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 knowledgebase 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. ©2009 IEEE.
Ontology-based intelligent fuzzy agent for diabetes application / Lee, Chang-shing; Wang, Mei-hui; Acampora, Giovanni; Loia, Vincenzo; Hsu, Chin-yuan. - (2009), pp. 16-22. (Intervento presentato al convegno 2009 IEEE Symposium on Intelligent Agents (IA 2009)) [10.1109/IA.2009.4927495].
Ontology-based intelligent fuzzy agent for diabetes application
Acampora Giovanni;
2009
Abstract
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 knowledgebase 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. ©2009 IEEE.File | Dimensione | Formato | |
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