii) search for a widespread existing vocabulary to be used; iii) if found, use
it, otherwise iv) search for a less-known vocabulary to reuse; v) if not found,
create a new term. Moreover, according to [8] this process includes: i) listing
common data categories about university information; and ii) getting a summary
of useful vocabularies for describing university datasets. When it is necessary to
create new ontologies, methodologies such as Methontology can be taken into
consideration.
Despite there has been a recent trend towards using the same vocabularies, in
the beginning, each platform used its own vocabularies/ontologies. For instance,
despite the fact that a course is the same in all universities, we can find it
modeled both as a Teach: Course1 as well as an Aiiso: Course2 . With the aim
of getting closer to a unified model, our proposal aims to use the most popular
vocabularies/ontologies of the state of the art, and thus, we define the first
research question and hypothesis related to the semantic modeling process in
Section 4.
Among the dataset metadata vocabularies, some notable ones are VoID3
used by most university platforms to describe datasets[3,6,12], the DataCube
1
http://linkedscience.org/teach/ns#Course
2
http://purl.org/vocab/aiiso/schema#Course
3
https://www.w3.org/TR/void/
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Proceedings of the Doctoral Consortium at ISWC 2021 - ISWC-DC 2021
vocabulary4 to describe datasets of multidimensional data, and the DCAT5 , a
feasible way to standardize metadata for catalogs, datasets and data services.
In this research proposal, we define the second research question and hypothesis
related to dataset metadata vocabularies in Section 4.
In regard to datasets access and query, the SPARQL language6 is the W3C
standard to express declarative queries over collections of RDF triples. There
are three common interfaces to RDF triples: Data dumps, SPARQL endpoints
and Linked Data documents [13]. To cope with the shortcomings of SPARQL
endpoints, the Semantic Web Community has created some technologies, such as
Triple Pattern Fragments (TPFs), which divides the query processing between
clients and servers and allows to restrict the kinds of queries the client can send
to the server[13]. Linked Connections are an example of a customized query in-
terface for the consumption of open data in the Transportation area[2]. Linked
Connections implement HTTP content negotiation7 [1]. Both, Linked Connec-
tions and TPFs use vocabularies such as Hydra8 and Tree9 that contribute to
the automation of the client-server communication and facilitate the federated
queries[1]. On the LOU platforms, to the best of our knowledge, customized
query interfaces of triples have not been implemented yet, publication and con-
sumption are mostly solved only via SPARQL endpoints. We define the third
research question and hypothesis related to datasets access and query in Section
4.
Regarding incomplete data, given a Knowledge Graph G = (E, R, T ), where
E and R denote the set of entities and relations and T = (h, r, t)|h, tE, rR is
the set of triplets (facts), the task of Knowledge Graph Completion (KGC) in-
volves inferring missing facts based on the known facts[11]. Much research work
has been devoted to KGC. A common approach to carry out KGC has been
knowledge graph embeddings. Most knowledge graph embedding models only
use structure information in observed triple facts, nevertheless, advanced tech-
niques use other information besides facts such as entity types, relation paths,
textual descriptions, and logical rules [14]. To advance on the LOU context,
our proposal aims at defining a knowledge graph embedding model able to use
other information besides facts on the output university datasets. We define the
fourth research question and hypothesis related to knowledge graph completion
in Section 4.
4
https://www.w3.org/TR/vocab-data-cube/
5
http://www.w3.org/TR/vocab-dcat-2/
6
http://www.w3.org/tr/sparql11-query/
7
http://tools.ietf.org/html/rfc7231#section-5.3
8
http://www.hydra-cg.com/spec/latest/core/
9
https://treecg.github.io/specification/
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Proceedings of the Doctoral Consortium at ISWC 2021 - ISWC-DC 2021
4 Research questions and hypotheses
Regarding the lack of a unified, well-accepted vocabulary that satisfies all uni-
versities’ requirements, we define the first research question and hypothesis as
follow:
Q1: How to contribute to having a unified, well-accepted vocabulary in the
LOU context?
H1: If we use the most used vocabularies/ontologies or extend them in our
LOD platform, we contribute to having a unified, well-accepted vocabulary that
satisfies all universities’ requirements.
Regarding dataset metadata vocabularies, we consider that advancing on the
LOU context means to advance on the automation of the client-server commu-
nication, therefore we need a metadata vocabulary to allow client applications
to get resources easily. In this sense, we define the second research question and
hypothesis related to dataset metadata vocabularies as follows:
Q2: How to support client applications getting resources easily in the LOU
context?
H2: Using the DCAT vocabulary to describe dataset metadata, client appli-
cations are able to easily get the resources in the LOU context.
With regard to datasets access and query, to advance on the LOU context
means to incorporate novel approaches and technologies developed by the Se-
mantic Web Community along with common interfaces. Query interfaces cus-
tomized according to the client applications’ needs can contribute to the automa-
tion of the client-server communication. We define the third research question
and hypothesis related to datasets access and query as follows:
Q3: Can query interfaces customized according to the client applications’
needs along with common interfaces improve datasets access and query in the
LOU context?
H3: Query interfaces customized according to the client applications’ needs
along with common interfaces can improve the datasets access and query in the
LOU context.
Regarding the incompleteness of the datasets, we define the fourth research
question and hypothesis related to incomplete data in the LOU context as fol-
lows:
Q4: What kinds of information besides facts in the knowledge graph em-
bedding models produce an acceptable trade-off in the LOU context to predict
missing facts?
H4: Integrating textual descriptions and logical rules besides facts in the
knowledge graph embedding models produces an acceptable trade-off in the LOU
context to predict missing facts.
Considerations about how to test these previous hypotheses are described as
follows in Section 5.
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Proceedings of the Doctoral Consortium at ISWC 2021 - ISWC-DC 2021
5 Evaluation
With the aim of finding out the most popular vocabularies/ontologies in the
LOU context and testing the hypothesis H1, we carried out a state-of-the-art
study. As outcomes, we found out: i) AIISO ontology10 is applied in most works
while other vocabularies such as Teach11 and Courseware include crucial terms
about university courses; ii) most works followed the principle of reusing existing
ontologies as much as possible, however, all of them had to extend these vocab-
ularies to fulfill their requirements; iii) general ontologies such as Dublin Core
Metadata Element Set, FOAF, W3C Basic Geo Vocabulary, and W3C Ontology
for Media Resources had a common usage along with the above vocabularies.
Unlike existing LOD university platforms that have been using the VoID
vocabulary to describe their datasets, in our proposal, the DCAT vocabulary is
promoted for advancing in the LOU context because of its broader coverage than
other vocabularies like VoID. The explanation of the DCAT vocabulary usage
(H2 evaluation) is shown below along with the customized query interface.
In order to evaluate the hypothesis H3, since all the state-of-the-art LOU
platforms include a course dataset, we developed a customized query interface for
the course dataset called coursesld server12 . We do not aim at replacing common
triple interfaces, but using our proposal as a complementary query interface.
University course data are a special segment of open data at the university given
the public nature of the data[12,10]. With the increase of distance learning, there
are a lot of course recommender applications that assist clients to find suitable
courses.
Our LOD university platform publishes on its homepage the DCAT-catalog
file where course recommender applications can find datasets about courses.
By accessing the dataset subject property, client applications can distinguish
one dataset among many other ones (as in this case, teach: Course is the wanted
subject). Additionally, by getting to the dataset distribution, clients know where
to retrieve the dataset (URL via distribution accessURL property) and how to
retrieve it (file format via distribution mediaType property, e.g., in this case, one
of the four options: JSON-LD, Turtle, N-quads, CSV). In order to support the
automation of the client-server communication a Hydra API Documentation was
also defined, which can be obtained via catalog-dataset-conformsTo property.
Normally, course recommender applications ask for courses about one specific
topic that start on a certain date. That is why, coursesld server interface offers
the possibility of getting courses related to that topic sorted by the start date
via URL templates. To deal with memory requirements and advancing on the
data reuse, the interface splits the answer into fragments with no more than
100 courses per fragment. Course recommender applications are able to request
the server each fragment through the Tree/Hydra vocabulary without human
10
http://purl.org/vocab/aiiso/schema#
11
http://linkedscience.org/teach/ns#
12
https://github.com/yalopez84/coursesld server
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Proceedings of the Doctoral Consortium at ISWC 2021 - ISWC-DC 2021
intervention. To complete the hypothesis H3 evaluation, a client application was
developed called coursesld client13 .
In regard to the evaluation of the graph completion model (H4 evaluation), a
controlled experiment will be carried out. The tests will be first on at least two
general knowledge graphs: DBpedia and Freebase, and then, on specific LOU
datasets. Currently, the model is being developed.
6 Results
Besides the vocabularies/ontologies found in the state-of-the-art study, we put
into consideration our semantic modeling process, which has benefited from pre-
vious works[3,8], and promotes the usage of the Linked Open Vocabularies Ini-
tiative (LOV14 ) to look for terms/vocabularies. Regarding H1 evaluation, we
consider that it is true. If we look at the semantic model of our platform, we
see AIISO vocabulary to represent the university structure and Tech vocabulary
to describe the university course according to the tendency in the state of the
art. Each time a platform is developed following this principle, such a university
contributes to having a unified vocabulary or group of vocabularies in the LOU
context.
Regarding the coursesld server interface (H3 evaluation), its effectiveness is
appreciated as an additional service for the exploitation of linked datasets. Since
most developers are familiar with APIs with descriptions more than SPARQL
endpoints over the RDF format, customized interfaces like the one presented
in this work can serve to pave that issue. At the same time, we confirm the
hypothesis H2 as the DCAT vocabulary allows to advance on the customized
interfaces, federated queries, automation of client-server communication, and
therefore the DCAT vocabulary allows to advance in the LOU context.
7 Reflection and future work
Future work aims at continuing with this research. Some of the previous hy-
potheses need further evaluation in order to reach the goal of this proposal: to
advance on the LOU context. Given the wide range of topics at the university
(research, academic programs, software production, internal places, energy and
water consumption, etc.), adding new datasets to the LOD platform allows us
to have a more complete knowledge graph. Afterward, we plan to extend the
solution to other universities and evaluate the research contributions beyond the
LOU context.
Acknowledgments
This research has been partially sponsored by VLIR-UOS Network University
Cooperation Programme - Cuba. Special thanks go to:
13
https://github.com/yalopez84/coursesld client
14
https://lov.linkeddata.es/dataset/lov/terms
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Proceedings of the Doctoral Consortium at ISWC 2021 - ISWC-DC 2021
– Erik Mannens. Ghent University. Research interests: Data Science. He re-
ceived his Ph.D. degree in Computer Science Engineering (2011) at UGent.
– Pieter Colpaert Ghent University. Research interests: Linked Open Data,
Transport. He received his Ph.D. degree in Computer Science Engineering
(2017) at UGent.
– Hector R. González. University of Informatics Science. Research interests:
Machine Learning. He received his Ph.D. degree in Computer Science (2019)
at University of Havana.
– Yusniel Hidalgo Delgado. University of Informatics Science. Research inter-
ests: Semantic Web.
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