and classifies a sub-set of tools providing an online interface or a Web service
simple to use, which help to enhance ontologies and synthesize a set of practices.
The eighth chapter was authored by Gianfranco E. Modoni, and Marco Sacco
and focuses on RDF stores that can be used, among others, in IoT deployments.
While multiple RDF stores exist, and may be appropriate and usable for some tasks,
they will not fit others. Moreover, a one-size-fits-all solution is not available and,
likely, will not be delivered. In this context, a methodological approach to evaluate
and rank the relevant functional and non-functional features of the RDF stores is
presented. The proposed approach is to help software architects to select which
RDF stores best fit their application scenario(s).
The following chapter, written by Vitalina Babenko, Igor Shostak, Mariia
Danova, and Olena Feoktystova, deals with creation of ontological knowledge
bases in the Semantic Web. Specifically, various tabular structures are considered as
sources of knowledge, with the main problem being a contradiction between the
wide variety of tabular structures used in knowledge sources and the insufficient
efficiency of classical methods for analyzing sources of this type. The implementation of the theoretical results of the study, in the form of algorithmic, mathematical support, as well as experimental studies conducted to determine the upper
bound and the nature of the growth of complexity of the method of forming the
ontological knowledge bases based on targeted enumeration, has been presented
and confirms the validity of the proposed approach.
The tenth chapter, contributed by Beniamino Di Martino and Antonio Esposito,
is focused on seamless interoperability of sensor-generated data, which is needed to
achieve specific goals. The context of the work is provided by the lack of a universally accepted standard for sensor communications. In the chapter, a prototype
tool for the analysis of sensors’ API tries to overcome the interoperability issues in
a sensor network and provides an instrument to support sensors’ orchestration and
management. The tool aims not only at automatically analyzing the APIs, but also
to derive a semantic representation of them, which can be then used to support the
manual annotation with external domain ontologies.
Finally, the last chapter in this part was written by Pratiyush Guleria and Manu
Sood. Here, authors focus their attention on interoperability in the healthcare sector.
Specifically, they propose to use the foundations of the Semantic Web, in a
three-layered framework for IoT interoperability, and have framed a Web ontology
structure for semantic interoperability in IoT for the healthcare sector. This is
combined with a text analytics model, which performs semantic data classification
on a synthetic healthcare dataset, to predict the patient diagnosis using machine
learning techniques.
The third part of the book deals with semantic IoT in the context of domainspecific applications. The first chapter was contributed by Gaurav Kant
Shankhdhar, Richa Sharma, and Manuj Darbari. Their contribution is focused on
the agriculture/farming industry. The considered solution provides a lightweight
IoT framework, focused on farming in developing countries like India. In this
context, authors have developed a semantically enriched agent-based model
viii
Preface
simple to use, which help to enhance ontologies and synthesize a set of practices.
The eighth chapter was authored by Gianfranco E. Modoni, and Marco Sacco
and focuses on RDF stores that can be used, among others, in IoT deployments.
While multiple RDF stores exist, and may be appropriate and usable for some tasks,
they will not fit others. Moreover, a one-size-fits-all solution is not available and,
likely, will not be delivered. In this context, a methodological approach to evaluate
and rank the relevant functional and non-functional features of the RDF stores is
presented. The proposed approach is to help software architects to select which
RDF stores best fit their application scenario(s).
The following chapter, written by Vitalina Babenko, Igor Shostak, Mariia
Danova, and Olena Feoktystova, deals with creation of ontological knowledge
bases in the Semantic Web. Specifically, various tabular structures are considered as
sources of knowledge, with the main problem being a contradiction between the
wide variety of tabular structures used in knowledge sources and the insufficient
efficiency of classical methods for analyzing sources of this type. The implementation of the theoretical results of the study, in the form of algorithmic, mathematical support, as well as experimental studies conducted to determine the upper
bound and the nature of the growth of complexity of the method of forming the
ontological knowledge bases based on targeted enumeration, has been presented
and confirms the validity of the proposed approach.
The tenth chapter, contributed by Beniamino Di Martino and Antonio Esposito,
is focused on seamless interoperability of sensor-generated data, which is needed to
achieve specific goals. The context of the work is provided by the lack of a universally accepted standard for sensor communications. In the chapter, a prototype
tool for the analysis of sensors’ API tries to overcome the interoperability issues in
a sensor network and provides an instrument to support sensors’ orchestration and
management. The tool aims not only at automatically analyzing the APIs, but also
to derive a semantic representation of them, which can be then used to support the
manual annotation with external domain ontologies.
Finally, the last chapter in this part was written by Pratiyush Guleria and Manu
Sood. Here, authors focus their attention on interoperability in the healthcare sector.
Specifically, they propose to use the foundations of the Semantic Web, in a
three-layered framework for IoT interoperability, and have framed a Web ontology
structure for semantic interoperability in IoT for the healthcare sector. This is
combined with a text analytics model, which performs semantic data classification
on a synthetic healthcare dataset, to predict the patient diagnosis using machine
learning techniques.
The third part of the book deals with semantic IoT in the context of domainspecific applications. The first chapter was contributed by Gaurav Kant
Shankhdhar, Richa Sharma, and Manuj Darbari. Their contribution is focused on
the agriculture/farming industry. The considered solution provides a lightweight
IoT framework, focused on farming in developing countries like India. In this
context, authors have developed a semantically enriched agent-based model
viii
Preface