Executable Knowledge
Synonyms
Definition
Executable knowledge is represented in a symbolic formalism that can be understood by human beings and interpreted and executed by a computer program. It 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 [6], 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 to determine the actions to...
Recommended Reading
- 1.Davis R., Shrobe H., and Szolovits P. What is a knowledge representation? AI Magazine, 14(1):17–33, 1993.Google Scholar
- 2.de Clercq P.A., Blom J.A., Korsten H.H.M., and Hasman A. Approaches for creating computer-interpretable guidelines that facilitate decision support. Artif. Intell. Med., 31:1–27, 2004.CrossRefGoogle Scholar
- 3.Gruber T.R. Toward principles for the design of ontologies used for knowledge sharing. Int. J. Human Comput. Stud., 43:907–928, 1995.CrossRefGoogle Scholar
- 4.Hripcsak G., Ludemann P., Pryor T.A., Wigertz O.B., and Clayton P.D. Rationale for the arden syntax. Comput. Biomed. Res., 27(4):291–324, 1994.CrossRefGoogle Scholar
- 5.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 Proc. 13th Int. Joint Conf. on AI, 1993.Google Scholar
- 6.Newell A. The knowledge level. AI Magazine, 2(2):1–20, 33, 1980.Google Scholar
- 7.Pearl J. Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann, San Francisco, CA, 1988.Google Scholar
- 8.Peleg M. Guideline and workflow models. In Clinical Decision Support – The Road Ahead, R.A. Greenes (ed.). Elsevier/Academic Press, Orlando, FL, 2006.Google Scholar
- 9.Peleg M., Keren S., and Denekamp Y. Mapping computerized clinical guidelines to electronic medical records: knowledge-data ontological mapper (KDOM). J. Biomed. Inform., 41(1):180–201, 2008.CrossRefGoogle Scholar
- 10.Peleg M., Tu S.W., Bury J., Ciccarese P., Fox J., Greenes R.A., et al. Comparing computer-interpretable guideline models: a case-study approach. J. Am. Med. Inform. Assoc., 10(1):52–68, 2003.CrossRefGoogle Scholar
- 11.Shortliffe E.H. Computer-Based Medical Consultations: Mycin. Elsevier/North Holland, New York, 1976.Google Scholar
- 12.Sordo M., Ogunyemi O., Boxwala A.A., Greenes R.A., and Tu S. GELLO: a common expression language. Available online at: http://cslxinfmtcs.csmc.edu/hl7/arden/2004–09-ATL/v3ballot_gello_aug2004.zip, 2004.
- 13.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., 36(3):193–209, 2006.CrossRefGoogle Scholar
- 14.Tu S.W., Campbell J.R., Glasgow J., Nyman M.A., McClure R., McClay J.P.C., Hrabak K.M., Berg D., Weida T., Mansfield J.G., Musen M.A., and Abarbanel R.M. The SAGE guideline model: achievements and overview. J. Am. Med. Inform. Assoc., 14(5):589–598, 2007.CrossRefGoogle Scholar
- 15.van der Aalst W.M.P. The application of petri nets to workflow management. J. Circuits Syst. Comput., 8(1):21–66, 1998.CrossRefGoogle Scholar