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 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 language | English |
|---|---|
| Title of host publication | Proceedings of the IEEE Symposium on Intelligent Agents IA '09, March 3 - April 2, 2009, Nashville, Tennessee |
| Place of Publication | Piscataway |
| Publisher | Institute of Electrical and Electronics Engineers |
| Pages | 16-22 |
| ISBN (Print) | 978-1-4244-2767-3 |
| DOIs | |
| Publication status | Published - 2009 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
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