208
A. Henaien et al.
17. Kalemi, E., Martiri, E.: FOAF-academic ontology. In: 2011 Third International
Conference on Intelligent Networking and Collaborative Systems, pp. 440–445
(2011). https://doi.org/10.1109/INCoS.2011.94
18. Kassahun, Y., et al.: Automatic classification of epilepsy types using ontologybased and genetics-based machine learning. Artif. Intell. Med. 61(2), 79–88
(2014). https://doi.org/10.1016/j.artmed.2014.03.001. http://www.sciencedirect.
com/science/article/pii/S0933365714000207
19. Kourou, K., Exarchos, T.P., Exarchos, K.P., Karamouzis, M.V., Fotiadis, D.I.:
Machine learning applications in cancer prognosis and prediction. Comput. Struct.
Biotech. J. 13, 8–17 (2015)
20. Lasierra, N., Alesanco, A., Guill´ en, S., Garc´ ıa, J.: A three stage ontology-driven
solution to provide personalized care to chronic patients at home. J. Biomed.
Inform. 46(3), 516–529 (2013). https://doi.org/10.1016/j.jbi.2013.03.006
21. Lezcano, L., Sicilia, M.A., Rodr´ ıguez-Solano, C.: Integrating reasoning and clinical
archetypes using OWL ontologies and SWRL rules. J. Biomed. Inform. 44(2),
343–353 (2011)
22. Liu, D., G¨ orges, M., Jenkins, S.A.: University of Queensland vital signs dataset:
development of an accessible repository of anesthesia patient monitoring data for
research. Anesth. Analg. 114, 584–589 (2012)
23. Liu, J., Li, Y., Tian, X., Sangaiah, A.K., Wang, J.: Towards semantic sensor data:
an ontology approach. Sensors (Switzerland) 19(5), 1–21 (2019). https://doi.org/
10.3390/s19051193
24. Liyanage, H., Krause, P., de Lusignan, S.: Using ontologies to improve semantic
interoperability in health data. BMJ Health Care Inform. 22(2), 309–315 (2015).
https://doi.org/10.14236/jhi.v22i2.159. https://informatics.bmj.com/content/22/
2/309
25. Marcelino, I., Laza, R., Domingues, P., G´ omez-Meire, S., Fdez-Riverola, F.,
Pereira, A.: Active and assisted living ecosystem for the elderly. Sensors 18(4),
1246 (2018)
26. Martin, H., Tsymbal, A., Zillner, S.: Medical ontologies for machine learning and
decision support. US Patent 7,899,764, 1 March 2011
27. Nachabe, L., Girod-genet, M., Hassan, B.E.: Unified data model for wireless sensor
network. IEEE Sens. J. 15(7), 3657–3667 (2015). https://doi.org/10.1109/JSEN.
2015.2393951
28. Ongenae, F., et al.: A probabilistic ontology-based platform for self-learning
context-aware healthcare applications. Expert Syst. Appl. 40(18), 7629–7646
(2013). https://doi.org/10.1016/j.eswa.2013.07.038. http://www.sciencedirect.
com/science/article/pii/S0957417413005174
29. Puri, C.A., Gomadam, K., Jain, P., Yeh, P.Z., Verma, K.: Multiple ontologies in
healthcare information technology: motivations and recommendation for ontology
mapping and alignment. In: ICBO (2011)
30. The University of Queensland: The University of Queensland vital signs dataset.
http://dx.doi.org/102.100.100/6914/
31. Ria˜ no, D., et al.: An ontology-based personalization of health-care knowledge to
support clinical decisions for chronically ill patients. J. Biomed. Inform. 45(3),
429–446 (2012). https://doi.org/10.1016/j.jbi.2011.12.008
32. Serrano, M., Barnaghi, P., Carrez, F., Cousin, P., Vermesan, O., Friess, P.: Internet of Things IoT semantic interoperability: research challenges, best practices,
recommendations and next steps. European Research Cluster on the Internet of
Things, Technical report, IERC (2015)
A. Henaien et al.
17. Kalemi, E., Martiri, E.: FOAF-academic ontology. In: 2011 Third International
Conference on Intelligent Networking and Collaborative Systems, pp. 440–445
(2011). https://doi.org/10.1109/INCoS.2011.94
18. Kassahun, Y., et al.: Automatic classification of epilepsy types using ontologybased and genetics-based machine learning. Artif. Intell. Med. 61(2), 79–88
(2014). https://doi.org/10.1016/j.artmed.2014.03.001. http://www.sciencedirect.
com/science/article/pii/S0933365714000207
19. Kourou, K., Exarchos, T.P., Exarchos, K.P., Karamouzis, M.V., Fotiadis, D.I.:
Machine learning applications in cancer prognosis and prediction. Comput. Struct.
Biotech. J. 13, 8–17 (2015)
20. Lasierra, N., Alesanco, A., Guill´ en, S., Garc´ ıa, J.: A three stage ontology-driven
solution to provide personalized care to chronic patients at home. J. Biomed.
Inform. 46(3), 516–529 (2013). https://doi.org/10.1016/j.jbi.2013.03.006
21. Lezcano, L., Sicilia, M.A., Rodr´ ıguez-Solano, C.: Integrating reasoning and clinical
archetypes using OWL ontologies and SWRL rules. J. Biomed. Inform. 44(2),
343–353 (2011)
22. Liu, D., G¨ orges, M., Jenkins, S.A.: University of Queensland vital signs dataset:
development of an accessible repository of anesthesia patient monitoring data for
research. Anesth. Analg. 114, 584–589 (2012)
23. Liu, J., Li, Y., Tian, X., Sangaiah, A.K., Wang, J.: Towards semantic sensor data:
an ontology approach. Sensors (Switzerland) 19(5), 1–21 (2019). https://doi.org/
10.3390/s19051193
24. Liyanage, H., Krause, P., de Lusignan, S.: Using ontologies to improve semantic
interoperability in health data. BMJ Health Care Inform. 22(2), 309–315 (2015).
https://doi.org/10.14236/jhi.v22i2.159. https://informatics.bmj.com/content/22/
2/309
25. Marcelino, I., Laza, R., Domingues, P., G´ omez-Meire, S., Fdez-Riverola, F.,
Pereira, A.: Active and assisted living ecosystem for the elderly. Sensors 18(4),
1246 (2018)
26. Martin, H., Tsymbal, A., Zillner, S.: Medical ontologies for machine learning and
decision support. US Patent 7,899,764, 1 March 2011
27. Nachabe, L., Girod-genet, M., Hassan, B.E.: Unified data model for wireless sensor
network. IEEE Sens. J. 15(7), 3657–3667 (2015). https://doi.org/10.1109/JSEN.
2015.2393951
28. Ongenae, F., et al.: A probabilistic ontology-based platform for self-learning
context-aware healthcare applications. Expert Syst. Appl. 40(18), 7629–7646
(2013). https://doi.org/10.1016/j.eswa.2013.07.038. http://www.sciencedirect.
com/science/article/pii/S0957417413005174
29. Puri, C.A., Gomadam, K., Jain, P., Yeh, P.Z., Verma, K.: Multiple ontologies in
healthcare information technology: motivations and recommendation for ontology
mapping and alignment. In: ICBO (2011)
30. The University of Queensland: The University of Queensland vital signs dataset.
http://dx.doi.org/102.100.100/6914/
31. Ria˜ no, D., et al.: An ontology-based personalization of health-care knowledge to
support clinical decisions for chronically ill patients. J. Biomed. Inform. 45(3),
429–446 (2012). https://doi.org/10.1016/j.jbi.2011.12.008
32. Serrano, M., Barnaghi, P., Carrez, F., Cousin, P., Vermesan, O., Friess, P.: Internet of Things IoT semantic interoperability: research challenges, best practices,
recommendations and next steps. European Research Cluster on the Internet of
Things, Technical report, IERC (2015)