Encyclopedia of Database Systems

2018 Edition
| Editors: Ling Liu, M. Tamer Özsu

Executable Knowledge

  • Mor Peleg
  • Arturo González-Ferrer
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_1356

Synonyms

Computer-interpretable formalism; Knowledge-based systems

Definition

Knowledge represented in a symbolic formalism that can be understood by human beings and interpreted and executed by a computer program. Executable knowledge allows a computer program to match case data to the knowledge, reason with the knowledge, select recommended actions that are specific to the case data, and deliver them to users. Executed knowledge can be delivered in the form of advice, alerts, and reminders and can be used in decision-support or process management.

Historical Background

Representing knowledge in a computer-interpretable format and reasoning with it so as to support humans in decision-making started to be developed by the artificial intelligence community in the 1970s. According to Newell [1], knowledge is separate from its representation. At the knowledge level, an agent has as parts bodies of knowledge, actions, and goals. An agent processes its knowledge and, behaving through the...

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Recommended Reading

  1. 1.
    Newell A. The knowledge level. Presidential address, American Association for Artificial Intelligence (AAAI80), Stanford University, 1980.Google Scholar
  2. 2.
    Davis R, Shrobe H, Szolovits P. What is a knowledge representation? AI Mag. 1993;14(1):17–33.Google Scholar
  3. 3.
    Shortliffe EH. Computer-based medical consultations: Mycin. New York: Elsevier/North Holland; 1976.Google Scholar
  4. 4.
    Pearl J. Probabilistic reasoning in intelligent systems. San Francisco: Morgan Kaufmann; 1988.zbMATHGoogle Scholar
  5. 5.
    Hripcsak G, Ludemann P, Pryor TA, Wigertz OB, Clayton PD. Rationale for the arden syntax. Comput Biomed Res. 1994;27(4):291–324.CrossRefGoogle Scholar
  6. 6.
    Gruber TR. Toward principles for the design of ontologies used for knowledge sharing. Int J Hum Comput Stud. 1995;43(5/6):907–28.CrossRefGoogle Scholar
  7. 7.
    Musen M.A., Tu S.W., Eriksson H., Gennari J.H., and Puerta A.R. PROTEGE-II: an environment for reusable problem-solving methods and domain ontologies. In: Proceedings of the 13th International Joint Conference on Artificial Intelligence; 1993.Google Scholar
  8. 8.
    Peleg M, Tu SW, Bury J, Ciccarese P, Fox J, Greenes RA, et al. Comparing computer-interpretable guideline models: a case study approach. J Am Med Inform Assoc. 2003;10(1):52–68.CrossRefGoogle Scholar
  9. 9.
    Peleg M. Computer-interpretable clinical guidelines: a methodological review. J Biomed Inform. 2013;46(4):744–63.CrossRefGoogle Scholar
  10. 10.
    Peleg M, González-Ferrer A. Guideline and workflow models. In: Greenes RA, editor. Clinical decision support – the road to broad adoption. 2nd ed. Orlando: Elsevier/Academic Press; 2014.Google Scholar
  11. 11.
    de Clercq PA, Blom JA, Korsten HHM, Hasman A. Approaches for creating computer-interpretable guidelines that facilitate decision support. Artif Intell Med. 2004;31(1):1–27.CrossRefGoogle Scholar
  12. 12.
    ten Teije A, Marcos M, Balser M, van Croonenborg J, Duelli C, van Harmelen F, et al. Improving medical protocols by formal methods. Artif Intell Med. 2006;36(3):193–209.CrossRefGoogle Scholar
  13. 13.
    van der Aalst WMP. The application of petri nets to workflow management. J Circuits Syst Comput. 1998;8(1):21–66.CrossRefGoogle Scholar
  14. 14.
    Tu SW, Campbell JR, Glasgow J, Nyman MA, McClure R, McClay JPC, Hrabak KM, Berg D, Weida T, Mansfield JG, Musen MA, Abarbanel RM. The SAGE guideline model: achievements and overview. J Am Med Inform Assoc. 2007;14(5):589–98.CrossRefGoogle Scholar
  15. 15.
    Peleg M, Keren S, Denekamp Y. Mapping computerized clinical guidelines to electronic medical records: knowledge-data ontological mapper (KDOM). J Biomed Inform. 2008;41(1):180–201.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Information SystemsUniversity of HaifaHaifaIsrael
  2. 2.Innovation UnitInstituto de Investigación Sanitaria del Hospital Clínico San Carlos (IdISSC)MadridSpain

Section editors and affiliations

  • Vipul Kashyap
    • 1
  1. 1.Clinical ProgramsCIGNA HealthcareBloomfieldUSA