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A. Gyrard et al.
time and ontologies need to be improved with the set of tools presented in Table 3.
From this experience, we realize the need to disseminate those practices and tools
within the IoT community [18]. In the LOV journal publication [4], note the special
acknowledgment for our dissemination work.
2 Further, there is a common co-author.
Evaluating ontology quality helps decide which ontologies to reuse.
2.3 Development Time Optimization and Ontology
Improvement
Re-using ontologies is highly encouraged by the ontology engineering research community [19]. What the communities urgently need, is an ontology improvement
methodology that assists novice ontology designers in improving and publishing
reusable ontologies. The key benefit is to enhance ontology methodology development [20], with a focus of best practices and help in the learning curve to reduce the
development time. It can be done by integrating existing tools. The main novelty is
to extend our analysis [18] on improving ontologies and automate the process of the
usage of the tools.
2.4 Semantic Ontology Interoperability Methodology
IoT infrastructure requires information sharing for identifying, locating, organizing,
and managing everyday “things”, their services, and resources. The IoT data infrastructure relies on Big Data systems to have enough capacity to store and process the
amount of information collected [21]. SEG 3.0 is a methodology defined to enable
data interoperability across different data systems, software platforms, and applications [22]. SEG 3.0 implements data exchange using an ontological approach and
allowing access to data. PerfectO is a platform designed to reduce the learning
curve to support the SEG 3.0 semantic interoperability. PerfectO (explained in
Sect. 4) focuses on ontological improvement and interoperability.
2.4.1 Semantic Interoperability According to Standards
IERC AC4 (European Research Cluster on the Internet of Things) [15, 23, 24]
highlighted four levels of interoperability: (1) technical, (2) syntactic, (3) semantic,
and (4) organizational. Technical and syntactical interoperability were the main concerns in research and development in recent years. IERC AC4 does not reference
concrete tools encouraging—(i) the best practices, (ii) the use of methodologies to
ensure interoperability among ontology-based IoT applications, and (iii) reuse of the
2 “Thanks to Amelie Gyrard for the help on the project”.
A. Gyrard et al.
time and ontologies need to be improved with the set of tools presented in Table 3.
From this experience, we realize the need to disseminate those practices and tools
within the IoT community [18]. In the LOV journal publication [4], note the special
acknowledgment for our dissemination work.
2 Further, there is a common co-author.
Evaluating ontology quality helps decide which ontologies to reuse.
2.3 Development Time Optimization and Ontology
Improvement
Re-using ontologies is highly encouraged by the ontology engineering research community [19]. What the communities urgently need, is an ontology improvement
methodology that assists novice ontology designers in improving and publishing
reusable ontologies. The key benefit is to enhance ontology methodology development [20], with a focus of best practices and help in the learning curve to reduce the
development time. It can be done by integrating existing tools. The main novelty is
to extend our analysis [18] on improving ontologies and automate the process of the
usage of the tools.
2.4 Semantic Ontology Interoperability Methodology
IoT infrastructure requires information sharing for identifying, locating, organizing,
and managing everyday “things”, their services, and resources. The IoT data infrastructure relies on Big Data systems to have enough capacity to store and process the
amount of information collected [21]. SEG 3.0 is a methodology defined to enable
data interoperability across different data systems, software platforms, and applications [22]. SEG 3.0 implements data exchange using an ontological approach and
allowing access to data. PerfectO is a platform designed to reduce the learning
curve to support the SEG 3.0 semantic interoperability. PerfectO (explained in
Sect. 4) focuses on ontological improvement and interoperability.
2.4.1 Semantic Interoperability According to Standards
IERC AC4 (European Research Cluster on the Internet of Things) [15, 23, 24]
highlighted four levels of interoperability: (1) technical, (2) syntactic, (3) semantic,
and (4) organizational. Technical and syntactical interoperability were the main concerns in research and development in recent years. IERC AC4 does not reference
concrete tools encouraging—(i) the best practices, (ii) the use of methodologies to
ensure interoperability among ontology-based IoT applications, and (iii) reuse of the
2 “Thanks to Amelie Gyrard for the help on the project”.