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Towards Semantic Interoperability Standards based on Ontologies

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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 technologies.
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Towards Semantic Interoperability Standards
based on Ontologies
1
Semantic Interoperability White Paper
Editor
• Martin Bauer, NEC Laboratories Europe
Contributors
• 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
International License.
22-Oct-2019 Towards Semantic Interoperability Standards based on Ontologies 1
Table of Contents
Background 3
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
References 24
2 Towards Semantic Interoperability Standards based on Ontologies 22-Oct-2019
Background
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
technologies.
The document is made available under the Creative Commons Attribution 4.0
International License.
1 Introduction
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
22-Oct-2019 Towards Semantic Interoperability Standards based on Ontologies 3
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 [1]
2
2 Figure under CC0 license:
https://commons.wikimedia.org/wiki/File:Semantic_web_stack.svg
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
practice
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
Description
Example of practice
integrating
separation of
concern
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
specification
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
specification.
Table 2. Example of practice for specification scope
Example
Description
Example of practice
for specification
scope
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
3
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.
Reusable.
Figure 3. Modular specification
Guarino [2] 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);
and
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 [3] inspired for object oriented software engineering, to edit and structure
of a specification into modules.
Table 3. Example of practice for ontology modularization
Example
Description
Example of practice
for ontology
modularization
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 [4] provides a survey on ontology
evaluation.
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
Description
Example of practice
for specification
evaluation
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
TRL).
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 [4]: is the specification changed? In the two cases,
mechanisms to support such evolutions should be agreed upfront.
Table 5. Example of deployment requirements
Examples of
deployment
requirements
Description
Concerning profiles
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.
Concerning version
management
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
standardization
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 [5] [6]).
Table 6. Standardization initiatives on semantic interoperability
Initiative
W3C Semantic
Sensor Network
ontology
W3C Web of
Things
oneM2M
SmartBAN
(MyOntoSens)
22-Oct-2019 Towards Semantic Interoperability Standards based on Ontologies 11
SAREF
Schema.org
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 [17].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 [22])
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 Interoperability-by-design
5.1.1 Introduction to system life cycles
ISO/IEC/IEEE 15288 (Systems and software engineering System life cycle
processes) [23] 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 [24].[25]
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 [23] describes thirty processes structured into four
categories:
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.
ISO/IEC/IEEE 15288)
Interoperability activities
Stakeholder needs and requirements
definition
Interoperability needs and ontology
requirements definition
System requirements definition process
Interoperability requirements
Architecture definition process
Interoperability point definition
Design definition process
No specific activity
System analysis process
Interoperability point specification
Implementation process
Interoperability point implementation
Integration process
No specific activity
Verification process
Interoperability test
Transition process
Interoperability plug test
Validation process
Validation test
Operation process
No specific activity
Maintenance process
Interoperability maintenance
Disposal process
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
stages
Description
Requirement
Define the requirements of the
interoperability specification
Specification
Provide the specification
Consensus validation
Consensus reaching on the specification
Publication
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
separated.
The interoperability specification lifecycles and the ontology lifecycles are
separated.
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
interoperability
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 [26] 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) [26]
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
Description
Ontology requirements definition
Define the requirements of the ontology to
create
Ontology co-creation
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
ontology
Ontology publication
Publish the ontology
A number of ontology lifecycle models have been proposed such as the OTK
methodology [27], the Neon project collection of lifecycles [28] or the 101 methodology
[29].
Table 10 below shows the stages of OTK.
Table 10. Ontology lifecycle stages
Ontology lifecycle stages
Description
Feasibility study
Identify stakeholders and use cases,
identify tools.
Ontology kickoff
Capture requirements
Analyse knowledge sources
Develop baseline ontology
Refinement
Extract knowledge
Formalise
Evaluation
Technology focused evaluation
User focused evaluation
Ontology focused evaluation
Application and evolution
Apply ontology
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,
maintenance),
the five-phase+merging phase model (initiation, reuse, merging, design,
implementation, maintenance),
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
ontology conceptualization,
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
Ontology-driven
Interoperability
specification lifecycle
process
Description
Interoperability
specification
requirements
Semantic
interoperability
ontology
requirements
definition
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.
compatibility)
Identify an ontology version management
scheme
Ontology driven
specification
Semantic
interoperability
ontology
Define the ontologies to be used, the part
that is encapsulated, the part that is
exposed and adapted
22-Oct-2019 Towards Semantic Interoperability Standards based on Ontologies 21
structure
co-creation
Seek consensus for standardisation
Semantic
interoperability
ontology
co-construction
Finalise or build the ontology that
describes the interoperability point
Seek consensus for standardisation
Semantic
interoperability
ontology test and
validation
Validate that the ontology is well formed
and semantically consistent
Validate that the exposed ontology is what
is expected
Interoperability
consensus validation
Semantic
interoperability
ontology
commissioning
and deployment
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
management
Publication
Semantic
interoperability
ontology
maintenance
Update and enhance the exposed ontology
Validate the updated ontology
Semantic
interoperability
ontology
decommissioning
Update the ontology version management
The ETSI document [22] 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 [22])
6 Recommendations for ontology-driven semantic
interoperability standards
The following recommendations for ontology-driven semantic interoperability standards
are:
Providing guidance to ensure a standardised practice of ontology-driven
interoperability. The overall guidance would be provided by ISO/IEC 21823-3
[30] 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
ontologies.
22-Oct-2019 Towards Semantic Interoperability Standards based on Ontologies 23
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Acknowledgements
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.
26 Towards Semantic Interoperability Standards based on Ontologies 22-Oct-2019
... Ontology-driven interoperability should ensure semantic interoperability [222]. The question pursued here is: Which ontology can be used for a device description in the MYNO framework? ...
... Therefore many organizations work on semantics for IoT. For example: AIOTI, ISO/IEC JTC1, ETSI, oneM2M and W3C collaborate on two joint white papers on Semantic Interoperability targeting developers and standardization engineers [221,222]. Also, European Research Cluster on the Internet of Things (IERC) published a report about semantic interoperability with best practices and recommendations [223]. ...
Thesis
The Internet of Things (IoT) is a system of physical objects that can be discovered, monitored, controlled, or interacted with by electronic devices that communicate over various networking interfaces and eventually can be connected to the wider Internet. [Guinard and Trifa, 2016]. IoT devices are equipped with sensors and/or actuators and may be constrained in terms of memory, computational power, network bandwidth, and energy. Interoperability can help to manage such heterogeneous devices. Interoperability is the ability of different types of systems to work together smoothly. There are four levels of interoperability: physical, network and transport, integration, and data. The data interoperability is subdivided into syntactic and semantic data. Semantic data describes the meaning of data and the common understanding of vocabulary e.g. with the help of dictionaries, taxonomies, ontologies. To achieve interoperability, semantic interoperability is necessary. Many organizations and companies are working on standards and solutions for interoperability in the IoT. However, the commercial solutions produce a vendor lock-in. They focus on centralized approaches such as cloud-based solutions. This thesis proposes a decentralized approach namely Edge Computing. Edge Computing is based on the concepts of mesh networking and distributed processing. This approach has an advantage that information collection and processing are placed closer to the sources of this information. The goals are to reduce traffic, latency, and to be robust against a lossy or failed Internet connection. We see management of IoT devices from the network configuration management perspective. This thesis proposes a framework for network configuration management of heterogeneous, constrained IoT devices by using semantic descriptions for interoperability. The MYNO framework is an acronym for MQTT, YANG, NETCONF and Ontology. The NETCONF protocol is the IETF standard for network configuration management. The MQTT protocol is the de-facto standard in the IoT. We picked up the idea of the NETCONF-MQTT bridge, originally proposed by Scheffler and Bonneß[2017], and extended it with semantic device descriptions. These device descriptions provide a description of the device capabilities. They are based on the oneM2M Base ontology and formalized by the Semantic Web Standards. The novel approach is using a ontology-based device description directly on a constrained device in combination with the MQTT protocol. The bridge was extended in order to query such descriptions. Using a semantic annotation, we achieved that the device capabilities are self-descriptive, machine readable and re-usable. The concept of a Virtual Device was introduced and implemented, based on semantic device descriptions. A Virtual Device aggregates the capabilities of all devices at the edge network and contributes therefore to the scalability. Thus, it is possible to control all devices via a single RPC call. The model-driven NETCONF Web-Client is generated automatically from this YANG model which is generated by the bridge based on the semantic device description. The Web-Client provides a user-friendly interface, offers RPC calls and displays sensor values. We demonstrate the feasibility of this approach in different use cases: sensor and actuator scenarios, as well as event configuration and triggering. The semantic approach results in increased memory overhead. Therefore, we evaluated CBOR and RDF HDT for optimization of ontology-based device descriptions for use on constrained devices. The evaluation shows that CBOR is not suitable for long strings and RDF HDT is a promising candidate but is still a W3C Member Submission. Finally, we used an optimized JSON-LD format for the syntax of the device descriptions. One of the security tasks of network management is the distribution of firmware updates. The MYNO Update Protocol (MUP) was developed and evaluated on constrained devices CC2538dk and 6LoWPAN. The MYNO update process is focused on freshness and authenticity of the firmware. The evaluation shows that it is challenging but feasible to bring the firmware updates to constrained devices using MQTT. As a new requirement for the next MQTT version, we propose to add a slicing feature for the better support of constrained devices. The MQTT broker should slice data to the maximum packet size specified by the device and transfer it slice-by-slice. For the performance and scalability evaluation of MYNO framework, we setup the High Precision Agriculture demonstrator with 10 ESP-32 NodeMCU boards at the edge of the network. The ESP-32 NodeMCU boards, connected by WLAN, were equipped with six sensors and two actuators. The performance evaluation shows that the processing of ontology-based descriptions on a Raspberry Pi 3B with the RDFLib is a challenging task regarding computational power. Nevertheless, it is feasible because it must be done only once per device during the discovery process. The MYNO framework was tested with heterogeneous devices such as CC2538dk from Texas Instruments, Arduino Yún Rev 3, and ESP-32 NodeMCU, and IP-based networks such as 6LoWPAN and WLAN. Summarizing, with the MYNO framework we could show that the semantic approach on constrained devices is feasible in the IoT.
... Semantic interoperability is critical for the continuous improvement of patient's quality of care, global health research, and the management of healthcare institutions. It ensures the preservation of meaning while exchanging information is exchanged between different systems [9]. The interdependence of technical and domain-knowledge is recognized as a barrier to successfully implementing and regularly updating an HIS. ...
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The COVID-19 pandemic had put pressure on various national healthcare systems, due to the lack of health professionals and exhaustion of those avaliable, as well as lack of interoperability and inability to restructure their IT systems. Therefore, the restructuring of institutions at all levels is essential, especially at the level of their information systems. Furthermore, the COVID-19 pandemic had arrived in Portugal at March 2020, with a breakout on the northern region. In order to quickly respond to the pandemic, the CHUP healthcare institution, known as a research center, has embraced the challenge of developing and integrating a new approach based on the openEHR standard to interoperate with the institution's existing information and its systems. An openEHR clinical modelling methodology was outlined and adopted, followed by a survey of daily clinical and technical requirements. With the arrival of the virus in Portugal, the CHUP institution has undergone through constant changes in their working methodologies as well as their openEHR modelling. As a result, an openEHR patient care workflow for COVID-19 was developed.
... Baqa et al. [42] clearly explained the need for semantic interoperability and used an ontology recommendation framework for semantic interoperability standards such as ontology driven interoperability, setting up of an ontology practitioner, and establishing ontology standards including domain and general ontology. Established ontology has been evaluated using syntactic, semantic based validation, and evolution based validation. ...
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The increasing demand for cloud computing has shifted business toward a huge demand for cloud services, which offer platform, software, and infrastructure for the day-to-day use of cloud consumers. Numerous new cloud service providers have been introduced to the market with unique features that assist service developers collaborate and migrate services among multiple cloud service providers to address the varying requirements of cloud consumers. Many interfaces and proprietary application programming interfaces (API) are available for migration and collaboration services among cloud providers, but lack standardization efforts. The target of the research work was to summarize the issues involved in semantic cloud portability and interoperability in the multi-cloud environment and define the standardization effort imminently needed for migrating and collaborating services in the multi-cloud environment.
... The semantic interoperability level of EHR systems is critical for the continuous improvement of the patient's quality of care, global health research, and the management of healthcare institutions. This level of interoperability ensures the meaning and semantic understanding of the information is exchanged between different systems [11]. The dual architecture approach has been gaining strength to promote and secure the meaning of knowledge and information. ...
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The COVID-19 pandemic has collapsed several national health systems, due to the lack of healthcare professionals and exhaustion of those employed, as well as the lack of interoperability and capacity to restructure their informatic systems. Therefore, the restructuring of institutions at all levels is essential, mainly at the level of their Information Systems. When the COVID-19 pandemic had spread to Portugal in March 2020, with a breakout on the northern region, the Centro Hospitalar Universitário do Porto (CHUP) healthcare institution had felt the need to develop and integrate a new approach based on the openEHR standard to interoperate with the institution’s existing information systems, with the aim of responding quickly to the pandemic’s evolution.
... The ontological view presents the semantic modeling of SHEs based on ontologies [25], which is a higher level abstraction and a SHE enabling technology to bring semantic interoperability, automatic federation and knowledge discovery capabilities. The SAREF (Smart Appliances REFerence) ontology was developed and standardized by the European Commission in close cooperation with ETSI (European Telecommunications Standards Institute) to provide a modular and domain-independent semantic layer for smart appliances. ...
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Along with the proliferation of smart home solutions, Smart Home Environment (SHE) has been constantly evolving with diverse functions and services to improve households’ living experiences. Consequently, a global view of SHE is desired to represent the characteristics of the ever-changing domain for both solution adoption and innovation purposes. In this paper, we present a reference model of SHE aiming at capturing the environmental characteristics by leveraging the quick evolution pace and the model abstraction level. The reference model consists of three views i.e., functional view, deployment view and ontological view organized following the middle-out methodology. The functional view firstly presents the hardware components and software features necessary to build modern SHE, and then the deployment view and ontological view respectively describe the deployment structure in lower level and the SHE semantics in higher level. The objective is to provide, from a service provider perspective, a common understanding of SHE for household adoption, better industry positioning and research innovation.
... According to [20] , ontology reuse can be understood as a three-step process: (i) ontology discovery, (ii) ontology selection, and (iii) ontology integration. [25] . The task of finding appropriate ontologies can nowadays be facilitated due to the numerous ontology catalogs to find existing ontologies. ...
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. The task of information extraction can be seen as a problem of semantic matching between a user-defined template and a piece of information written in natural language. To this purpose, the ontological assumptions of the template need to be suitably specified, and compared with the ontological implications of the text. So-called "ontologies", consisting of theories of various kinds expressing the meaning of shared vocabularies, begin to be used for this task. This paper addresses the theoretical issues related to the design and use of such ontologies for purposes of information retrieval and extraction. After a discussion on the nature of semantic matching within a model-theoretical framework, we introduce the subject of Formal Ontology, showing how the notions of parthood, integrity, identity, and dependence can be of help in understanding, organizing and formalizing fundamental ontological distinctions. We present then some basic principles for ontology design, and we illustrate a preliminary proposal for a top-level ontology develped according to such principles. As a concrete example of ontology-based information retrieval, we finally report an ongoing experience of use of a large linguistic ontology for the retrieval of object -oriented software components. 1.