Semantic IoT Interoperability and Data Analytics Using …
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agents. Here, the information is having a decisive meaning. The two important technologies that have been developed for the semantic web are as follows: (a) XML, (b)
RDF (Resource Description Framework). The Semantic web enables the machine
to understand the semantic data. In the semantic web, another role is played by
ontologies that find the synonym vocabulary which is having similar meaning e.g.
“address” can also have synonym “location” in databases. Apart from the Semantic
Web, there is need to develop knowledge representation techniques using artificial
techniques such that meaningful information can be extracted from large data. On
the contrary, knowledge representation and annotation languages have been developed using web infrastructure like HTML. The tags used in HTML gives the shape
to domain-specific language to a machine processed form. The RDF Frameworks
uses the ontology for providing semantic structure to an application [4]. In [5],
authors have discussed ontology learning for the web which structures the data
into a machine-understandable form. The semantic analysis helps in information
synthesis whereas ontology results in knowledge representation. The main motive of
the Semantic web is to give machine accessible information. In the Semantic web,
there is a need for knowledge management but along with it the limitations which
need to be handled are as follows: (a) keyword-based search, (b) extracting meaningful information and its maintenance, (c) deliver the information in the user and
human-friendly manner, (d) effective use of ontologies for a shared understanding
of a particular domain, (e) removal of ambiguities in terminologies i.e. to specify the
terminology having similar understanding and meaning [6].
Authors [7] have explored the semantics and sentics related to healthcare for
bridging the gap between structured and unstructured data. The framework proposed
by the authors considers the patients’ opinion on the web and health care providers to
deliver improved services to the end-user. The innovative semantics-based methods
help in addressing the healthcare problems which includes the effective use of
biomedical vocabulary, semantic web technologies to extract meaningful information
in biomedical and health data. The biomedical ontologies provide efficient domain
knowledge to support data similarity and interoperability in a variety of healthcare
information systems such as EHR, healthcare administration, and clinical decision
support [8]. In [9], authors have stressed the role of agent-based systems and ontologies in the web world. With the help of agents and ontologies, there will be an
effective use of programs to perform tasks with less human intervention. The author
[10] has proposed a framework for handling clinical models through Semantic Web
technology. The data source used for the framework is Electronic Health Records.
The Analytical capability of the framework is ontology building through OWL.
In the semantic web, the patient medical records, history, medicines prescribed
are represented in Web ontology language. The data in web ontology language
form will be helpful for data-driven computing aid for personalized health maintenance. With the help of Intelligent Semantic Analytics, the medical records of
patients can be retrieved taking symptoms in the user input query. Rahman et al. [11]
have proposed a framework to maintain confidentiality in RFID based healthcare
systems. In this framework, the data is generated from RFID tags. Semantic Web
Analytics can help in the classification of Healthcare data for handling unstructured
247
agents. Here, the information is having a decisive meaning. The two important technologies that have been developed for the semantic web are as follows: (a) XML, (b)
RDF (Resource Description Framework). The Semantic web enables the machine
to understand the semantic data. In the semantic web, another role is played by
ontologies that find the synonym vocabulary which is having similar meaning e.g.
“address” can also have synonym “location” in databases. Apart from the Semantic
Web, there is need to develop knowledge representation techniques using artificial
techniques such that meaningful information can be extracted from large data. On
the contrary, knowledge representation and annotation languages have been developed using web infrastructure like HTML. The tags used in HTML gives the shape
to domain-specific language to a machine processed form. The RDF Frameworks
uses the ontology for providing semantic structure to an application [4]. In [5],
authors have discussed ontology learning for the web which structures the data
into a machine-understandable form. The semantic analysis helps in information
synthesis whereas ontology results in knowledge representation. The main motive of
the Semantic web is to give machine accessible information. In the Semantic web,
there is a need for knowledge management but along with it the limitations which
need to be handled are as follows: (a) keyword-based search, (b) extracting meaningful information and its maintenance, (c) deliver the information in the user and
human-friendly manner, (d) effective use of ontologies for a shared understanding
of a particular domain, (e) removal of ambiguities in terminologies i.e. to specify the
terminology having similar understanding and meaning [6].
Authors [7] have explored the semantics and sentics related to healthcare for
bridging the gap between structured and unstructured data. The framework proposed
by the authors considers the patients’ opinion on the web and health care providers to
deliver improved services to the end-user. The innovative semantics-based methods
help in addressing the healthcare problems which includes the effective use of
biomedical vocabulary, semantic web technologies to extract meaningful information
in biomedical and health data. The biomedical ontologies provide efficient domain
knowledge to support data similarity and interoperability in a variety of healthcare
information systems such as EHR, healthcare administration, and clinical decision
support [8]. In [9], authors have stressed the role of agent-based systems and ontologies in the web world. With the help of agents and ontologies, there will be an
effective use of programs to perform tasks with less human intervention. The author
[10] has proposed a framework for handling clinical models through Semantic Web
technology. The data source used for the framework is Electronic Health Records.
The Analytical capability of the framework is ontology building through OWL.
In the semantic web, the patient medical records, history, medicines prescribed
are represented in Web ontology language. The data in web ontology language
form will be helpful for data-driven computing aid for personalized health maintenance. With the help of Intelligent Semantic Analytics, the medical records of
patients can be retrieved taking symptoms in the user input query. Rahman et al. [11]
have proposed a framework to maintain confidentiality in RFID based healthcare
systems. In this framework, the data is generated from RFID tags. Semantic Web
Analytics can help in the classification of Healthcare data for handling unstructured