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Automatic Mapping of Motivational Text Messages into Ontological Entities for Smart Coaching Applications

  • Claudia Villalonga
  • Harm op den Akker
  • Hermie Hermens
  • Luis Javier Herrera
  • Hector Pomares
  • Ignacio Rojas
  • Olga Valenzuela
  • Oresti Banos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10586)

Abstract

Unwholesome lifestyles can reduce lifespan by several years or even decades. Therefore, raising awareness and promoting healthier behaviors prove essential to revert this dramatic panorama. Virtual coaching systems are at the forefront of digital solutions to educate people and procure a more effective health self-management. Despite their increasing popularity, virtual coaching systems are still regarded as entertainment applications with an arguable efficacy for changing behaviors, since messages can be perceived to be boring, unpersonalized and can become repetitive over time. In fact, messages tend to be quite general, repetitive and rarely tailored to the specific needs, preferences and conditions of each user. In the light of these limitations, this work aims at help building a new generation of methods for automatically generating user-tailored motivational messages. While the creation of messages is addressed in a previous work, in this paper the authors rather present a method to automatically extract the semantics of motivational messages and to create the ontological representation of these messages. The method uses first natural language processing to perform a linguistic analysis of the message. The extracted information is then mapped to the concepts of the motivational messages ontology. The proposed method could boost the quantity and diversity of messages by automatically mining and parsing existing messages from the internet or other digitised sources, which can be later tailored according to the specific needs and particularities of each user.

Keywords

Ontology Natural language processing Motivational messages Smart coaching 

Notes

Acknowledgments

This work was supported by Project TIN2015-71873-R (Spanish Ministry of Economy and Competitiveness -MINECO- and the European Regional Development Fund -ERDF).

References

  1. 1.
    Apache Jena: https://jena.apache.org/. Accessed 13 July 2017
  2. 2.
  3. 3.
    Protégé: http://protege.stanford.edu/. Accessed 13 July 2017
  4. 4.
    op den Akker, H., Cabrita, M., op den Akker, R., Jones, V.M., Hermens, H.J.: Tailored motivational message generation: a model and practical framework for real-time physical activity coaching. J. Biomed. Inf. 55, 104–115 (2015). http://www.sciencedirect.com/science/article/pii/S1532046415000489
  5. 5.
    Banos, O., Bilal Amin, M., Ali Khan, W., Afzal, M., Hussain, M., Kang, B.H., Lee, S.: The mining minds digital health and wellness framework. Biomed. Eng. Online 15(1), 165–186 (2016). http://dx.doi.org/10.1186/s12938-016-0179-9
  6. 6.
    Buitelaar, P., Olejnik, D., Sintek, M.: A protg plug-in for ontology extraction from text based on linguistic analysis. In: The Semantic Web: Research and Applications. Proceedings of the 1st European Semantic Web Symposium (ESWS04). pp. 31–44. Springer (2004)Google Scholar
  7. 7.
    Cimiano, P., Völker, J.: Text2Onto. In: Montoyo, A., Muńoz, R., Métais, E. (eds.) NLDB 2005. LNCS, vol. 3513, pp. 227–238. Springer, Heidelberg (2005). doi: 10.1007/11428817_21 CrossRefGoogle Scholar
  8. 8.
    Cyganiak, R., Wood, D., Lanthaler, M.: RDF 1.1 Concepts and Abstract Syntax. W3C Recommendation. https://www.w3.org/TR/rdf11-concepts/. Accessed 25 Feb 2014
  9. 9.
    Erriquez, E., Grasso, F.: Generation of personalised advisory messages: an ontology based approach. In: 2008 21st IEEE International Symposium on Computer-Based Medical Systems, pp. 437–442 (2008)Google Scholar
  10. 10.
    Gerdes, M., Martinez, S.G., Tjondronegoro, D.: Conceptualization of a personalized ecoach for wellness promotion. In: 11th EAI International Conference on Pervasive Computing Technologies for Healthcare (2017)Google Scholar
  11. 11.
    Harris, S., Seaborne, A.: SPARQL 1.1 (SPARQL Query Language for RDF). W3C Recommendation. http://www.w3.org/TR/sparql11-query/. Accessed 21 Mar 2013
  12. 12.
    Hazman, M., El-Beltagy, S.R., Rafea, A.: Article: a survey of ontology learning approaches. Int. J. Comput. Appl. 22(8), 36–43 (2011)Google Scholar
  13. 13.
    Kitsiou, S., Thomas, M., Marai, G.E., Maglaveras, N., Kondos, G., Arena, R., Gerber, B.: Development of an innovative mhealth platform for remote physical activity monitoring and health coaching of cardiac rehabilitation patients. In: 2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), pp. 133–136. IEEE (2017)Google Scholar
  14. 14.
    Malina, R.M., Little, B.B.: Physical activity: the present in the context of the past. Am. J. Hum. Biol. 20(4), 373–391 (2008)CrossRefGoogle Scholar
  15. 15.
    Manning, C.D., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S.J., McClosky, D.: The Stanford CoreNLP natural language processingtoolkit. In: Association for Computational Linguistics (ACL) System Demonstrations, pp. 55–60 (2014). http://www.aclweb.org/anthology/P/P14/P14-5010
  16. 16.
    Missikoff, M., Navigli, R., Velardi, P.: Integrated approach to web ontology learning and engineering. IEEE Comput. 35(11), 60–63 (2002). https://doi.org/10.1109/MC.2002.1046976
  17. 17.
    Mollee, J., Middelweerd, A., Velde, S.T., Klein, M.: Evaluation of a personalized coaching system for physical activity: user appreciation and adherence. In: 11th EAI International Conference on Pervasive Computing Technologies for Healthcare (2017)Google Scholar
  18. 18.
    Peterson, D., Gao, S., Malhotra, A., Sperberg-McQueen, C.M., Thompson, H.S.: W3C XML Schema Definition Language (XSD) 1.1 Part 2: Datatypes. W3C Recommendation. http://www.w3.org/TR/xmlschema11-2/. Accessed 5 Apr 2012
  19. 19.
    Stephens, J., Allen, J.K., Himmelfarb, C.R.D.: Smart coaching to promote physical activity, diet change, and cardiovascular health. J. Cardiovasc. Nurs. 26(4), 282 (2011)CrossRefGoogle Scholar
  20. 20.
    Villalonga, C., op den Akker, H., Hermens, H., Herrera, L.J., Pomares, H., Rojas, I., Valenzuela, O., Banos, O.: Ontological modeling of motivational messages for physical activity coaching. In: 11th EAI International Conference on Pervasive Computing Technologies for Healthcare (2017)Google Scholar
  21. 21.
    Villalonga, C., Razzaq, M.A., Khan, W.A., Pomares, H., Rojas, I., Lee, S., Banos, O.: Ontology-based high-level context inference for human behavior identification. Sensors 16(10), 1617 (2016). http://www.mdpi.com/1424-8220/16/10/1617
  22. 22.
    W3C OWL Working Group : OWL 2 Web Ontology Language: Document Overview, 2nd edn. W3C Recommendation. http://www.w3.org/TR/owl2-overview/. Accessed 11 Dec 2012
  23. 23.
    Watson, A., Bickmore, T., Cange, A., Kulshreshtha, A., Kvedar, J.: An internet-based virtual coach to promote physical activity adherence in overweight adults: randomized controlled trial. J. Med. Internet Res. 14(1), 1–12 (2012)CrossRefGoogle Scholar
  24. 24.
    Wieringa, W., Akker, H., Jones, V.M., Akker, R., Hermens, Hermie J.: Ontology-based generation of dynamic feedback on physical activity. In: Peleg, M., Lavrač, N., Combi, C. (eds.) AIME 2011. LNCS (LNAI), vol. 6747, pp. 55–59. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-22218-4_7 CrossRefGoogle Scholar
  25. 25.
    Wong, W., Liu, W., Bennamoun, M.: Ontology learning from text: a look back and into the future. ACM Comput. Surv. 44(4), 20: 1–20: 36. http://doi.acm.org/10.1145/2333112.2333115

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Claudia Villalonga
    • 1
  • Harm op den Akker
    • 2
  • Hermie Hermens
    • 2
    • 3
  • Luis Javier Herrera
    • 1
  • Hector Pomares
    • 1
  • Ignacio Rojas
    • 1
  • Olga Valenzuela
    • 4
  • Oresti Banos
    • 3
  1. 1.Research Center for Information and Communications TechnologiesUniversity of GranadaGranadaSpain
  2. 2.Roessingh Research and Development, Telemedicine GroupEnschedeThe Netherlands
  3. 3.Center for Telematics and Information TechnologyUniversity of TwenteEnschedeThe Netherlands
  4. 4.Department of Applied MathematicsUniversity of GranadaGranadaSpain

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