Semantic Modelling of Smart Healthcare Data

  • Roberto Reda
  • Filippo Piccinini
  • Antonella CarbonaroEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 869)


Nowadays, healthcare is becoming increasingly connected and increasingly complex. These changes provide opportunities and challenges to the research community. For instance, the enormous volume of data gathered from IoT wearable fitness devices and wellness appliances, if effectively analysed and understood, can be exploited to improve people’s well-being and identify predictive markers of future diseases. However, due to the lack of devices interoperability and heterogeneity of data representation formats, the IoT healthcare landscape is characterised by a pervasive presence of “data silos" which prevents users and health practitioners from obtaining an overall view of whole knowledge. Semantic web technologies, such as ontologies and inference rules have been shown as a promising way for the integration and exploitation of data from heterogeneous sources. In this paper, we present a semantic data model useful to: (a) analyse information from unstructured data sources along with generic or domain specific datasets; (b) unify them in an interlinked data processing area. The proposed semantic eHealth system enables automatic inferences and logical reasoning, and can significantly facilitate reuse, exploitation and possible extension of IoT health data sources.


Internet of Things Healthcare data Ontologies Semantic web technologies Reasoning 


  1. 1.
    Islam, S.R., Kwak, D., Kabir, M.H., Hossain, M., Kwak, K.-S.: The internet of things for health care: a comprehensive survey. IEEE Access 3, 678–708 (2015)CrossRefGoogle Scholar
  2. 2.
    Kim, J., Lee, J.-W.: OpenIoT: an open service framework for the internet of things. In: 2014 IEEE World Forum on Internet of Things (WF-IoT), pp. 89–93. IEEE (2014)Google Scholar
  3. 3.
    Mendoza, J.A., Baker, K.S., Moreno, M.A., Whitlock, K., Abbey-Lambertz, M., Waite, A., Colburn, T., Chow, E.J.: A fitbit and facebook mHealth intervention for promoting physical activity among adolescent and young adult childhood cancer survivors: a pilot study (2017)Google Scholar
  4. 4.
    Sun, J., Reddy, C.K.: Big data analytics for healthcare. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1525–1525. ACM (2013)Google Scholar
  5. 5.
    Horrocks, I., Patel-Schneider, P.F., Van Harmelen, F.: From SHIQ and RDF to owl: the making of a web ontology language. Web Semant. Sci. Serv. Agents World Wide Web 1(1), 7–26 (2003)CrossRefGoogle Scholar
  6. 6.
    iOS - Health - Apple: Accessed 21 Dec 2017
  7. 7.
    Google Fit: Accessed 21 Dec 2017
  8. 8.
    Gay, V., Leijdekkers, P.: Bringing health and fitness data together for connected health care: mobile apps as enablers of interoperability. J. Med. Internet Res. 17(11), e260 (2015)CrossRefGoogle Scholar
  9. 9.
    Healthvault - Microsoft : Accessed 27 Mar 2018
  10. 10.
    Kim, H.H., Lee, S.Y., Baik, S.Y., Kim, J.H.: Mello: medical lifelog ontology for data terms from self-tracking and lifelog devices. Int. J. Med. Inf. 84(12), 1099–1110 (2015)CrossRefGoogle Scholar
  11. 11.
    Horrocks, I., Patel-Schneider, P.F., Boley, H., Tabet, S., Grosof, B., Dean, M., et al.: SWRL: a semantic web rule language combining OWL and RuleML. W3C Member submission, vol. 21, p. 79 (2004)Google Scholar
  12. 12.
    Noy, N.F.: Semantic integration: a survey of ontology-based approaches. ACM Sigmod Rec. 33(4), 65–70 (2004)CrossRefGoogle Scholar
  13. 13.
    Barnaghi, P., Cousin, P., Maló, P., Serrano, M., Viho, C.: Simpler IoT word (s) of tomorrow, more interoperability challenges to cope today. In: River Publishers Series in Communications, p. 277 (2013)Google Scholar
  14. 14.
    Carbonaro, A.: Towards an automatic forum summarization to support tutoring. In: Technology Enhanced Learning. Quality of Teaching and Educational Reform, pp. 141–147 (2010)Google Scholar
  15. 15.
    Henze, N., Dolog, P., Nejdl, W.: Reasoning and ontologies for personalized e-learning in the semantic web. J. Educ. Technol. Soc. 7(4), 82–97 (2004)Google Scholar
  16. 16.
    Carbonaro, A.: Collaborative and semantic information retrieval for technology-enhanced learning. In: Proceedings of the 3rd International Workshop on Social Information Retrieval for Technology-Enhanced Learning (SIRTEL 2009), Aachen, Germany (2009)Google Scholar
  17. 17.
    Carbonaro, A.: Improving web search and navigation using summarization process. In: World Summit on Knowledge Society. Springer, Berlin, pp. 131–138 (2010)CrossRefGoogle Scholar
  18. 18.
    Carbonaro, A.: Wordnet-based summarization to enhance learning interaction tutoring. J. e-Learning Knowl. Soc. 6(2), 67–74 (2010)Google Scholar
  19. 19.
    Carbonaro, A., Ferrini, R.: Personalized information retrieval in a semantic-based learning environment. In: Social Information Retrieval Systems, pp. 270–288 (2007)Google Scholar
  20. 20.
    Riccucci, S., Carbonaro, A., Casadei, G.: An architecture for knowledge management in intelligent tutoring system. In: CELDA, pp. 473–476 (2005)Google Scholar
  21. 21.
    Jara, A.J., Olivieri, A.C., Bocchi, Y., Jung, M., Kastner, W., Skarmeta, A.F.: Semantic web of things: an analysis of the application semantics for the iot moving towards the iot convergence. Int. J. Web Grid Serv. 10(2–3), 244–272 (2014)CrossRefGoogle Scholar
  22. 22.
    Pfisterer, D., Romer, K., Bimschas, D., Kleine, O., Mietz, R., Truong, C., Hasemann, H., Kröller, A., Pagel, M., Hauswirth, M.: Spitfire: toward a semantic web of things. IEEE Commun. Mag. 49(11), 40–48 (2011)CrossRefGoogle Scholar
  23. 23.
    Dimou, A., Vander Sande, M., Colpaert, P.,Verborgh, R., Mannens, E., Van de Walle, R.: RML: a generic language for integrated RDF mappings of heterogeneous data. In: LDOW (2014)Google Scholar
  24. 24.
    Amardeilh, F.: Semantic annotation and ontology population. In: Semantic Web Engineering in the Knowledge Society, p. 424 (2008)Google Scholar
  25. 25.
    Manola, F., Miller, E., McBride, B.: RDF primer W3C recommendation 10 February 2004 (2004)Google Scholar
  26. 26.
    O’Connor, M. J., Das, A. K.: A lightweight model for representing and reasoning with temporal information in biomedical ontologies. In: HEALTHINF, pp. 90–97 (2010)Google Scholar
  27. 27.
    Allen, J.F.: Maintaining knowledge about temporal intervals. Commun. ACM 26(11), 832–843 (1983)CrossRefGoogle Scholar
  28. 28.
    Dyer, A., Berkson, D., Stamler, J., Lindberg, H., Stevens, E.: High blood-pressure: a risk factor for cancer mortality? Lancet 305(7915), 1051–1056 (1975)CrossRefGoogle Scholar
  29. 29.
    Quail, D.F., Olson, O.C., Bhardwaj, P., Walsh, L.A., Akkari, L., Quick, M.L., Chen, I.-C., Wendel, N., Ben-Chetrit, N., Walker, J.: Obesity alters the lung myeloid cell landscape to enhance breast cancer metastasis through IL5 and GM-CSF. Nat. Cell Biol. 19(8), 974 (2017)CrossRefGoogle Scholar
  30. 30.
    Robinson, L.E., Holt, T.A., Rees, K., Randeva, H.S., O’Hare, J.P.: Effects of exenatide and liraglutide on heart rate, blood pressure and body weight: systematic review and meta-analysis. BMJ Open 3(1), e001986 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Roberto Reda
    • 1
  • Filippo Piccinini
    • 2
  • Antonella Carbonaro
    • 3
    Email author
  1. 1.Master’s Degree in Computer Science and EngineeringUniversity of BolognaBolognaItaly
  2. 2.Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) S.r.l. IRCCS, Oncology Research HospitalMeldola (FC)Italy
  3. 3.Department of Computer Science and EngineeringUniversity of BolognaBolognaItaly

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