PerfectO: An Online Toolkit for Improving Quality …
167
domain knowledge already designed within ontologies. For this reason, PerfectO
provides a set of concrete tools to encourage semantic interoperability and reuse
improved ontologies. Standardizations are demonstrating the need to help ontology developers, and ontology quality to achieve semantic interoperability. AIOTI
Working Group 3 is dedicated to IoT standardization and has confirmed that one
of the most important topics are the semantic interoperability.
3 OneM2M, an international standard for IoT and Machine to Machine (M2M), is looking for the
best IoT semantic interoperability practices as well [25]. Semantic Interoperability for the Web of Things White paper highlights the main interoperability issues
[26] and citing our research work as a baseline. Furthermore, in October 2019, two
“Semantic Interoperability for IoT” White Papers [27, 28] have been released and
disseminated by ETSI, W3C, AOITI, etc. to guide IoT and standard developers (also
from OneM2M and ISO) to reuse and develop semantics-based IoT applications
easily.
3 Related Work
We review the literature in this section which is summarized in Table 1. The literature review has been introduced earlier when the Catalogs of Tools depicted in Fig.
9 has been described in Sect. 6. Existing surveys are classified in Sect. 3.1, ontology methodology in Sect. 3.2, ontology evaluation in Sect. 3.3, ontology metrics
in Sect. 3.4, and ontology quality in Sect. 3.5. Finally, the main limitations of the
related work are highlighted in Sect. 3.6.
3.1 Existing Surveys
Existing surveys are covering complementary research topics for ontology quality,
evaluation, ranking, metrics, usability, and methodologies that are reviewed within
this section and summarized within Table 1.
McDaniel et al. [9] provide a set of metrics to evaluate ontologies, but it can
be challenging to implement them. For instance, the recognition metric computes
the number of times the ontology is downloaded which is not provided by catalogs
such as LOV and LOV4IoT. A second example is the lack of explanations for the
metric consistency implementation. Reasoners can explain if the consistency metric is satisfied but do not provide an explicit number. McDaniel et al. design the
Domain Ontology Ranking System (DoORS) prototype [9] to query ontology catalogs with specific keywords and assess ontology quality. Metrics are implemented to
automate the selection of the ontology within the prototype. The DoORS prototype
provides the following quality assessment metric modules: (1) Syntactic: Quality,
3 https://ec.europa.eu/digital-single-market/en/alliance-internet-things-innovation-aioti.
167
domain knowledge already designed within ontologies. For this reason, PerfectO
provides a set of concrete tools to encourage semantic interoperability and reuse
improved ontologies. Standardizations are demonstrating the need to help ontology developers, and ontology quality to achieve semantic interoperability. AIOTI
Working Group 3 is dedicated to IoT standardization and has confirmed that one
of the most important topics are the semantic interoperability.
3 OneM2M, an international standard for IoT and Machine to Machine (M2M), is looking for the
best IoT semantic interoperability practices as well [25]. Semantic Interoperability for the Web of Things White paper highlights the main interoperability issues
[26] and citing our research work as a baseline. Furthermore, in October 2019, two
“Semantic Interoperability for IoT” White Papers [27, 28] have been released and
disseminated by ETSI, W3C, AOITI, etc. to guide IoT and standard developers (also
from OneM2M and ISO) to reuse and develop semantics-based IoT applications
easily.
3 Related Work
We review the literature in this section which is summarized in Table 1. The literature review has been introduced earlier when the Catalogs of Tools depicted in Fig.
9 has been described in Sect. 6. Existing surveys are classified in Sect. 3.1, ontology methodology in Sect. 3.2, ontology evaluation in Sect. 3.3, ontology metrics
in Sect. 3.4, and ontology quality in Sect. 3.5. Finally, the main limitations of the
related work are highlighted in Sect. 3.6.
3.1 Existing Surveys
Existing surveys are covering complementary research topics for ontology quality,
evaluation, ranking, metrics, usability, and methodologies that are reviewed within
this section and summarized within Table 1.
McDaniel et al. [9] provide a set of metrics to evaluate ontologies, but it can
be challenging to implement them. For instance, the recognition metric computes
the number of times the ontology is downloaded which is not provided by catalogs
such as LOV and LOV4IoT. A second example is the lack of explanations for the
metric consistency implementation. Reasoners can explain if the consistency metric is satisfied but do not provide an explicit number. McDaniel et al. design the
Domain Ontology Ranking System (DoORS) prototype [9] to query ontology catalogs with specific keywords and assess ontology quality. Metrics are implemented to
automate the selection of the ontology within the prototype. The DoORS prototype
provides the following quality assessment metric modules: (1) Syntactic: Quality,
3 https://ec.europa.eu/digital-single-market/en/alliance-internet-things-innovation-aioti.