Ontology-based multi-agents for intelligent healthcare applications

  • Mei-Hui Wang
  • Chang-Shing Lee
  • Kuang-Liang Hsieh
  • Chin-Yuan Hsu
  • Giovanni Acampora
  • Chong-Ching Chang
Original Research


A healthy diet and lifestyle are the most effective approaches to prevent disease. Good eating habits are central to a healthy lifestyle. When a person eats too much or too little on a continual basis, the risk of disease will increase. Therefore, developing healthy and balanced eating habits is essential to disease prevention. This paper proposes an ontology-based multi-agents (OMAS), including a personal knowledge agent, a fuzzy inference agent, and a semantic generation agent, for evaluating the health of diets. Using the proposed approach, domain experts can create nutritional facts for common Taiwanese foods. Next, the users are requested to input foods eaten. Finally, the food ontology and personal profile ontology are constructed by domain experts. Fuzzy markup language (FML) is used to describe the knowledge base and rule base of the OMAS. Additionally, web ontology language (OWL) is employed to describe the food ontology and personal profile ontology. Finally, the OMAS semantically analyzes dietary status for users based on the pre-constructed ontology and fuzzy inference results. Using the generated semantic analysis, people can obtain health information about what they eat, which can lead to a healthy lifestyle and healthy diet. Experimental results show that the proposed approach works effectively and diet health status can be provided as a reference to promote healthy living.


Ontology Fuzzy inference Agent Fuzzy markup language (FML) Diet planning 



This work is supported by the National Science Council (NSC) of Taiwan under the grant NSC97-2221-E-024-011-MY2 and NSC98-2221-E-024-009-MY3. The authors would like to thank all subjects for their involving this research project. Ted Knoy is appreciated for his editorial assistance.


  1. Acampora G, Loia V (2005a) Using FML and fuzzy technology in adaptive ambient intelligence environments. Int J Comput Intell Res 1(2):171–182Google Scholar
  2. Acampora G, Loia V (2005b) Fuzzy control interoperability and scalability for adaptive domotic framework. IEEE Trans Ind Inform 1(2):97–111CrossRefGoogle Scholar
  3. Caceres C, Fernandez A, Ossowski S, Vasirani M (2006) Agent-based semantic service discovery for healthcare: an organizational approach. IEEE Intell Syst 21(6):11–20CrossRefGoogle Scholar
  4. Chan V, Ray P, Parameswaran N (2008) Mobile e-Health monitoring: an agent-based approach. IET Commun 2(2):223–230CrossRefGoogle Scholar
  5. Chen RS, Chen DK (2008) Apply ontology and agent technology to construct virtual observatory. Exp Syst Appl 34(3):2019–2028CrossRefGoogle Scholar
  6. Debenham J, Sierra C (2008) Merging intelligent agency and the Semantic Web. Knowledge Based Syst Arch 21(3):184–191CrossRefGoogle Scholar
  7. Ferber J (1999) Multi-Agent Systems: an introduction to distributed artificial intelligence. Addison-Wesley, New YorkGoogle Scholar
  8. Frankenfield DC, Muth ER, Rowe WA (1998) The Harris-Benedict studies of human basal metabolism: history and limitations. J Am Diet Assoc 98(4):439–445CrossRefGoogle Scholar
  9. Lee CS, Wang MH (2007) Ontology-based intelligent healthcare agent and its application to respiratory waveform recognition. Exp Syst Appl 33(3):606–619CrossRefMathSciNetGoogle Scholar
  10. Lee CS, Wang MH (2008) Ontological fuzzy agent for electrocardiogram application. Exp Syst Appl 35(3):1223–1236CrossRefGoogle Scholar
  11. Lee CS, Jian ZW, Huang LK (2005) A fuzzy ontology and its application to news summarization. IEEE Trans Syst Man Cybern B 35(5):859–880CrossRefGoogle Scholar
  12. Lee CS, Jiang CC, Hsieh TC (2006) A genetic agent using ontology model for meeting scheduling system. Inf Sci 176(9):131–1155CrossRefGoogle Scholar
  13. Lee CS, Wang MH, Acampora G, Loia V, Hsu CY (2009) Ontology-based intelligent fuzzy agent for diabetes application. In: Proceeding of the 2009 IEEE Symposium on Computational Intelligence for Intelligent Agents (IA 2009), Nashville, Tennessee, USA, March 30–April 2, pp 16–22Google Scholar
  14. Moreno A (2006) On the evolution of applying agent technology to healthcare. IEEE Intell Syst 21(6):8–10CrossRefGoogle Scholar
  15. Moreno A, Valls A, Isern D, Sanchez D (2006) Applying agent technology to healthcare: the CruSMA experience. IEEE Intell Syst 21(6):63–67CrossRefGoogle Scholar
  16. Noy NF, McGuinness DL (2001) Ontology development 101: a guide to creating your first ontology. Stanford knowledge systems laboratory technical report KSL-01-05 and Stanford medical informatics technical report SMI-2001-0880Google Scholar
  17. Orgun B, Vu J (2006) HL7 ontology and mobile agents for interoperability in heterogeneous medical information systems. Comput Biol Med 36(7–8):817–836CrossRefGoogle Scholar
  18. Reformat M, Ly C (2009) Ontological approach to development of computing with words based systems. Int J Approx Reason 50(1):72–91CrossRefGoogle Scholar
  19. Sanchez FG, García RV, Bejar RM, F. Breis JT (2009) An ontology, intelligent agent-based framework for the provision of semantic web services. Exp Syst Appl 36(2):3167–3187CrossRefGoogle Scholar
  20. US Food and Drug Administration (2009) How to understand and use the nutrition facts label. http://www.cfsan.fda.gov/~dms/foodlab.html. Accessed 2 July 2009
  21. Wang MH, Lee CS, Hsieh KL, Hsu CY, and Chang CC (2009) Intelligent ontological multi-agent for healthy diet planning. In: Proceeding of the 2009 IEEE International Conference on Fuzzy System (FUZZ-IEEE 2009), Jeju Island, Korea, Aug. 19–24, pp 751–756Google Scholar
  22. Zunino A, Campo M (2009) Chronos: a multi-agent system for distributed automatic meeting scheduling. Exp Syst Appl 36(3):7011–7018CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Mei-Hui Wang
    • 1
  • Chang-Shing Lee
    • 1
  • Kuang-Liang Hsieh
    • 2
  • Chin-Yuan Hsu
    • 3
  • Giovanni Acampora
    • 4
  • Chong-Ching Chang
    • 2
  1. 1.Department of Computer Science and Information EngineeringNational University of TainanTainanTaiwan
  2. 2.Graduate Institute of System EngineeringNational University of TainanTainanTaiwan
  3. 3.Advance e-Commerce InstituteInstitute for Information IndustryKaohsiungTaiwan
  4. 4.Department of Mathematics and Computer ScienceUniversity of SalernoSalernoItaly

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