=Paper=
{{Paper
|id=Vol-3250/messpaper3
|storemode=property
|title=Modeling Linked Open Data (Poster)
|pdfUrl=https://ceur-ws.org/Vol-3250/messpaper3.pdf
|volume=Vol-3250
|authors=Adiel Tuyishime, Javier Luis Cánovas Izquierdo,Maria Teresa Rossi,Martina De Sanctis
|dblpUrl=https://dblp.org/rec/conf/staf/TuyishimeIRS22
}}
==Modeling Linked Open Data (Poster)==
https://ceur-ws.org/Vol-3250/messpaper3.pdf
Modeling Linked Open Data (Poster)
Adiel Tuyishime1 , Javier Luis Cánovas Izquierdo2 , Maria Teresa Rossi1 and
Martina De Sanctis1
1
Gran Sasso Science Institute (GSSI), Viale Francesco Crispi 7, L’Aquila, 67100, Italy
2
IN3 – UOC, Barcelona, Spain
1. Introduction
Nowadays, many entities (e.g., business companies, government institutions) move
toward sharing their data online for reuse [1], leading to the incremental growing of
Open Data [2]. In public administrations, Open Data increases transparency [3] and
allows citizens access to valuable information. Usually, the information provided by
a unique dataset coming from Open Data sources are very limited as they are not
integrated with other data sources. We propose to leverage the concept of Linked
Open Data (LOD) [2] which, besides the benefits of Open Data, exploits Linked Data
best practices for publishing and connecting structured data on the Web [4]. Thus,
LOD supports knowledge sharing and information enrichment by adding links to both
properties and values of a data object. In the literature, traditional modeling approaches
exploit semantic Web features to model LOD. For instance, Alaoui et al. [5] propose data
modeling in the context of enterprise applications development in a semantic-oriented
perspective by using RDF and OWL ontologies. Meanwhile, Jamil et al. [6] present an
approach for the semantic modeling of events using the case study of refugee registration
and repatriation. Although ontologies offer powerful solutions, they are specialized
in conceptual modeling and inferring new knowledge. This does not facilitate the
development of various software artifacts (e.g., automatically generated code, APIs or
libraries). Moreover, using ontologies implies good knowledge of the domain and the
understanding of the used technologies, requiring a considerable effort and leading
to a steep learning curve. For this reason, we propose a novel approach to model
LOD based on Model-Driven Engineering (MDE) as it presents a wide range of tools
and techniques supporting not only conceptual modeling but also the development
and generation of different software artifacts, easy integration, and non-functional
requirements analysis. MDE has been successfully applied in different domains and
has proven to be a promising approach to follow due to the benefits it offers (e.g., code
generation or model transformation). In addition, MDE enables the linking between
models through the exploitation of weaving models. Thus, weaving models can be
exploited in the scenario of LOD to integrate different models (e.g., Open Data models),
while maintaining the separation of concerns and avoiding the construction of large and
monolithic models for LOD, which could be difficult to handle, maintain and reuse [7].
2. Modeling LOD exploiting MDE Techniques
In LOD, data elements are linked to each other in such a way that they can be effectively
navigated to provide additional context. In a smart city context, LOD together with
MeSS’22: International workshop on MDE for Smart IoT Systems, July 04–08, 2022, Nantes, France
$ adiel.tuyishime@gssi.it (A. Tuyishime); jcanovasi@uoc.edu (J. Cánovas Izquierdo);
mariateresa.rossi@gssi.it (M. T. Rossi); martina.desanctis@gssi.it (M. De Sanctis)
0000-0002-2326-1700 (J. Cánovas Izquierdo); 0000-0003-0273-7324 (M. T. Rossi); 0000-0002-9417-660X
(M. De Sanctis)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC
BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
data provided by IoT devices could contribute to the creation of new knowledge about
a city. For instance, multiple information are provided by IoT devices installed around a
smart city, such as air quality, traffic, noise, etc. In this context, anybody interested in
pollution, could not only see the information about pollution but could also navigate
towards other factors that can have an impact on pollution, such as road traffic, or that
can be impacted by it, such as health, among others. In Figure 1 we report an example
as an abstract representation of our proposal to model LOD exploiting MDE techniques.
On the left-side (a) we report an example of LOD in which we have three different
domains, namely Population, Mobility, and Pollution that could be expressed as data
objects defined by different classes and relationships. In the figure, we report at least
one class for each domain representing how the data is linked to each other to enable
information sharing. For instance, in the Pollution domain we have a class AirQuality
which is linked with a class Health in the Population domain with a relationship (has
impact on) that indicates the impact AirQuality has on Health. On the right-side (b) of the
figure, we report how the example can be modeled by using MDE techniques, by means
of the typical structure of data objects in the three domains through metamodeling. As
can be seen, we propose to use three models (i.e., Population Model, Pollution Model
and Mobility Model) which are conforming to their corresponding metamodels (i.e.,
Population metamodel, Pollution metamodel and Mobility metamodel). To establish
the link between these three models a Weaving Model is introduced. This way, we
integrate different modeling domains that contributes to the megamodelling by enabling
an ecosystem of models.
Figure 1: Example of modeling LOD exploiting MDE Techniques.
References
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linking decentralized data, International Journal of Web Engineering and Technology 6 (2011) 266–285.
[2] F. Bauer, M. Kaltenböck, Linked Open Data: The Essentials - A Quick Start Guide for Decision Makers,
edition mono/monochrom, Vienna, Austria, 2012. ISBN: 978-3-902796-05-9.
[3] P. P. M. J. Anneke Zuiderwijk, Mila Gascó, Special issue on transparency and open data policies: Guest
editors’introduction, volume 9, 2014.
[4] C. Bizer, T. Heath, T. Berners-Lee, Linked data - the story so far, Int. J. Semantic Web Inf. Syst. 5 (2009)
1–22.
[5] K. Alaoui, M. Bahaj, Semantic Oriented Data Modeling for Enterprise Application Engineering
Using Semantic Web Languages, International Journal of Advanced Trends in Computer Science and
Engineering 9 (2020).
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Intelligent Automation Soft Computing 29 (2021) 511–524. doi:10.32604/iasc.2021.017770.
[7] D. Di Ruscio, Specification of Model Transformation and Weaving in Model Driven Engineering,
Universitá di L’Aquila, PhD Thesis, 2007.