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|authors=Lucía Sánchez-González,Ana Iglesias-Molina,Oscar Corcho,María Poveda-Villalón,Benedikt T. Arnold,Johannes Theissen-Lipp,Diego Collarana,Christoph Lange,Sandra Geisler,Edward Curry,Stefan Decker,Inès Akaichi,Wout Slabbinck,Julián Andrés Rojas,Casper Van Gheluwe,Gabriele Bozzi,Pieter Colpaert,Ruben Verborgh,Sabrina Kirrane,Ieben Smessaert,Patrick Hochstenbach,Ben De Meester,Ruben Taelman,Ruben Verborgh,Rohit A. Deshmukh,Diego Collarana,Joshua Gelhaar,Johannes Theissen-Lipp,Christoph Lange,Benedikt T. Arnold,Edward Curry,Stefan Decker,Maximilian Stäbler,Paul Moosmann,Patrick Dittmer,DanDan Wang,Frank Köster,Christoph Lange
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==On the Governance of Semantic Artefacts in Dataspaces==
https://ceur-ws.org/Vol-3705/short08.pdf
On the Governance of Semantic Artefacts in
Dataspaces
Lucía Sánchez-González1 , Ana Iglesias-Molina1 , Oscar Corcho1 and
María Poveda-Villalón1
1
Ontology Engineering Group, Universidad Politécnica de Madrid, Madrid, Spain
Abstract
There is widespread agreement that the use of Semantic Artefacts (SA) (vocabularies, ontologies, etc.) in
dataspaces is key for ensuring the interoperability of data, hence facilitating its integration. However, the
different methodologies and ad-hoc practices for developing SA may incur in producing resources that
do not meet the dataspace requirements, or that are not interoperable with the rest of the environment.
The introduction of SA in dataspaces opens the door for SA governance to harmonize the efforts and
resources within. This paper provides an overview of governance models for SA, along with the dataspace
initiatives that address this concept, from which we extract the challenges to face for the implementation
of SA governance in dataspaces.
Keywords
Dataspaces, Semantic Artefacts, Data Governance, Semantic Artefacts Governance
1. Introduction
Dataspaces were first conceived almost two decades ago as a data co-existence approach [1].
Most of the relevant initiatives have arisen more recently, such as the International Data Spaces
Association (IDSA)1 ; the Gaia-X European Association for Data and Cloud 2 ; or the Data Spaces
Business Alliance (DSBA)3 , formed by the Big Data Value Association4 , FIWARE Foundation5 ,
Gaia-X and IDSA. Indeed, dataspaces have had such an impact that even the European Union is
allocating significant resources for their implementation at European level [2].
The need for data harmonization and interoperability promotion between dataspace compo-
nents has increasingly fostered the use of Semantic Artefacts (SA). The term Semantic Artefact
refers to ontologies, terminologies, taxonomies, thesauri, vocabularies, metadata schemas, and
other standards [3]. The European Commission itself emphasizes the importance in dataspaces
The Second International Workshop on Semantics in Dataspaces, co-located with the Extended Semantic Web Conference,
May 26 – 27, 2024, Hersonissos, Greece
Envelope-Open lu.sanchez@upm.es (L. Sánchez-González); ana.iglesiasm@upm.es (A. Iglesias-Molina); oscar.corcho@upm.es
(O. Corcho); m.poveda@upm.es (M. Poveda-Villalón)
Orcid 0000-0002-1685-4700 (L. Sánchez-González); 0000-0001-5375-8024 (A. Iglesias-Molina); 0000-0002-9260-0753
(O. Corcho); 0000-0003-3587-0367 (M. Poveda-Villalón)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings CEUR Workshop Proceedings (CEUR-WS.org)
http://ceur-ws.org
ISSN 1613-0073
1
https://internationaldataspaces.org/
2
https://gaia-x.eu/
3
https://data-spaces-business-alliance.eu/
4
https://bdva.eu/
5
https://www.fiware.org/
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
of domain-specific vocabularies and ontologies for the integration of data from heterogeneous
data sources [2]. The IDS Information Model [4] is a clear example of how SA can achieve
successful interoperability between dataspace components and roles.
As a result, the volume of SA available in dataspaces is growing rapidly. However, ontolo-
gies and vocabularies are most commonly developed following ad-hoc practices, which often
result in resources that are not necessarily interoperable, reusable, and may even be obsolete,
hindering their consumption and exploitation. This is where the concept of governance comes
in. Governance can be defined as the set of political, institutional and administrative principles,
rules, practices and processes through which and how decisions are taken and implemented [5].
While data governance has been defined as one of the essential elements of a dataspace [6], the
concept of Semantic Artefact Governance (SAG) has been barely mentioned when it comes to
dataspace components.
Given the importance of SA and the role of SAG for their correct development and manage-
ment within dataspaces, this article reviews state of the art SAG frameworks and initiatives of
dataspaces that address the concept of SAG. From this analysis, we identify the main challenges
for implementing SAG in dataspaces, in order to promote governance to get the most out of
dataspaces empowered with SA. The remainder of this article is structured as follows: We first
present current proposals for SAG in Section 2. Then, we proceed to describe the dataspaces
that implement or mention SAG in Section 3. Next, we identify the challenges of implementing
SAG in dataspaces in Section 4. Finally, we draw the conclusions and future steps in Section 5.
2. Frameworks for Semantic Artefacts Governance
A search among major communities that develop, use and publish SA led to the identification of
six relevant governance frameworks. We consider as SAG frameworks the initiatives that fulfill
the following criteria: i) They must be focused on a network of SA, ii) there must be a community
supporting them, and iii) they must define a set of guidelines (or principles, requirements, etc.)
whose objective is to harmonize the development and publication of SA.
We provide a brief description of the identified frameworks, along with a comparison between
them based on a series of features.
OBO Foundry [7]. The Open Biomedical Ontologies (OBO) consortium launched this initiative
aiming for governing the increasing heterogeneity of ontologies in the biological domain. It
comprises a set of detailed principles6 that indicate how to develop ontologies, each one including
recommendations, requirements and implementation guidelines. This proposal focuses on
developing a family of interoperable ontologies within their community.
IOF [8]. The Industrial Ontology Foundry (IOF) revolves around creating interoperable ontolo-
gies for the digital manufacturing industry. It proposes, similarly to OBO, a set of governing
principles7 , along with standards, tools and training materials (on purchase).
SAREF Publication Framework [9]. The European Telecommunications Standards Institute
6
https://obofoundry.org/principles/fp-000-summary.html
7
https://oagi.org/pages/technical-principles
Table 1
Summary of features of governance frameworks: principles (F1), guidelines, best practices of method-
ologies (F2), standards or requirements (F3), roles and responsibilities (F4), tutorials or training support
(F5), quality assurance methods (F6), tooling support (F7) and scope (F8).
F1 F2 F3 F4 F5 F6 F7 F8
OBO 3 3 3 5 3 3 3 Biology
IOF 3 3 3 5 3 5 3 Manufacturing
SAREF 3 3 3 3 5 5 3 IoT
GOMO 3 3 3 3 3 3 5 Multidomain
CROP 5 3 3 3 5 3 3 Crops
IVOA 5 3 3 5 5 5 3 Astronomy
(ETSI)8 released a framework applicable for the SAREF9 suite of ontologies [10], which provide
the means for semantic interoperability in the Internet of Things (IoT) sector. This framework
includes generic principles10 , actors, use cases, technical requirements for each step of the
ontology’s life cycle, and a set of best practices regarding naming conventions, metadata and
ontology reuse.
GOMO [11]. The chemicals company BASF started a transition towards adopting semantic tech-
nologies to improve internal data management and exploitation. The Governance Operational
Model for Ontologies (GOMO) was developed to establish how ontologies should be created
and maintained within the company. This framework defines a set of governing principles,
standards, best practices, and training materials.
CROP Ontology Governance and Stewardship Framework [12]. This framework was
proposed by CGIAR (Consultative Group for International Agricultural Research) for the CROP
Ontology Project, which collects crop-related ontologies [13]. It primarily focuses on defining
the roles and corresponding responsibilities of the actors involved in the ontology design,
development and maintenance. It also provides a set of additional guidelines [14] for data
annotation, and quality assurance methods.
IVOA [15]. The International Virtual Observatory Alliance (IVOA) released a recommendation
for the Virtual Observatory vocabularies to enhance the correct functioning and interoperability
within the astronomical community. It provides guidelines for vocabulary content, metadata,
management and publication, along with compliant tooling for enabling interoperability.
Table 1 summarizes the similarities and differences of the governance frameworks presented.
We characterize them according to a set of features, which are described below. These fea-
tures are extracted from the most common and shared elements that the analyzed governance
frameworks define.
F1 – Principles. Set of fundamental rules based on the scope of the community that develops
the SA. They establish the basis for the design of the rest of the components. For example,
the OBO Principle 1 states that “The ontology MUST be openly available”11 .
8
https://www.etsi.org/
9
https://saref.etsi.org/
10
https://saref.etsi.org/principles.html
11
https://obofoundry.org/principles/fp-001-open.html
F2 – Guidelines, Best Practices or Methodologies. Group of recommended procedures,
processes and activities that explain how to develop the resource based on the prin-
ciples and requirements established in the first place (e.g. Linked Open Terms (LOT)
methodology [16]).
F3 – Standards or Requirements. Formal specifications and conditions created to satisfy
the needs of the organization. For example, the SAREF Publication Framework in Section
8.2.3 Requirements for usability and referencing requires that “Each ontology module
version should be available at least in Turtle, RDF/XML, and HTML formats”[9].
F4 – Roles and Responsibilities. List of actors involved in each of the phases of the SA life
cycle and their corresponding activities (e.g., ontology engineer, domain expert, ontology
maintainer).
F5 – Tutorials or Training support. Series of dissemination and teaching activities about
the SAG framework. For instance, GOMO included as a fundamental pillar the orga-
nization of training workshop sessions to teach practitioners within BASF to develop
ontologies according to the governance framework [11].
F6 – Quality Assurance Methods. Set of methods, metrics and procedures that allow
evaluating the correct implementation of the standards, requirements or best practices.
For example, the CROP Ontology Governance Framework provides a quality assessment
workflow for ontologies that are candidates to be submitted to the network, to ensure
that they follow the required guidelines.
F7 – Tooling Support. Specification of a series of tools to use for SA development and
management. For instance, OBO Foundry has developed the ROBOT tool, which supports
automation of ontology development tasks, focusing on OBO conventions [17].
F8 – Scope. Refers to the domain area of the SA. For example, the SAREF SAG refers to
ontologies that model knowledge in the area of the Internet of Things.
3. Initiatives for the Governance of Semantic Artefacts in
Dataspaces
In order to determine the status of the use of SAG in dataspaces, a search was carried out in
official documents and technical reports on dataspace design and implementation initiatives.
We consider those initiatives that explicitly mention SAG, or those that indicate the use of guide-
lines, tools or resources for SA management. This analysis of the main activities in dataspaces
led us to the identification of two main initiatives.
IDS-Vocabulary Hub and Vocabulary Provider. In the IDS Reference Architecture Model
(IDS-RAM12 ), the IDSA refers to two main elements related to SA governance: (i) The Vocabulary
Hub, and (ii) the Vocabulary-related roles. The Vocabulary Hub serves as a platform to store,
maintain, and publish shared vocabularies and related schema documents. In addition, this
platform is thought to provide dataspace users with a series of tools that allow the creation,
improvement, and publication of the terms of the vocabularies. However, it is specified that,
12
https://docs.internationaldataspaces.org/ids-knowledgebase/v/ids-ram-4/
while it is required that these vocabularies employ RDF, it is not enforced the use of formal
ontologies. Regarding Vocabulary-related roles, the IDS-RAM also defines a set of roles related
to the management of vocabularies. For instance, in the documentation they mention roles
such as Vocabulary creator, Vocabulary owner, Vocabulary publisher, Vocabulary consumer or
Vocabulary user.
EOSC Interoperability Framework and the European dataspaces. EOSC (European
Open Science Cloud)13 has been defined as a key element to develop a science, research and
innovation dataspace, and as a support for the implementation of the rest of sector specific
dataspaces in Europe [18]. Among its multiple initiatives, the EOSC Interoperability Task Force
published in 2021 the guidelines and principles that should drive the development of the EOSC
Interoperability Framework [19]. Specifically, the document remarks the need for a semantic
interoperability layer, where they address the use of SA to homogenize the interpretation
and treatment of the exchanged data and all of its associated resources. They point out the
lack of common and well-documented SA between communities, which is also affected by
the absence of common and reference repositories. As a solution, they highlight the need for
principles-based approaches and tools for the creation, maintenance, governance and use of SA.
In addition, it is stated that EOSC should provide support for the maintenance of a repository
of these SA, and a governance framework for such a repository. The establishment of these
SAG initiatives by EOSC will be key to later being able to implement them in the SA used in
the European dataspaces.
4. Challenges
The analysis carried out in Section 2 and Section 3, together with our expertise in dataspaces, SA
and governance led us to identify the following challenges for implementing SAG in dataspaces.
Challenge I – Adoption of SAG by dataspaces. SA are gaining importance in dataspaces,
as they have proven beneficial for enhancing interoperability and heterogeneous data integra-
tion[20]. As a result, they have already been incorporated in several initiatives [4], and more
initiatives will follow in the near future [20]. While data governance in dataspaces is always
considered, only a few initiatives mention how to govern their SA. Their proposed strategies to
manage SA are too limited in comparison with SAG approaches outside dataspaces. Therefore,
it is necessary to increase efforts to raise awareness among the actors involved in dataspaces
about the fundamental role of the governance of these resources.
Challenge II – Generalization of SAG. Current SAG frameworks were designed to harmonize
the ontology development efforts within a certain scope. In other words, they are limited to a
specific domain of knowledge (e.g. OBO to biomedical ontologies) or purpose (e.g. GOMO for SA
within BASF), providing ad-hoc practices for their needs (e.g. using OBO vs OWL2 for ontology
implementation) that are difficult to map to other SAGs. In addition, all frameworks fail to
provide all features identified in Section 2 and there is no agreement on the terminology used
for each of their elements (i.e, one framework may use principle while another one may employ
good practices). Lastly, to the best of our knowledge, none of them includes the governance
13
https://eosc.eu/
of other semantic resources (e.g. queries, validation shapes, mappings). Hence, there is no
holistic SAG framework that can be instantiated for each scenario and that includes all semantic
resources that may play a role in dataspaces.
Challenge III – Coordination and limits between SAG and dataspace governance. Within
dataspaces, specific governance frameworks are being designed [21]. The inclusion of SAG
within these frameworks may be different from how current SAG frameworks are designed,
specifically regarding the following aspects: (i) how the SAG framework overlaps with the
dataspace governance framework; (ii) whether the SAG can be applied at dataspace level or
stakeholder/users level, or both; and (iii) which specific parts of a SAG play a more relevant role,
e.g. the key elements for interoperability and maintainability. The answer to these questions
vary depending on the combination of dataspace and SAG framework.
5. Conclusions and Future Steps
With this work, we look into how SA are governed and their relevance for dataspaces. To
this end, we perform a two-fold analysis. On the one hand, we identify and compare different
SAG frameworks, with the aim of understanding what comprises a SAG framework; and
assessing the implications of SAGs in real-world scenarios. From this analysis we observe that
(i) SAGs are mostly domain- and/or community-specific, and (ii) there is no agreement regarding
which elements should compose a SAG framework. On the other hand, we investigate which
dataspace initiatives address the concept of SAG. Only two initiatives are identified, despite
how emphasized and extended the use of SA in dataspaces is. Between them, only one actually
defines some of the elements found in SAGs, while the other only mentions the relevance of
implementing them in dataspaces.
The results of this analysis arise the following challenges, which define the future lines of
research. First, a greater dissemination of the SAG concept is necessary among the dataspace
community. This heavily relies on the second challenge: so far, there is no standard or refer-
ence SAG framework that allows for a clear identification and definition of the elements or
features that a SAG framework should have. Among the initiatives that we analyze, we find
discrepancies in the definition of each element, making their alignment difficult, thus hindering
their implementation in dataspaces. Therefore, there is a need for identifying the different
governance need scenarios for the development and maintenance of SA in dataspaces. Based on
these scenarios, the elements that comprise the SAG frameworks to be used can be designed and
adapted accordingly. This will open the door for addressing the last issue, on how to escalate
and coordinate SAGs with dataspace governance.
In future steps, we plan to perform a more fine-grained analysis of the SAG models applied
to dataspaces to elucidate the design of an abstract model suitable for different application
scenarios. We will base the design and subsequent evaluation in close collaboration with
dataspace initiatives, such as the Public Procurement Data Space (PPDS)14 , the Urban Data
Space for the Green Deal (USAGE)15 , and INESData16 .
14
https://europa.eu/!qx9WxQ
15
https://www.usage-project.eu/
16
https://inesdata-project.eu/content/en/index.html
Acknowledgments
Lucía Sánchez González is funded by the EU project USAGE - Urban Data Space for Green Deal
(https://www.usage-project.eu/) which has received funding from the European Union’s Horizon
Europe Framework Programme for Research and Innovation under the Grant Agreement no
101059950 - call HORIZONCL6-2021-GOVERNANCE-01-17 (IA).
María Poveda-Villalón is funded by the European Union’s Horizon 2020 research and innova-
tion programme under the grant agreement no. 101016854 (AURORAL).
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