Semantic IoT Interoperability and Data Analytics Using …
261
with less human intervention for understanding the semantics in a right and unambiguous manner. There is a lot of future scope in the semantic web to automate the
routine tasks. It helps the machine to understand those statements which are having
similar meanings in databases where the data is stored. The ontologies developed
using semantic web represent the knowledge structure for web science which helps in
handling the unstructured data, machine-readable ontologies, developing IoT enabled
devices for different domains, and use of semantic markup in query interfaces.
The IoT based semantic interoperability works on heterogeneous data capable of
removing ambiguity in shared meaning and meaningful dissemination of data across
multiple domains. The semantic interoperability in the IoT domain enhances its
potential for offering value-added services in different sectors of society, especially
in the healthcare sector. With the help of semantic interoperability in the IoT field,
the meaning of the data will be interpreted correctly like a prescription of medicine
to the patient based on the symptoms and exchange of medical history of patients
using IoT devices on different platforms. The semantic web devices complete the
work as per the necessity which saves time and is capable in handling multiple tasks,
but in the present scenario, there is a lot of crime on the internet and connecting
each device with the internet is a big question of security which needs to be carefully
handled.
References
1. Jabbar, S., Ullah, F., Khalid, S., Khan, M., Han, K.: Semantic interoperability in heterogeneous
IoT infrastructure for healthcare. Wirel. Commun. Mob. Comput. (2017)
2. Gomes, P., Cavalcante, E., Batista, T., Taconet, C., Conan, D., Chabridon, S., Pires, P.F.: A
semantic-based discovery service for the Internet of Things. J. Internet Serv. Appl. 10(1), 10
(2019)
3. Berners-Lee, T., Hendler, J., Lassila, O.: The semantic web. Sci. Am. 284(5), 28–37 (2001)
4. Van Ossenbruggen, J., Hardman, L., Rutledge, L.: Hypermedia and the semantic web: a research
agenda. J. Dig. Inform. 3(1) (2002)
5. Maedche, A., Staab, S.: Ontology learning for the semantic web. IEEE Intell. Syst. 16(2), 72–79
(2001)
6. Antoniou, G., Van Harmelen, F.: A Semantic Web Primer. MIT Press (2004)
7. Cambria, E., Hussain, A., Eckl, C.: Bridging the gap between structured and unstructured
healthcare data through semantics and sentics (2011)
8. He, Z., Tao, C., Bian, J., Dumontier, M., Hogan, W.R.: Semantics-powered healthcare
engineering and data analytics (2017)
9. Hendler, J.: Agents and the semantic web. IEEE Intell. Syst. 16(2), 30–37 (2001)
10. Del Carmen Legaz-García, M., Martínez-Costa, C., Menárguez-Tortosa, M., Fernández-Breis,
J.T.: A semantic web based framework for the interoperability and exploitation of clinical
models and EHR data. Knowl. Based Syst. 105, 175–189 (2016)
11. Rahman, F., Bhuiyan, M.Z.A., Ahamed, S.I.: A privacy preserving framework for RFID based
healthcare systems. Future Gener. Comput. Syst. 72, 339–352 (2017)
12. Hossain, M.S., Muhammad, G.: Healthcare big data voice pathology assessment framework.
IEEE Access 4, 7806–7815 (2016)
13. OWL Working Group: OWL 2 web ontology language document overview: W3C recommendation 27 October 2009
261
with less human intervention for understanding the semantics in a right and unambiguous manner. There is a lot of future scope in the semantic web to automate the
routine tasks. It helps the machine to understand those statements which are having
similar meanings in databases where the data is stored. The ontologies developed
using semantic web represent the knowledge structure for web science which helps in
handling the unstructured data, machine-readable ontologies, developing IoT enabled
devices for different domains, and use of semantic markup in query interfaces.
The IoT based semantic interoperability works on heterogeneous data capable of
removing ambiguity in shared meaning and meaningful dissemination of data across
multiple domains. The semantic interoperability in the IoT domain enhances its
potential for offering value-added services in different sectors of society, especially
in the healthcare sector. With the help of semantic interoperability in the IoT field,
the meaning of the data will be interpreted correctly like a prescription of medicine
to the patient based on the symptoms and exchange of medical history of patients
using IoT devices on different platforms. The semantic web devices complete the
work as per the necessity which saves time and is capable in handling multiple tasks,
but in the present scenario, there is a lot of crime on the internet and connecting
each device with the internet is a big question of security which needs to be carefully
handled.
References
1. Jabbar, S., Ullah, F., Khalid, S., Khan, M., Han, K.: Semantic interoperability in heterogeneous
IoT infrastructure for healthcare. Wirel. Commun. Mob. Comput. (2017)
2. Gomes, P., Cavalcante, E., Batista, T., Taconet, C., Conan, D., Chabridon, S., Pires, P.F.: A
semantic-based discovery service for the Internet of Things. J. Internet Serv. Appl. 10(1), 10
(2019)
3. Berners-Lee, T., Hendler, J., Lassila, O.: The semantic web. Sci. Am. 284(5), 28–37 (2001)
4. Van Ossenbruggen, J., Hardman, L., Rutledge, L.: Hypermedia and the semantic web: a research
agenda. J. Dig. Inform. 3(1) (2002)
5. Maedche, A., Staab, S.: Ontology learning for the semantic web. IEEE Intell. Syst. 16(2), 72–79
(2001)
6. Antoniou, G., Van Harmelen, F.: A Semantic Web Primer. MIT Press (2004)
7. Cambria, E., Hussain, A., Eckl, C.: Bridging the gap between structured and unstructured
healthcare data through semantics and sentics (2011)
8. He, Z., Tao, C., Bian, J., Dumontier, M., Hogan, W.R.: Semantics-powered healthcare
engineering and data analytics (2017)
9. Hendler, J.: Agents and the semantic web. IEEE Intell. Syst. 16(2), 30–37 (2001)
10. Del Carmen Legaz-García, M., Martínez-Costa, C., Menárguez-Tortosa, M., Fernández-Breis,
J.T.: A semantic web based framework for the interoperability and exploitation of clinical
models and EHR data. Knowl. Based Syst. 105, 175–189 (2016)
11. Rahman, F., Bhuiyan, M.Z.A., Ahamed, S.I.: A privacy preserving framework for RFID based
healthcare systems. Future Gener. Comput. Syst. 72, 339–352 (2017)
12. Hossain, M.S., Muhammad, G.: Healthcare big data voice pathology assessment framework.
IEEE Access 4, 7806–7815 (2016)
13. OWL Working Group: OWL 2 web ontology language document overview: W3C recommendation 27 October 2009