Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Automatic mapping of motivational text messages into ontological entities for smart coaching applications

18 views

Published on

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 automat- ically 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.

Published in: Science
  • Be the first to comment

  • Be the first to like this

Automatic mapping of motivational text messages into ontological entities for smart coaching applications

  1. 1. AUTOMATIC MAPPING OF MOTIVATIONAL TEXT MESSAGES INTO ONTOLOGICAL ENTITIES FOR SMART COACHING APPLICATIONS C. VILLALONGA(*), H. OP DEN AKKER, H. HERMENS, L.J. HERRERA, H. POMARES, I. ROJAS, O. VALENZUELA, O. BANOS (*) claudia.villalonga@unir.net
  2. 2. 23/3/18 2 E-COACHING WHY IS IT THAT CHALLENGING?  Major efforts have been mainly targeted at improving the quantification part (oversimplification vs exaggeration)  “Generic” motivational messages (one- size-does-not-fit-all)  Shallow recommendations (explanation of goals, how to reach them, implications, etc.)  We are still learning…
  3. 3. 23/3/18 3 MOTIVATIONAL MESSAGES FOR E-COACHING MAIN CHALLENGES  Represent the principal, and perhaps more natural, means for translating behavioral findings into easy-to- follow and realizable recommendations (actions)  KEY challenges:  Generation of relevant messages tailored to the performance, needs and characteristics of each specific user [Noar2007]  Fostering the diversity of the messages to increase adherence and make the coaching system more realistic and trustworthy [opdenAkker2015] Noar et al. Does tailoring matter? Meta-analytic review of tailored print health behavior change interventions. Psychological bulletin 133, 4 (2007), 673. op den Akker et al.Tailored motivational message generation: A model and practical framework for real-time physical activity coaching. Journal of Biomedical Informatics 55 (2015), 104-115.
  4. 4. 23/3/18 4 FRAMEWORK OF MOTIVATIONAL MESSAGES PROPOSED SOLUTION
  5. 5. 23/3/18 5 FRAMEWORK OF MOTIVATIONAL MESSAGES MOTIVATIONAL MESSAGE ONTOLOGY “You should walk the dog to the park early in the morning”
  6. 6. 23/3/18 6 FRAMEWORK OF MOTIVATIONAL MESSAGES AUTOMATIC MAPPING OF MOTIVATIONAL TEXT MESSAGES “You should walk the dog to the park early in the morning” Automatic Mapping
  7. 7. 23/3/18 7 AUTOMATIC MAPPING OF MOTIVATIONAL TEXT MESSAGES LINGUISTIC ANALYSIS “You should walk the dog to the park early in the morning” predicatesubject Part of Speech pronoun verb verb article article article noun noun noun adverb preposition preposition Grammatical Structure object modifiermodifier
  8. 8. 23/3/18 8 AUTOMATIC MAPPING OF MOTIVATIONAL TEXT MESSAGES LINGUISTIC ANALYSIS “You should walk the dog to the park early in the morning” predicatesubject pronoun verb verb article article article noun noun noun adverb preposition preposition object modifiermodifier ACTION LOCATION TIMEELEMENT
  9. 9. 23/3/18 9 AUTOMATIC MAPPING OF MOTIVATIONAL TEXT MESSAGES ONTOLOGICAL REPRESENTATION ACTION PLACE TIMEELEMENT “You should walk the dog to the park early in the morning”
  10. 10. 23/3/18 10 AUTOMATIC MAPPING OF MOTIVATIONAL TEXT MESSAGES ONTOLOGICAL REPRESENTATION “You should walk the dog to the park early in the morning” TIME ACTION ELEMENT PLACE
  11. 11. 23/3/18 11 AUTOMATIC MAPPING OF MOTIVATIONAL TEXT MESSAGES MESSAGE SPLITTING “Walk or run to the park!” “Walk to the park!” “Run to the park!”
  12. 12. 23/3/18 12 AUTOMATIC MAPPING OF MOTIVATIONAL TEXT MESSAGES MESSAGE INFERENCE “Why don’t you go to the gym and practice some exercise?” “Why don’t you go to the park and practice some exercise?” Ontology reasoning
  13. 13. 23/3/18 13 AUTOMATIC MAPPING OF MOTIVATIONAL TEXT MESSAGES IMPLEMENTATION  Java Implementation  Stanford CoreNLP  Apache Jena (v2.11.2)
  14. 14. 23/3/18 14 CONCLUSIONS This work contributes with:  A new approach for automatically extracting the semantics of motivational messages and creating the ontological representation of these messages Future work:  Evaluation of the message mapping method  Improvement of the method to infer new messages based on the knowledge modeled in the ontology  Extension of the motivational message ontology to include more concepts by linking available ontologies and thesaurus  Implementation of the message retrieval method to have a fully functional framework for the automatic generation of tailored coaching messages
  15. 15. 23/3/18 15 Thank you! Questions? This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 769553.

×