Semantic Description of Healthcare Devices to Enable Data Integration

  • Antonella CarbonaroEmail author
  • Filippo Piccinini
  • Roberto Reda
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 738)


With the blooming of data created for example by IoT devices, the possibility to handle all information coming from healthcare applications is becoming increasingly challenging. Cognitive computing systems can be used to analyse large information volume by providing insights and recommendations to represent, access, integrate, and investigate data in order to improve outcomes across many domains, including healthcare. This paper presents an ontology-based system for the eHealth domain. It provides semantic interoperability among heterogeneous IoT devices and facilitates data integration and sharing. The novelty of the proposed approach lies in exploiting semantic web technologies to explicitly describe the meaning of sensor data and define a common communication strategy for information representation and exchange.


eHealth Semantic web technologies Ontology-based representation IoT Cognitive computing 


  1. 1.
    J.A. Mendoza, K.S. Baker, M.A. Moreno, K. Whitlock, M. Abbey-Lambertz, A. Waite, T. Colburn, E.J. Chow, Fitbit and Facebook mHealth intervention for promoting physical activity among adolescent and young adult childhood cancer survivors: a pilot study. Pediatr. Blood Cancer 64(12) (2017) Google Scholar
  2. 2.
    S.R. Islam, D. Kwak, M.H. Kabir, M. Hossain, K.-S. Kwak, The internet of things for health care: a comprehensive survey. IEEE Access 3, 678–708 (2015)CrossRefGoogle Scholar
  3. 3.
    J. Kim, J.W. Lee, OpenIoT: an open service framework for the internet of things, in IEEE World Forum on Internet of Things (2014)Google Scholar
  4. 4.
    J. Sun, C.K. Reddy, Big data analytics for healthcare, in Tutorial Presentation at the SIAM International Conference on Data Mining (Texas, USA, 2013), p. 327Google Scholar
  5. 5.
    S. Riccucci, A. Carbonaro, G. Casadei, An architecture for knowledge management in intelligent tutoring system, in IADIS International Conference on Cognition and Exploratory Learning in Digital Age, CELDA (2005), pp. 473–476Google Scholar
  6. 6.
    J. Fox, Cognitive systems at the point of care: The CREDO program. J. Biomed. Inform. 68, 83–95 (2017)CrossRefGoogle Scholar
  7. 7.
    A. Carbonaro, V. Maniezzo, M. Roccetti, P. Salomoni, Modelling the student in Pitagora 2.0. User Model. User-Adap. Inter. 4(4), 233–251 (1994)CrossRefGoogle Scholar
  8. 8.
    A. Carbonaro, P. Zingaretti, Object tracking in a varying environment, in IEEE Conference Publication Issue 443 pt 1. (1997), pp. 229–233Google Scholar
  9. 9.
    A. Carbonaro, P. Zingaretti, A comprehensive approach to image-contrast enhancement, in Proceedings—International Conference on Image Analysis and Processing, Article number 797602 (1999), pp. 241–246Google Scholar
  10. 10.
    R. Fang, S. Pouyanfar, Y. Yang, S.-C. Chen, S.S. Iyengar, Computational health informatics in the Big Data Age: a survey. ACM Comput. Surv 49(1), Article 12 (2016)CrossRefGoogle Scholar
  11. 11.
    A.G. Patel, S.K. Datta, M.I. Ali, SWoTSuite: a toolkit for prototyping end-to-end semantic web of things applications, in Proceedings of the 26th International Conference on World Wide Web Companion (2017), pp. 263–267Google Scholar
  12. 12.
    A. Carbonaro, Towards an automatic forum summarization to support tutoring, in Technology Enhanced Learning: Quality of Teaching and Educational Reform, (Springer, Berlin, Heidelberg, 2010), pp. 141–147CrossRefGoogle Scholar
  13. 13.
    N. Henze, P. Dolog, W. Nejdl, Reasoning and ontologies for personalized e-learning in the semantic web. Educ. Technol. Soc. 7(4), 82–97 (2004)Google Scholar
  14. 14.
    A. Andronico, A. Carbonaro, L. Colazzo, A. Molinari, M. Ronchetti, A. Trifonova, Designing models and services for learning management systems in mobile settings, in Workshop on Mobile and Ubiquitous Information Access, (Springer, Berlin, 2003)Google Scholar
  15. 15.
    A. Andronico, A. Carbonaro, L. Colazzo, A. Molinari, Personalisation services for learning management systems in mobile settings. Int. J. Contin. Eng. Educ. Life Long Learn 14(4–5), 353–369 (2004)CrossRefGoogle Scholar
  16. 16.
    Carbonaro A., Defining personalized learning views of relevant learning objects in a collaborative bookmark management system, in Web-Based Intelligent ELearning Systems: Technologies and Applications, ed. by Z. Ma (Information Science Publishing, Hershey, PA) , 2006, pp. 139–155Google Scholar
  17. 17.
    N.F. Noy, D.L. McGuinness, Ontology development 101: a guide to creating your first ontology. Stanford Knowledge Systems Laboratory Technical Report KSL-01-05 (2001)Google Scholar
  18. 18.
    Shearer R., B. Motik, I. Horrocks, Hermit: a highly-efficient owl reasoner, in OWLED, vol. 432 (2008), p 91Google Scholar
  19. 19.
    F. Amardeilh, Semantic annotation and ontology population, in Semantic Web Engineering in the Knowledge Society (2008), p.424Google Scholar
  20. 20.
    F. Manola, E. Miller, B. McBride, RDF primer. W3C Recommend. 10(1–107), 6 (2004)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Antonella Carbonaro
    • 1
    Email author
  • Filippo Piccinini
    • 2
  • Roberto Reda
    • 1
  1. 1.Department of 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 HospitalMeldolaItaly

Personalised recommendations