Towards Semantic Interoperability Standards
based on Ontologies
Semantic Interoperability White Paper
• Martin Bauer, NEC Laboratories Europe
• Hamza Baqa, Easy Global Market
• Martin Bauer, NEC Laboratories Europe
• Sonia Bilbao, Tecnalia Corporación Tecnológica
• Aitor Corchero, Eurecat - Technology Centre of Catalonia
• Laura Daniele, TNO
• Iker Esnaola, IK4-TEKNIKER
• Izaskun Fernández, IK4-TEKNIKER
• Östen Frånberg,1A Konsult
• Raúl García-Castro, Universidad Politécnica de Madrid
• Marc Girod-Genet, Institut Mines-Télécom
• Patrick Guillemin, ETSI
• Amélie Gyrard, Kno.e.sis, Wright State University
• Charbel El Kaed, Google
• Antonio Kung, TRIALOG
• Jaeho Lee, University of Seoul
• Maxime Lefrançois, École des Mines de Saint-Étienne
• Wenbin Li, Orange
• Dave Raggett, W3C
• Michelle Wetterwald, NETELLANY / FBConsulting
1This document is available under the Creative Commons Attribution 4.0
22-Oct-2019 Towards Semantic Interoperability Standards based on Ontologies 1
Table of Contents
1 Introduction 3
2 Semantic interoperability 3
2.1 Ontology-driven interoperability 4
2.2 Benefits of semantic interoperability 5
3 Industry requirements for semantic interoperability practice 5
3.1 Co-creation and separation of concerns 5
3.2 Defining the knowledge perimeter needed for a specification 6
3.3 Modularization design principle 7
3.4 Evaluation of a specification 8
3.5 Deployment concerns 9
4 Initiatives for structured ontologies supported by standardization 11
4.1 Initiatives on ontologies supported by standardization 11
4.2 System viewpoint of ontologies 12
5 Life cycles for ontology-driven interoperability 13
5.1 Interoperability-by-design 13
5.1.1 Introduction to system life cycles 13
5.1.2 Definition of interoperability-by-design 14
5.1.3 Interoperability activities system lifecycle 15
5.1.4 Interoperability specification lifecycle 15
5.2 Ontology-driven semantic Interoperability 16
5.2.1 Life cycles involved 16
5.2.2 Example for benefits of ontology-driven semantic interoperability 17
5.2.3 Ontology engineering 18
5.2.4 Ontology validation methodsSemantic-based 20
5.2.5 Ontology-driven semantic interoperability lifecycle 21
6 Recommendations for ontology-driven semantic interoperability standards 23
2 Towards Semantic Interoperability Standards based on Ontologies 22-Oct-2019
This paper is co-authored by an informal group of experts from a broad range of
backgrounds, all of whom are active in standards groups, consortia, alliances and/or
research projects in the Internet of Things (IoT) space.
This paper has two objectives: 1) explain the need for semantic interoperability, 2)
provide recommendations for semantic interoperability standards using ontologies.
The target audience for this paper are:
● IoT system product owners who need to understand how they can effectively
ensure interoperability of their products.
● IoT system and standardization engineers without background in semantic
The document is made available under the Creative Commons Attribution 4.0
The paper is structured as follows: Section 2 introduces semantic interoperability and its
benefits; Section 3 provides industry requirements for semantic interoperability practice;
Section 4 describes various initiatives for ontology-driven interoperability; Section 5
explains the various life cycles for ontology-driven interoperability; and finally, Section 6
provides recommendations on ontology-based semantic interoperability.
2 Semantic interoperability
Interoperability specification describes how two systems or components can engage into
a working interaction e.g. two IoT devices. Semantic interoperability focuses on
describing the semantics of such interaction.
A semantic interoperability process might focus on various description viewpoints (as
shown in Figure 1: 1) information exchanged, 2) interactions, and 3) others
Figure 1. Semantic interoperability
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For instance, the interoperability specification of the protocol between two IoT devices
connected through a network may include:
● A semantic description to describe the device capabilities such as measuring
temperature (other semantic description).
● A semantic description to describe the protocols such as wifi (or interactions)
● A semantic description to describe protocol data units such as celsius data unit
(or information exchanged)..
2.1 Ontology-driven interoperability
Ontology-driven interoperability aims to produce the semantic descriptions in Figure 1.
An ontology describes concepts and relationships between concepts in a specific
domain. For instance, in the case of a description of information exchanged, ontologies
describe the concepts contained in the information exchanged as well as the
relationship links between those concepts.
An ontology can be created using computer description languages such as RDF
(Resource Description Framework), RDFS (Resource Description Framework) Schema)
or OWL (Ontology Web Language). Languages can be serialized in several formats
such as XML (eXtensible Markup Language). The semantic web stack classifies
languages such as RDF, RDFS, and OWL (as shown in Figure 2).
Figure 2. Semantic Web Cake 
2 Figure under CC0 license:
4 Towards Semantic Interoperability Standards based on Ontologies 22-Oct-2019
2.2 Benefits of semantic interoperability
Applying semantic interoperability in the industry has several benefits:
●The quality of an interoperability specification is improved as a systematic
process is applied for defining interoperability.
●The resulting specifications can be used as a reference when interpretation
problems have to be solved. Instead of a textual specification, a formalised
specification (e.g. ontologies) is available. It can be used by further tools for
verification and validation.
●Maintenance and extension of the specification is more straightforward. While it
is difficult to assess the impact of a modification in a textual specification of
interoperability, it is easier to do so in a formalised specification.
3 Industry requirements for semantic interoperability
Producing a semantic interoperability specification that can be widely used in a market
requires a specification practice that takes into account the following requirements: 1)
co-creation and separation of concerns; 2) definition of the knowledge perimeter needed
for a specification; 3) modular design principle following design pattern approaches; 4)
evaluation of a specification, and 5) support of industry deployment concerns.
3.1 Co-creation and separation of concerns
Co-creation is a design approach that brings experts with different expertise and
viewpoints together, for instance, a domain expert and a technology expert), in order to
jointly produce a mutually valued outcome.
Separation of Concerns (SoC) is a design principle for separating an item to design
into distinct elements, so that each element addresses a separate concern (Table 1
provides an example).
The practice of semantic interoperability, i.e. the creation of an interoperability
specification requires two kinds of expertise: 1) domain experts bring knowledge on
domain engineering, and 2) semantic interoperability experts bring knowledge on
ontology engineering. Depending on the domain, other categories of experts are
relevant such as security and privacy experts, or user-centric design experts, e.g. the
eHealth vertical where systems have to be designed both taking into account
security/privacy/trust (by design) and in co-conception with the patients, caregivers and
the helpers (relatives). It is important to achieve a clear separation of concerns between
domain experts and semantic interoperability experts. Without this separation of
concerns, one can easily fall into a trap where a domain expert has to rely on a semantic
interoperability expert to propose a specification, and where interoperability decisions
are taken by the wrong expert (e.g. the domain expert changes the ontology). From a
22-Oct-2019 Towards Semantic Interoperability Standards based on Ontologies 5
method and tools viewpoint, recommendations must be provided to enable separation of
concerns. For instance, domain experts inspect and update a specification using a
domain viewpoint, while the semantic interoperability expert’s focus is on inspecting and
updating a specification using ontology engineering.
Table 1. Separation of concerns SAREF example
Example of practice
An example of good separation of concerns is to organize
co-creation sessions when both categories are present to
make design decisions. This was achieved by the SAREF
team when they organized a session for the European Large
Scale Pilots during the IoT week in Bilbao in June 2018 to get
input from domain experts that they could use to specify an
ontology to model different domains (e.g., smart home,
agriculture, energy) as depicted in Figure 7.
3.2 Defining the knowledge perimeter needed for a
It is important to clearly define the knowledge that is needed for a semantic
interoperability specification. We call this the knowledge perimeter.
If the selected knowledge perimeter is too broad, then many concepts that are defined in
the ontology might not be used. Worse, it could be counter effective. Moreover, when
cross domain ontologies are used, it is important to select the subset of concepts and
properties rather than the entire domain ontology.
If the selected knowledge perimeter is too small then needed concepts in the
specification would be missing, which could result in an incomplete semantic
Table 2. Example of practice for specification scope
Example of practice
An interoperability specification is defined to enable cross
domain interoperability. For instance, interoperability is
needed between an energy management system and an
electric vehicle charging system. The resulting ontology
covers a common subset of the energy, mobility domain, and
the vehicle charging system.
6 Towards Semantic Interoperability Standards based on Ontologies 22-Oct-2019
3.3 Modularization design principle
The modularization principle concerns the structuring of a wide concept into multiple and
simpler sub-concepts that can be detailed independently . These sub-concepts can
therefore be described by self-contained knowledge sub-ontologies (modules) that are:
● Loosely coupled among themselves and can be designed, used and maintained
in a stand-alone way, as well as processed with far less processing power
requirements than complex ones. This is in particular mandatory for handling
both: use cases involving embedded devices with low power/energy and
resources constraints, edge computing and device-embedded analytics.
● Linked to other sub-ontologies with defined relationships. This preserves the full
semantic richness of the model or ontology.
Figure 3. Modular specification
Guarino  proposes the following structure:
● top-level ontologies covering general concepts (e.g. space, time, matter, object,
event, action) which are independent of a particular problem or domain;
● domain ontologies and task ontologies, covering concepts related to a generic
domain (e.g. energy) or a generic task or activity (e.g. flexibility management);
● application ontologies, covering a particular specialization of the above
ontologies, often corresponding to the description of a specific capability (such
as energy consumption measurement).
3 This can be achieved by design pattern approaches
22-Oct-2019 Towards Semantic Interoperability Standards based on Ontologies 7
However, when using the modularization principle, one shall ensure that:
● integrity of a sub-ontology is maintained, i.e. if a sub-ontology depends on other
ones, any sub-ontologies changes should preserve those dependency relations,
● The processing of the sub-ontologies union is not too complex.
● The reasoning and querying are still decidable for the modularized ontology, i.e.
can still be performed within a finite time period.
● integrity of a sub-ontology is maintained, which means that if a sub-ontology
depends on other ones, any changes should preserve those dependency links.
Modularization is easier to achieve if an organisation can use specification tools, like
e.g. ModOnto  inspired for object oriented software engineering, to edit and structure
of a specification into modules.
Table 3. Example of practice for ontology modularization
Example of practice
In the previous cross-domain interoperability specification it is
not useful to publish the entire energy domain ontology nor the
entire electric vehicle ontology. A modular specification allows
for the sharing of sub-ontologies at a sufficient level.
Figure 4. Example of modular specification
3.4 Evaluation of a specification
It is important to evaluate the “usefulness” of a specification. Specifications are defined
for designing applications. One typical indicator is the level of consensus. A specification
that has not reached consensus is likely not to be adopted. Semantic interoperability
specifications that are not cocreated by domain and ontology experts can fall into this
trap. Domain experts are required to constantly follow the specification process and
agree on the content while semantic interoperability experts guarantee that the
specification is sound.
Specifications need evaluations. It could rely on an indicator consisting of two TRLs
(Technology Readiness Level) or a metrics used in the industry to measure whether a
8 Towards Semantic Interoperability Standards based on Ontologies 22-Oct-2019
product is close to the market. A specification is deemed mature when both TRLs are
high, TRL examples are: 1) a domain specification TRL which focuses on whether all
domain needs are covered, and 2) an ontology specification TRL, which focuses on
whether the specification is well-formed. Raad  provides a survey on ontology
For instance, a tool assisting interoperability engineers to structure a specification into
modules and to assess the TRL could be useful.
Table 4. Example of practice specification evaluation
Example of practice
In the previous example, the new cross domain specification
starts with a low TRL for the ontology and for the specification.
The TRL increases as the associated ontology is validated
(ontology TRL) and the consensus is reached (specification
Figure 5. Example of specification evaluation
3.5 Deployment concerns
Deployment concerns in a specification of a semantic interoperability standard is
important. Two main concerns are:
●Provision for profiles and discovery. Some specifications concern a domain
market segment. For instance, device manufacturers want to add semantic
specifications concerning features (e.g., providing web services to send data on
the Web). Specifications might even be proprietary when device manufacturers
agree on co-existing solutions solved by service discovery capabilities. Profiles
are widely used in interoperability specifications (e.g., a washing machine)
implements extra features for interoperability such as finer grain remotely control
of the washing machine. Consequently semantic interoperability specifications
should also support profiles; a profile can be a concept in the ontology.
●Support for version management. Semantic interoperability specifications
evolve as a domain evolves to match the needs of different generations of
22-Oct-2019 Towards Semantic Interoperability Standards based on Ontologies 9
products (e.g., a new generation of smartphone). Two types of version
management are needed: 1) a specification change: the rules for compatibility
must be anticipated, e.g. do two systems using different versions interoperate?,
and 2) an ontology evolution : is the specification changed? In the two cases,
mechanisms to support such evolutions should be agreed upfront.
Table 5. Example of deployment requirements
Managing ontologies from a profile viewpoint: The profile
concept is handled at the ontology level (either as part of the
ontology, or as part of tools supporting the ontology).
Browsing an ontology from a profile viewpoint is possible, i.e.
only showing the concepts that are used by the profile.
Managing Intellectual Property Rights (IPR) while
ensuring open interoperability specifications: a semantic
interoperability specification refers to ontology subsets which
contain IPR, for instance, the use of an ontology describing a
functional behavior that is patented.
Upward compatibility: Here is an example scenario: a
washing machine uses the SAREF V1 ontology. In a second
generation of washing machine, an extended specification
allows control of the washing machine by an Artificial
Intelligence (AI) agent. The SAREF V1 ontology evolves to a
SAREF V2 ontology. All new generation washing machines
are upward compatible with SAREF V1 ontology.
Figure 6. Ontology evolution management
10 Towards Semantic Interoperability Standards based on Ontologies 22-Oct-2019
4 Initiatives for structured ontologies supported by
4.1 Initiatives on ontologies supported by standardization
A number of ongoing standardization initiatives on semantic interoperability are
described in Table 7 (initially referenced in  ).
Table 6. Standardization initiatives on semantic interoperability
The Semantic Sensor Network (SSN) ontology is an ontology
for describing sensors and their observations, the involved
procedures, the studied features of interest, the samples used to
do so, and the observed properties, as well as actuators. SSN
follows a horizontal and vertical modularization architecture by
including a lightweight but self-contained core ontology called
SOSA (Sensor, Observation, Sample, and Actuator)  for its
elementary classes and properties . With their different scope
and different degrees of axiomatization, SSN and SOSA are able
to support a wide range of applications and use cases, including
satellite imagery, large-scale scientific monitoring, industrial and
household infrastructures, social sensing, citizen science,
observation-driven ontology engineering, and the Web of Things.
W3C Web of
The Web of Things (WoT) is an extension of the Internet of
Things (IoT) to ease the access to data using the benefits of Web
technologies [10,11]. Data is generated by things/devices and
then exploited by more and more web-based applications to
monitor healthcare or even control home automation devices. The
W3C Web of Things (WoT) Interest Group is designing a
vocabulary to describe interactions between objects through the
Web, a potential implementation is the WoT ontology . At the
date of writing, the WoT ontology is not aligned with W3C SSN
ontologies, but there is ongoing work on aligning them. A
healthcare scenario has been designed "Remote health
monitoring system" among several use cases.
oneM2M is an international standard for Machine-to-Machine
(M2M) that has developed the oneM2M Base Ontology . At
the date of writing, the oneM2M Base Ontology is not aligned with
W3C SSN, but it is aligned with SAREF core concepts.
MyOntoSens modular ontology, mainly based on SSN V1 and
OGC standards, is an improvement of existing WSNs ontologies
. It has been standardized in 2015 for medical devices and
22-Oct-2019 Towards Semantic Interoperability Standards based on Ontologies 11
BANs (Body Area Networks) as a Technical Specification (TS)
within the SmartBAN Technical Committee of the ETSI
standardization body . This ontology is relevant to build
health, wellbeing/wellness and personal safety applications based
on smart devices.
The Smart Applications Reference Ontology (SAREF) is a
standardized ontology for IoT devices and solutions published by
ETSI in a series of Technical Specifications initially released in
2015  and updated in 2017 . Even if its initial objective
was to build a reference ontology for appliances relevant for
energy efficiency, SAREF is not limited to this scope and can
serve as upper reference model to enable better integration of
data from various vertical domains in the IoT. Hence, SAREF has
been extended to different domains such as energy, environment,
buildings, smart cities, agriculture, industry & manufacturing; and
is currently being extended to the automotive,
eHealth/ageing-well, wearables and water domains.
SAREF has been designed re-using SSN and oneM2M according
to . ETSI has consolidated SAREF with new reference
ontology patterns and is developing a new SAREF development
Schema.org. is a well-known schema catalog to structure data on
Web pages to describe the location, person, etc. The IoT
Schema.org extension  is planned; discussions are ongoing.
4.2 System viewpoint of ontologies
While it is important to foster ontology developments, there is a need for convergence in
order to avoid the following risks:
●The use of incompatible ontologies might actually prevent interoperability, thus
creating a market fragmentation effect.
●There might be too many competing ontologies for the same domain creating a
babel tower situation.
In order to prevent these issues, a system viewpoint should be taken, as exemplified by
SAREF .Figure 7 shows an architecture on how ontologies are structured: a base
ontology (e.g., based on oneM2M) is above which a SAREF framework is positioned to
host domain-specific ontologies.
12 Towards Semantic Interoperability Standards based on Ontologies 22-Oct-2019
Figure 7. Example of system vision (from ETSI TR 103 411 )
5 Life cycles for ontology-driven interoperability
Supporting interoperability requires a system lifecycle viewpoint to ensure that proper
requirements, design, implementation, validation and maintenance of interoperability
features are integrated.
5.1.1 Introduction to system life cycles
ISO/IEC/IEEE 15288 (Systems and software engineering — System life cycle
processes)  defines a system lifecycle as “an abstract functional model representing
the conceptualization of a need for the system, its realization, utilization, evolution and
disposal”. A system lifecycle is described as a set of processes, which can take place
sequentially or in parallel, as shown in Figure 8 .
Figure 8. Example of System Life Cycle Processes
22-Oct-2019 Towards Semantic Interoperability Standards based on Ontologies 13
As shown in Figure 9, a process is described according to: its purpose; the outcome it
creates, and its activities which themselves consist of tasks.
Figure 9. Processes, Activities and Tasks
The ISO/IEC/IEEE 15288 standard  describes thirty processes structured into four
●Agreement processes which focus on activities related to supplier agreements,
●Organizational project-enabling processes which focus on activities related to
improvement of the organization’s business or undertaking,
●Technical management processes which focus on managing the resources and
assets allocated to the engineering of a system, and
●Technical processes which focus on technical actions throughout the life cycle.
The sections below provide guidance on which system life cycle processes need to
integrate interoperability activities.
5.1.2 Definition of interoperability-by-design
We define interoperability-by-design as the integration of the concept of interoperability
in the design and lifecycle of systems, as shown in Figure 10.
Figure 10. Interoperability-by-design
14 Towards Semantic Interoperability Standards based on Ontologies 22-Oct-2019
Relationship between interoperability-by-design process (i.e. integrating interoperability
concerns in the development of a system) and an interoperability specification lifecycle
is shown in Figure 11.
Figure 11. Interoperability-by-design vs Interoperability specification lifecycle
5.1.3 Interoperability activities system lifecycle
Activities/tasks related to interoperability by design that need to be integrated are shown
in the table below which uses the ISO/IEC/IEEE 15288 processes and provides
examples of activities that are related to interoperability.
Table 7. Lifecycle process and related interoperability activities
Typical lifecycle technical process (e.g.
Stakeholder needs and requirements
Interoperability needs and ontology
System requirements definition process
Architecture definition process
Interoperability point definition
Design definition process
No specific activity
System analysis process
Interoperability point specification
Interoperability point implementation
No specific activity
Interoperability plug test
No specific activity
No specific activity
5.1.4 Interoperability specification lifecycle
An interoperability specification follows its own lifecycle (a simple example is depicted in
Figure 12 and explained in Table 8. Such lifecycles are well known.
22-Oct-2019 Towards Semantic Interoperability Standards based on Ontologies 15
Figure 12. Example of interoperability specification lifecycle
Table 8. Interoperability specification lifecycle stages
Interoperability specification lifecycle
Define the requirements of the
Provide the specification
Consensus reaching on the specification
Publish the interoperability specification
5.2 Ontology-driven semantic Interoperability
5.2.1 Life cycles involved
Ontology-driven semantic interoperability assumes that interoperability-by-design is
based on the use of ontologies to describe the meaning of exchanged information.
Figure 13 shows the relationship between the interoperability lifecycle and the ontology
lifecycle. The following remarks can be made:
●The system lifecycles and the interoperability specification lifecycles are
●The interoperability specification lifecycles and the ontology lifecycles are
16 Towards Semantic Interoperability Standards based on Ontologies 22-Oct-2019
Figure 13. Ontology-driven Interoperability
5.2.2 Example for benefits of ontology-driven semantic
The benefit of ontology-driven ontology can be applied within Internet of Things
applications, for instance:
● Domain specific capabilities are described (e.g., sensing information from a
connected vehicle, or health sensing information from connected body sensors)
annotated with domain specific ontologies;
● The annotated sensing information is extended with higher level concepts to
provide an IoT application and platform viewpoint, using a service ontology
model as suggested by the W3C  as shown in Figure 14. The result is that a
sensor is viewed as a service (here a sensing service), which is described with
unified, common and shared concepts:
○ A service profile which expresses the service capabilities,
○ A service process which specifies how the service works (including the
service control and function calls),
○ The service grounding, which specifies how to access the service,
22-Oct-2019 Towards Semantic Interoperability Standards based on Ontologies 17
This approach is beneficial for cross-domain interoperability:
● a generic query service is available allowing the inspection of the device services
(connected vehicle sensor, health body sensor or an environmental sensor)
● a unified discovery service can be used, and
● an overall application / platform level interoperability framework is available..
Figure 14: High level example of a service ontology model (OWL-S) 
5.2.3 Ontology engineering
Ontology development typically follows a lifecycle, as shown in Figure 15 and explained
in Table 9.
Figure 15. Ontology lifecycle model example
Table 9. Ontology lifecycle process
Ontology lifecycle process
Ontology requirements definition
Define the requirements of the ontology to
Co-create the ontology. This process must
at least include a domain specific expert
and on ontology expert
Ontology consistency validation
Validation that an ontology is well-formed
18 Towards Semantic Interoperability Standards based on Ontologies 22-Oct-2019
Ontology consensus validation
Consensus reaching on the created
Publish the ontology
A number of ontology lifecycle models have been proposed such as the OTK
methodology , the Neon project collection of lifecycles  or the 101 methodology
Table 10 below shows the stages of OTK.
Table 10. Ontology lifecycle stages
Ontology lifecycle stages
Identify stakeholders and use cases,
Analyse knowledge sources
Develop baseline ontology
Technology focused evaluation
User focused evaluation
Ontology focused evaluation
Application and evolution
Manage evolution and maintenance
The Neon project lists the following models:
● Waterfall models such as
○ the four-phase model (initiation, design, implementation, maintenance),
○ the five-phase model (initiation, reuse, design, implementation,
○ the five-phase+merging phase model (initiation, reuse, merging, design,
○ the six-phase model (initiation, reuse, re-engineering, design,
implementation, maintenance), and
○ the six-phase+merging phase model (initiation, reuse, merging,
re-engineering, design, implementation, maintenance),
● Iterative-incremental ontology network lifecycle models, where there are
iterations and where each iteration follows a waterfall model.
The NeOn Methodology is a scenario-based methodology supporting different aspects
of the ontology development process: from the reuse of existing resources, to the
22-Oct-2019 Towards Semantic Interoperability Standards based on Ontologies 19
dynamic evolution of ontologies in distributed environments where knowledge is
introduced by different people at different stages. Furthermore, the proposed scenarios
are decomposed into different activities which can be combined for the achievement of
the expected goal.
There are nine scenarios defined in the NeOn Methodology:
● Scenario 1: From specification to implementation
● Scenario 2: Reusing and re-engineering non-ontological resources (NORs)
● Scenario 3: Reusing ontological resources
● Scenario 4: Reusing and re-engineering ontological resources
● Scenario 5: Reusing and merging ontological resources
● Scenario 6: Reusing, merging and re-engineering ontological resources
● Scenario 7: Reusing ontology design patterns (ODPs)
● Scenario 8: Restructuring ontological resources
● Scenario 9: Localizing ontological resources
5.2.4 Ontology validation methodsSemantic-based
Several methods are available for the validation of an ontology: 1) Syntactic-based
validation, 2) Semantic-based validation, and 3) Evolution-based validation.
Syntactic-based validation mainly consists in detecting potential pitfalls that could lead
to modelling errors. It includes the use of undefined properties and classes, poorly
formed namespaces, problematic prefixes, literal syntax.
Semantic-based validation uses rules which are built in the ontology languages and
rules users provided to detect logical issues in ontologies (ex: contradictory inferred
result). Examples of the first type are when two objects in an OWL ontology are said to
be different from each other (owl:differentFrom), the ontology can’t say that they are the
same thing (owl:sameAs).
Finally, evolution-based validation consists in observing the evolution of the ontology
usage, over its usage lifecycle. The original ontology schema is a posteriori compared to
all the instances of that ontology that have been used and or introduced (i.e. amended)
during a given period of time. The retained evaluation criteria can be:
● Ontology domain changes, i.e. any new knowledge that could have been added
to the domain formalized by the original ontology,
● Ontology usage perspectives changes, in a given domain, impacting the
20 Towards Semantic Interoperability Standards based on Ontologies 22-Oct-2019
● Ontology specification changes (ontology stability metric), i.e. number of new
concepts or attributes introduced in the original ontology.
Ontology usage, after its publication, is also monitored, and access to ontology classes
(i.e. concepts) and attributes can be counted. This provides metrics for pointing out the
concepts and attributes most often used, as well as the never used concepts and
attributes that will most probably have to finally be removed from the ontology since a
priori useless. Evolution-based ontology validation is suitable to address the objectives
of the ontology lifecycle presented in the next section.
5.2.5 Ontology-driven semantic interoperability lifecycle
Semantic interoperability can be driven by ontologies as shown in Figure 16.
Figure 16. Ontology-driven semantic interoperability lifecycles
Table 11. Ontology-driven interoperability specification lifecycle process
Define the type of knowledge that needs to
be captured in the ontology (domain, cross
domain and transversal, e.g. health,
transport and security)
Define the operational requirements (e.g.
Identify an ontology version management
Define the ontologies to be used, the part
that is encapsulated, the part that is
exposed and adapted
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Seek consensus for standardisation
Finalise or build the ontology that
describes the interoperability point
Seek consensus for standardisation
ontology test and
Validate that the ontology is well formed
and semantically consistent
Validate that the exposed ontology is what
Acceptance by the ecosystem (e.g.
community that will use the ontology) that
the ontology is at suitable maturity level
Integrate in the ontology version
Update and enhance the exposed ontology
Validate the updated ontology
Update the ontology version management
The ETSI document  describes in detail the ontology development process as
shown in Figure 17.
22 Towards Semantic Interoperability Standards based on Ontologies 22-Oct-2019
Figure 17. Ontology development process (from )
6 Recommendations for ontology-driven semantic
The following recommendations for ontology-driven semantic interoperability standards
● Providing guidance to ensure a standardised practice of ontology-driven
interoperability. The overall guidance would be provided by ISO/IEC 21823-3
 which is under development, and it would be complemented by other types
of guidance (e.g., on co-creation, modular design).
● Providing guidance on the creation and maintenance of reference ontologies.
This includes assistance on ontology engineering and lifecycle management.
This also involves the set up of a community of ontology practitioners to share
and collect practices and tools.
● Developing ontology standards, including general ontologies and domain
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This work has received funding from the European Union’s Horizon 2020 research and
innovation programme under grant agreements No.732240 (SynchroniCity) and No. 688467
(VICINITY); from ETSI under Specialist Task Forces 534, 556, and 566.
This work is partially funded by Hazards SEES NSF Award EAR 1520870, and KHealth NIH
1 R01 HD087132-01. The opinions expressed are those of the authors and do not reflect
those of the sponsors.
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