MMSL 2011, 80(1):21-27 | DOI: 10.31482/mmsl.2011.003

ONTOLOGICAL MODELS AND EXPERT SYSTEMS IN DECISION SUPPORT OF EMERGENCY SITUATIONSOriginal article

Pavel Čech1,2, Vladimír Bureš1,2*, Karel Antoš2,3, Tereza Otčenášková1,2, Aleš Macela ORCID...2, Petr Musílek4
1 University of Hradec Kralove, Faculty of Informatics and Management, Hradec Králové,
2 University of Defence, Faculty of Military Health Sciences, Hradec Králové,
3 University hospital Hradec Králové,
4 University of Alberta, Faculty of Engineering, Edmonton, Canada

During emergency response operations many decisions have to be made. Information technologies provide possibilities for new tools to support decision makers in decisions that comprise of many critical factors and that require specialized knowledge. In these tools the complexity is tackled using modelling and simulations of possible scenarios of response operations. Today, conceptual modelling in the field of information technology is oriented on the ontological approach. Ontology is a shared vocabulary and an unambiguous machine processed specification of terms together with their relationships. The ontology can have the form of a taxonomy or classification, database schema or axiomatic theory. The ontological modelling can be utilized along with expert systems for decision support. Expert systems, in contrast to other approaches such as neural networks for instance, better reflect the domain knowledge and provide justification for the decision. The aim of this paper is to describe prerequisites and design general schema for decision support in response operations during biological incidents including the applicable technology.

Keywords: decision; biological incident; decision support systems; ontological model; expert system

Received: October 4, 2010; Revised: March 9, 2011; Published: April 6, 2011  Show citation

ACS AIP APA ASA Harvard Chicago Chicago Notes IEEE ISO690 MLA NLM Turabian Vancouver
Čech, P., Bureš, V., Antoš, K., Otčenášková, T., Macela, A., & Musílek, P. (2011). ONTOLOGICAL MODELS AND EXPERT SYSTEMS IN DECISION SUPPORT OF EMERGENCY SITUATIONS. MMSL, 80(1), 21-27. doi: 10.31482/mmsl.2011.003
Share...
Download citation
  • BibTeX (.bib)
  • Bookends (.ris)
  • EasyBib (.ris)
  • EndNote (.enw)
  • EndNote 8 (.xml)
  • ISI WoS (.isi)
  • Medlars (.medlars)
  • Mendeley (.ris)
  • MODS (.xml)
  • Papers (.ris)
  • RefWorks (.txt)
  • RefManager (.ris)
  • RIS (.ris)
  • MS Word (.xml)
  • Zotero (.ris)
    • Open full article

    References

    1. COLIZZA, V. - BARRAT, A. - BARTHELEMY, M - VALLERON, AJ - VESPIGNANI, A. Modeling the Worldwide Spread of Pandemic Influenza: Baseline Case and Containment Interventions. PLoS Med. 2007, vol. 4, iss. 1, p. 13. Go to original source...
    2. DOHNAL, V. - SLÁDKOVÁ, A. - KUČA, K. - JUN, D. Artificial Noses in Detection of Moulds and Mycotoxins, Vojenské zdravotnické listy, 2008, vol. 77, no. 2, p. 66-70 (in Czech).
    3. FORBUS, K. D. Qualitative Process Theory, Artificial Intelligence 24, pp. 85-168, 1984. Go to original source...
    4. FRIEDMAN-HILL, E. Jess in Action, Manning, New York, 2003.
    5. MARAKAS, G. M. Decision Support Systems in the 21st Century, Prentice Hall, Upper Saddle River, NJ, 1999.
    6. PONCE, D. Slow Processes, dissertation thesis, Economic University in Bratislava, 2006.
    7. POWER, D. J. Decision Support Systems: Concepts and Resources for Managers, Quorum Books, London, 2002.
    8. PROTÉGE: The official website, available: http://protege.stanford.edu, 2009.
    9. SKLENÁK, V. et al. Data, information, knowledge and Internet, C.H.Beck, Praha, 2001(in Czech).
    10. VASANT, D. - STEIN, R.: Intelligent Decision Support Methods. Prentice Hall, New Jersey, 1997.

    Return to the content