Ontology-based intelligent fuzzy agent for diabetes application

G. Acampora, C.-S. Lee, M.-H. Wang, C.-Y. Hsu, V. Loia

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

23 Citations (Scopus)


It is widely pointed out that classical ontologies are not sufficient to deal with imprecise and vague knowledge for some real world applications, but the fuzzy ontology can effectively solve data and knowledge with uncertainty. In this paper, an ontology-based intelligent fuzzy agent (OIFA), including a fuzzy markup language (FML) generating mechanism, a FML parser, a fuzzy inference mechanism, and a semantic decision making mechanism, is proposed to apply to the semantic decision making for diabetes domain. In addition, a FML-based definition is considered modeling the knowledge base and rule base of the fuzzy objects and inference operators. The experimental results show that the proposed method is feasible for diabetes semantic decision-making
Original languageEnglish
Title of host publicationProceedings of the IEEE Symposium on Intelligent Agents IA '09, March 3 - April 2, 2009, Nashville, Tennessee
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
ISBN (Print)978-1-4244-2767-3
Publication statusPublished - 2009


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