ResearchPDF Available

Abstract and Figures

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.
Content may be subject to copyright.
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
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
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
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 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
separation of
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
for specification
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 [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);
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 of practice
for ontology
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
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
for specification
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 [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
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
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 [5] [6]).
Table 6. Standardization initiatives on semantic interoperability
The Semantic Sensor Network (SSN)[7] 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) [8] for its
elementary classes and properties [9]. 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.
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 [12]. 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 [13]. 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
[14]. 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 [15]. This ontology is relevant to build
health, wellbeing/wellness and personal safety applications based
on smart devices.
The Smart Applications Reference Ontology (SAREF)[16] is a
standardized ontology for IoT devices and solutions published by
ETSI in a series of Technical Specifications initially released in
2015 [17] and updated in 2017 [18]. 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 [19]. ETSI has consolidated SAREF with new reference
ontology patterns and is developing a new SAREF development
workflow [20]. is a well-known schema catalog to structure data on
Web pages to describe the location, person, etc. The IoT extension [21] 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 [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
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.
Interoperability activities
Stakeholder needs and requirements
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
Define the requirements of the
interoperability specification
Provide the specification
Consensus validation
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 [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
Ontology requirements definition
Define the requirements of the ontology to
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 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
Table 10 below shows the stages of OTK.
Table 10. Ontology lifecycle stages
Ontology lifecycle stages
Feasibility study
Identify stakeholders and use cases,
identify tools.
Ontology kickoff
Capture requirements
Analyse knowledge sources
Develop baseline ontology
Extract knowledge
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,
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
specification lifecycle
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
Ontology driven
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
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
is expected
consensus validation
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
Update and enhance the exposed ontology
Validate the updated ontology
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
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
22-Oct-2019 Towards Semantic Interoperability Standards based on Ontologies 23
1. Semantic Web Layer Cake Tweak, Explained. [cited 20 Oct 2019]. Available:
2. Guarino N. Semantic matching: Formal ontological distinctions for information
organization, extraction, and integration. Information Extraction A Multidisciplinary
Approach to an Emerging Information Technology. 1997. pp. 139–170.
3. Bezerra C, Freitas F, Euzenat J, Zimmermann A. ModOnto: A tool for modularizing
ontologies. Proceedings of the 3rd workshop on ontologies and their applications
(Wonto). 2009.
4. Raad J, Cruz C. A Survey on Ontology Evaluation Methods. Proceedings of the
International Conference on Knowledge Engineering and Ontology Development.
2015. Available:
5. Gyrard A, Gaur M, Padhee S, Sheth A, Juganaru-Mathieu M. Knowledge Extraction
for the Web of Things (KE4WoT). Companion of the The Web Conference 2018 on
The Web Conference 2018 - WWW ’18. 2018. doi:10.1145/3184558.3192305
6. Knowledge Extraction for the Web of Things (KE4WoT) Challenge 2018. [cited 20
Oct 2019]. Available:
7. OGC / W3C Semantic Sensor Network Incubator Group. Semantic Sensor Network
Ontology (SSN). Available:
8. Sensor Observation, Sample and Actuator (SOSA) Ontology. [cited 20 Oct 2019].
9. Haller A, Janowicz K, Cox SJD, Lefrançois M, Taylor K, Le Phuoc D, et al. The
modular SSN ontology: A joint W3C and OGC standard specifying the semantics of
sensors, observations, sampling, and actuation. Semantic Web. 2018. pp. 9–32.
10. Web of Things Architecture. Available:
11. Web of Things - Thing Description. Available:
12. Web of Things (WoT) Thing Description Ontology. In: W3C Editor’s Draft 20
October 2019 [Internet]. [cited 20 Oct 2019]. Available:
13. Ontologies used for oneM2M. In: oneM2M [Internet]. [cited 20 Oct 2019]. Available:
14. Nachabe L, Girod-Genet M, El Hassan B. Unified Data Model for Wireless Sensor
Network. IEEE Sensors Journal. 2015. pp. 3657–3667.
24 Towards Semantic Interoperability Standards based on Ontologies 22-Oct-2019
15. ETSI. Smart Body Area Network (SmartBAN); Unified data representation formats,
semantic and open data model. 10-2015. Report No.: TS 103 378 (V1.1.1).
16. Smart Applications REFerence Ontology (SAREF). [cited 20 Oct 2019]. Available:
17. Daniele L, den Hartog F, Roes J. Created in Close Interaction with the Industry: The
Smart Appliances REFerence (SAREF) Ontology. Formal Ontologies Meet Industry
FOMI 2015. Cham: Springer; 2015. pp. 100–112.
18. ETSI. SmartM2M Smart Appliances; Reference Ontology and oneM2M Mapping.
03-2017. Report No.: TS 103 264 V2.1.1. Available:
19. Moreira JL, Daniele LM, Pires LF, van Sinderen MJ, Wasielewska K, Szmeja P, et
al. Towards iot platforms’ integration: Semantic translations between W3C SSN and
ETSI SAREF. Proceedings of SIS-IoT: Semantic Interoperability and
Standardization in the IoT Workshop 2017.
20. SAREF Framework and Development Workflow. [cited 20 Oct 2019]. Available:
21. [cited 20 Oct 2019]. Available:
22. ETSI. ETSI Technical Report: SmartM2M; Smart Appliances; SAREF extension
investigation. 2017 Feb. Report No.: TR 103 411 V1.1.1. Available:
23. ISO/IEC/IEEE. Systems and software engineering -- System life cycle processes.
2015. Report No.: 15288. Available:
24. International Organization for Standardization (Ginebra). ISO/IEC TR 24748-1
Technical Report: Systems and Software Engineering : Life Cycle Management.
Guide for life cycle management. Guide de gestion du cycle de vie. 2010.
25. Lake JG. Report on Development of ISO Standard 15288 System Life Cycle
Processes. INCOSE International Symposium. 1997. pp. 435–442.
26. OWL-S: Semantic Markup for Web Services. 22 Nov 2004 [cited 20 Oct 2019].
27. On-To-Knowledge (OTK) Methodology. [cited 20 Oct 2019]. Available:
28. Neon methodology lifecycles. [cited 20 Oct 2019]. Available:
22-Oct-2019 Towards Semantic Interoperability Standards based on Ontologies 25
29. Noy NF, McGuinness DL. Ontology Development 101: A Guide to Creating Your
First Ontology. Available:
30. ISO/IEC/IEEE. Internet of Things (IoT) - Interoperability for IoT Systems - Part 3:
Semantic interoperability. Standard under development. Report No.: 21823-3.
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]. ...
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.
... • C5: Standardization-compliancy: Lessons learned from semantic interoperability are disseminated within the ISO/IEC 21823-3 IoT semantic interoperability [16], and the Alliance for the Internet of Things Innovation (AIOTI) Standardization WG (, accessed on 17 September 2022), which includes the Semantic Interoperability Expert Group [17,18] where the rule-based inference engine is taken as a baseline [19]. SAREF designers are also members of AIOTI Standard WG. ...
Full-text available
Humans are feeling emotions every day, but they can still encounter difficulties understanding them. To better understand emotions, we integrated interdisciplinary knowledge about emotions from various domains such as neurosciences (e.g., neurobiology), physiology, and psychology (affective sciences, positive psychology, cognitive psychology, psychophysiology, neuropsychology, etc.). To organize the knowledge, we employ technologies such as Artificial Intelligence with Knowledge Graphs and Semantic Reasoning. Furthermore, Internet of Things (IoT) technologies can help to acquire physiological data knowledge. The goal of this paper is to aggregate the interdisciplinary knowledge and implement it within the Emotional Knowledge Graph (EmoKG). The Emotional Knowledge Graph is used within our naturopathy recommender system that suggests food to boost emotion (e.g., chocolate contains magnesium that is recommended when we feel depressed). The recommender system also answers a set of competency questions to easily retrieve emotional related-knowledge from EmoKG, such as what are the basic emotions and the more sophisticated ones, what are the neurotransmitters and hormones related to emotions, etc. To follow FAIR principles, EmoKG is mapped to existing knowledge bases found on the BioPortal biomedical ontology catalog such as SNOMEDCT, FMA, RXNORM, MedDRA, and also from emotion ontologies (when available online). We design the LOV4IoT-Emotion ontology catalog that encourages researchers from heterogeneous communities to apply FAIR principles by releasing online their (emotion) ontologies, datasets, rules, etc. The set of ontology codes shared online can be semi-automatically processed; if not available, the scientific publications describing the emotion ontologies are semi-automatically processed with Natural Language Processing (NLP) technologies. This research is also relevant for other use cases such as European projects (ACCRA for emotional robots to reduce the social isolation of aging people, StandICT for standardization, and AI4EU for Artificial Intelligence) and alliances for IoT such as AIOTI. The recommender system can be extended to address other advice such as aromatherapy and take into consideration medical devices to monitor patients’ vital signals related to emotions and mental health.
... 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. ...
Full-text available
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. ...
Full-text available
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. ...
Full-text available
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. ...
Full-text available
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.
Over the lifecycle of a production plant digitalization leads to a large data footprint along different software (SW) components. A digital data exchange is the fundament to support data integrity, prevent data loss and avoid duplicate work. AutomationML (AML) is a commonly accepted tool-independent format to exchange data of different domains along the lifecycle of a production plant. This contribution provides an easy-to-use workflow that empowers SW components to transform AML into a strongly typed class hierarchy which is the basis for an efficient and maintainable SW solution.
Full-text available
Solutions for the generation of FAIR (Findable, Accessible, Interoperable, and Reusable) data and metadata in experimental tribology are currently lacking. Nonetheless, FAIR data production is a promising path for implementing scalable data science techniques in tribology, which can lead to a deeper understanding of the phenomena that govern friction and wear. Missing community-wide data standards, and the reliance on custom workflows and equipment are some of the main challenges when it comes to adopting FAIR data practices. This paper, first, outlines a sample framework for scalable generation of FAIR data, and second, delivers a showcase FAIR data package for a pin-on-disk tribological experiment. The resulting curated data, consisting of 2,008 key-value pairs and 1,696 logical axioms, is the result of (1) the close collaboration with developers of a virtual research environment, (2) crowd-sourced controlled vocabulary, (3) ontology building, and (4) numerous-seemingly-small-scale digital tools. Thereby, this paper demonstrates a collection of scalable non-intrusive techniques that extend the life, reliability, and reusability of experimental tribological data beyond typical publication practices.
Full-text available
This paper presents a condensed review of the European smart body area network (BAN) standardization work and its already published standards. The work is carried out under the ETSI Technical Committee (TC) SmartBAN. The goal of ETSI TC SmartBAN is to define and develop new European BAN standards; fostering the successful market adoption of wireless BAN technology by providing a standardized way to bring new interoperable products into the health, medical, sport, leisure and IoT markets, targeting to global exploitation. TC SmartBAN covers wireless communications, especially the physical and medium access control layers, associated security, semantic interoperability and open data representation, which are necessary features in the data transmissions, as well as building blocks of the future smart coordinator. The use of smart coordinator centralizes the network intelligence to the hub, which enables implementation of simple nodes. The SmartBAN approach will be computationally light, and it supports reliable operations across heterogeneous networks. Due to the semantic approach in all functional levels, SmartBAN also supports high interoperability. For emergency traffic, SmartBAN provides different priority classes and fast channel access associated with a low latency. Novel concept adopted in the SmartBAN for high-priority traffic is a multi-use channel access, which can also improve the spectral efficiency. Not only on-body nodes and applications, TC SmartBAN is considering in-body communications for capsule endoscopy. In addition, SmartBAN collaborates with other standardization groups, such as oneM2M and AIOTI, to widen the impact of its work.
Conference Paper
Full-text available
The Web of Things (WoT) is an extension of the Internet of Things (IoT) to ease the access to data generated by things/devices using the benefits of Web technologies. Data is exploited by WoT applications to monitor healthcare or even control home automation devices. The purpose of the Knowledge Extraction for the Web of Things (KE4WoT) challenge is to automatically extract the relevant knowledge from already designed smart WoT applications in various applicative domains. Those applications design and release Knowledge Bases (e.g., datasets and/or models) on the web.
Conference Paper
Full-text available
Several IoT ontologies have been developed lately to improve the semantic interoperability of IoT solutions. The most popular of these ontologies, the W3C Semantic Sensor Network (SSN), is considered an ontological foundation for diverse IoT initiatives, particularly OpenIoT. With characteristics similar to SSN, the ETSI Smart Appliances REFerence (SAREF) ontology evolved from the needs of smart home solutions to common requirements of IoT. Some IoT solutions rely on platform-specific ontologies and their integration requires mechanisms to align these ontologies. In this paper we discuss the ontology alignment between SSN and SAREF, identifying mapping alternatives and proposing basic mappings that can be re-used to define more complex ones. We introduce here an initial specification of the semantic translations from the main elements of SSN to SAREF, which includes classes, object properties and data properties. The alignment will be used in a semantic matching process leveraging the semantic mediator component of the INTER-IoT project. An initial evaluation of the translation was executed by translating the wind sensor (Vaisala WM30), an example provided by the W3C, from SSN to SAREF. This initial evaluation demonstrates the coherence and feasibility of the proposed mappings.
Conference Paper
Full-text available
Around two thirds of the energy consumed by buildings can be traced back to the residential sectors and thus household appliances. Today, most appliances are highly intelligent and networked devices, in principle being able to form complete energy consuming, producing, and managing systems. Reducing the use of energy has therefore become a matter of managing and optimizing the energy utilization at a system level. These systems are technically very heterogeneous, and standardized interfaces on a sensor and device level are therefore needed. Many of the required standards already exist, but a common architecture does not, resulting in a too fragmented and powerless market. To enable semantic interoperability for smart appliances we therefore developed SAREF, the Smart Appliance REFerence ontology. In this paper we present SAREF and describe our experience in creating this ontology in close interaction with the industry, pointing out the lessons learned and identifying topics for follow-up actions.
Conference Paper
Full-text available
Ontologies nowadays have become widely used for knowledge representation, and are considered as foundation for Semantic Web. However with their wide spread usage, a question of their evaluation increased even more. This paper addresses the issue of finding an efficient ontology evaluation method by presenting the existing ontology evaluation techniques, while discussing their advantages and drawbacks. The presented ontology evaluation techniques can be grouped into four categories: gold standard-based, corpus-based, task-based and criteria based approaches.
Conference Paper
Full-text available
During the last three years there has been growing interest and consequently active research on ontology modularization. This paper presents a concrete tool that incorporates an approach to ontology modularization that inherits some of the main principles from object-oriented software engineering, which are encapsulation and information hiding. What motivated us to track that direction is the fact that most ontology approaches to the problem focus on linking ontologies (or modules) rather than building modules that can encapsulate foreign parts of ontologies (or other modules) that can be managed more easily.
Full-text available
The Semantic Web should enable greater access not only to content but also to services on the Web. Users and software agents should be able to discover, invoke, compose, and monitor Web resources offering particular services and having particular properties. As part of the DARPA Agent Markup Language program, we have begun to develop an ontology of services, called DAMLS, that will make these functionalities possible. In this paper we describe the overall structure of the ontology, the service profile for advertising services, and the process model for the detailed description of the operation of services.
Full-text available
. 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.