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P. Guleria and M. Sood
and historical events. In the semantic web, knowledge is the source for intelligent
systems and ontologies support the tasks for it. The ontology is conceptualization i.e.
knowledge base of a particular domain. In Artificial Intelligence, an agent performs
the work of communication for which it uses the structure from some ontology. The
Semantic web uses the term ontology which is in a simple term known as vocabulary.
Ontologies help in organizing knowledge and the inference techniques are used on
the Semantic web, which involves relationships. These techniques play an important role in the healthcare sector where patient symptoms, diagnosis, and treatments
are prescribed using ontologies. The knowledge acquired using these terms helps in
building intelligent decision support systems to predict the diagnosis. Another area of
research includes social networking websites like language use on Facebook, Twitter,
and Instagram, etc. The ontologies use linked data techniques to create an Intelligent
Semantic system. In the semantic web, there are 4 techniques of ontologies defined.
These are as follows: (a) RDF Schemes, (b) Simple knowledge organization system
(SKOS), (c) Web ontology language (OWL), (d) Rule Interchange Format (RIF).
IoT semantic web uses ontologies which help in the medical field. E.g. the ontologies generate the vocabulary for problems like blood pressure and its correlated
medicine i.e. aspirin to take immediate action in such a situation. In the present
scenario, the IoT enabled devices are ontology processed. The technology is not
limited only to smartphones, smart watches; this place is now taken by IoT. IoT is
just like networking which connects gadgets and electronic devices for information
dissemination and utilization. IoT is a concept where all devices which run in on
and off state, now after IoT works by medium of the Internet. Here the information
dissemination and intelligence come with web ontologies. The IoT technology is
also known as non-screen computing where devices work like computers but with
no screen in front of them. In IoT, there are smart objects with sensing competence,
embedded recognition through RFID tags. With the help of semantic IoT, there
is a unification of sensors, RFID tags, and communication. In Semantic IoT, the
resource description framework provides semantic inoperability using different IoT
devices. The semantic interoperability helps in effective and meaningful communication which is economical and ensures faster decision-making [1]. As the IoT
devices generate heterogeneous data, there is a need for such information that contains
unambiguous information of IoT resources. The information obtained facilitates data
access, semantic interpretation, and knowledge extraction [2].
The introduction of the chapter is followed with the literature review in Sect. 2.
The layered framework of interoperability in IoT is discussed in Sect. 3. Section 4
covers the methodology for classifying semantic data using Machine learning. The
paper is concluded in Sect. 5 followed with references at the end.
2 Literature Review
Authors in [3] have discussed in detail about the semantic web. The semantic web
provides proper format to the relevant information of web pages with the help of
P. Guleria and M. Sood
and historical events. In the semantic web, knowledge is the source for intelligent
systems and ontologies support the tasks for it. The ontology is conceptualization i.e.
knowledge base of a particular domain. In Artificial Intelligence, an agent performs
the work of communication for which it uses the structure from some ontology. The
Semantic web uses the term ontology which is in a simple term known as vocabulary.
Ontologies help in organizing knowledge and the inference techniques are used on
the Semantic web, which involves relationships. These techniques play an important role in the healthcare sector where patient symptoms, diagnosis, and treatments
are prescribed using ontologies. The knowledge acquired using these terms helps in
building intelligent decision support systems to predict the diagnosis. Another area of
research includes social networking websites like language use on Facebook, Twitter,
and Instagram, etc. The ontologies use linked data techniques to create an Intelligent
Semantic system. In the semantic web, there are 4 techniques of ontologies defined.
These are as follows: (a) RDF Schemes, (b) Simple knowledge organization system
(SKOS), (c) Web ontology language (OWL), (d) Rule Interchange Format (RIF).
IoT semantic web uses ontologies which help in the medical field. E.g. the ontologies generate the vocabulary for problems like blood pressure and its correlated
medicine i.e. aspirin to take immediate action in such a situation. In the present
scenario, the IoT enabled devices are ontology processed. The technology is not
limited only to smartphones, smart watches; this place is now taken by IoT. IoT is
just like networking which connects gadgets and electronic devices for information
dissemination and utilization. IoT is a concept where all devices which run in on
and off state, now after IoT works by medium of the Internet. Here the information
dissemination and intelligence come with web ontologies. The IoT technology is
also known as non-screen computing where devices work like computers but with
no screen in front of them. In IoT, there are smart objects with sensing competence,
embedded recognition through RFID tags. With the help of semantic IoT, there
is a unification of sensors, RFID tags, and communication. In Semantic IoT, the
resource description framework provides semantic inoperability using different IoT
devices. The semantic interoperability helps in effective and meaningful communication which is economical and ensures faster decision-making [1]. As the IoT
devices generate heterogeneous data, there is a need for such information that contains
unambiguous information of IoT resources. The information obtained facilitates data
access, semantic interpretation, and knowledge extraction [2].
The introduction of the chapter is followed with the literature review in Sect. 2.
The layered framework of interoperability in IoT is discussed in Sect. 3. Section 4
covers the methodology for classifying semantic data using Machine learning. The
paper is concluded in Sect. 5 followed with references at the end.
2 Literature Review
Authors in [3] have discussed in detail about the semantic web. The semantic web
provides proper format to the relevant information of web pages with the help of