FML-based ontological agent for healthcare application with diabetes

It is well-known that classical ontologies are not sufficient to deal with imprecise and vague knowledge. On the other hand, fuzzy ontologies can effectively solve data and knowledge with uncertainty, most importantly, if they are integrated with innovative methods for developing agents' intelligence as the Fuzzy Markup Language (FML). In this paper, an FML-based ontology-based intelligent fuzzy agent and a semantic decision making mechanism are proposed to apply to the semantic decision making for diabetes domain. The 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. © 2009 IEEE.