An Ontology-Based Approach for Representing Medical Recommendations in mHealth Applications

  • Aniello MinutoloEmail author
  • Massimo Esposito
  • Giuseppe De Pietro
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 60)


Nowadays, mHealth applications have been evolving in the form of pervasive solutions for supporting healthy life-style and wellness self-management. In such a direction, the Italian project “Smart Health 2.0” realized innovative technological infrastructures, on which different mHealth applications and services were developed, aimed at remotely supporting individuals in diseases prevention and improving their welfare and life styles. In this paper, the ontology-based approach proposed in the project to represent, share, and reason on the knowledge characterizing a subject within mHealth applications is presented. The proposed approach uses a hybrid strategy integrating ontology models and deductive rules built on the top of them. In order to better describe the proposed approach, a case of application has been presented with respect to an mHealth application designed for managing diet according to given daily caloric needs.


Ontology Logic rules mHealth Knowledge-based systems 



This work has been partially supported by the Italian project “Smart Health 2.0” funded by the Italian Ministry of Education, University, and Research (MIUR).


  1. 1.
    Malvey, D.M., Slovensky, D.J.: mHealth: Transforming Healthcare. Springer, (2014)Google Scholar
  2. 2.
    World Health Organization: mHealth: New horizons for health through mobile technologies. Global Observatory for eHealth Series, vol. 3, Geneva, Switzerland, (2011)Google Scholar
  3. 3.
    Akter, S., et al.: Modelling the impact of mHealth service quality on satisfaction, continuance and quality of life. Behav. Inf. Technol. 32(12), 1225–1241 (2013)CrossRefGoogle Scholar
  4. 4.
    Knight, E., Stuckey, M.I., Petrella, R.J.: Health promotion through primary care: enhancing self-management with activity prescription and mHealth. Phys. Sportsmed. 42(3), 90–99 (2014)CrossRefGoogle Scholar
  5. 5.
    Hamine, S., et al.: Impact of mHealth chronic disease management on treatment adherence and patient outcomes: a systematic review. J. Med. Internet Res. 17(2) (2015)Google Scholar
  6. 6.
    Krummenacher, R., Strang, T.: Ontology-based context modeling. In: Proceedings of the 3rd Workshop on Context Awareness for Proactive Systems, Guildford (2007)Google Scholar
  7. 7.
    Strang, T., Linnhoff-Popien, C.: Context modelling survey. In: Proceedings of the Workshop on Advanced Context Modelling, Reasoning and Management, UbiComp—The Sixth International Conference on Ubiquitous Computing, Nottingham, UK (2004)Google Scholar
  8. 8.
    Bettini, C., et al.: A survey of context modelling and reasoning techniques. Pervasive Mobile Comput. 6(2), 161–180 (2010)CrossRefGoogle Scholar
  9. 9.
    Nadoveza, D., Kiritsis, D.: Ontology-based approach for context modeling in enterprise applications. Comput. Ind. 65(9), 1218–1231 (2014)CrossRefGoogle Scholar
  10. 10.
    RDF/XML Syntax Specification (2004).
  11. 11.
    Ejigu, D., Scuturici, M., Brunie, L.: An ontology-based approach to context modeling and reasoning in pervasive computing. In: Proceedings of the Fifth Annual IEEE International Conference On Pervasive Computing and Communications Workshops, PerCom Workshops’ 07, pp. 14–19 (2007)Google Scholar
  12. 12.
    Wang, X.H., Gu, T., Zhang, D.Q., Pung, H.K.: Ontology based context modeling and reasoning using OWL. In: Proceedings of the Second IEEE Annual Conference on Pervasive Computing and Communications Workshops, pp. 18–22 (2004)Google Scholar
  13. 13.
    Patel-Schneider, P., Hayes, P., Horrocks, I., et al.: OWL web ontology language semantics and abstract syntax. In: W3C Recommendation, vol. 10 (2004).
  14. 14.
    Minutolo, A., Esposito, M., De Pietro, G.: Design and validation of a light-weight reasoning system to support remote health monitoring applications. Eng. Appl. Artif. Intell. 41, 232–248 (2015)CrossRefGoogle Scholar
  15. 15.
    Carroll, J.J., Dickinson, I., Dollin, C., Reynolds, D., Seaborne, A., Wilkinson, K.: Jena: implementing the semantic web recommendations. In: Proceedings of the 13th International World Wide Web conference on Alternate track, New York, USA, pp. 74–83 (2004)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Aniello Minutolo
    • 1
    Email author
  • Massimo Esposito
    • 1
  • Giuseppe De Pietro
    • 1
  1. 1.Institute for High Performance Computing and NetworkingICAR-CNRNaplesItaly

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