FML-based ontological agent for healthcare application with diabetes

G. Acampora; C.-S. Lee; M.-H. Wang

doi:10.1109/WI-IAT.2009.314

FML-based ontological agent for healthcare application with diabetes

G. Acampora, C.-S. Lee, M.-H. Wang

Information Systems IE&IS

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

6 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	Proceedings of the IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technologies, WI-IAT '09, 15-18 September 2009, Milan, Italy
Place of Publication	Piscataway
Publisher	Institute of Electrical and Electronics Engineers
Pages	413-416
ISBN (Print)	978-1-4244-5331-3
DOIs	https://doi.org/10.1109/WI-IAT.2009.314
Publication status	Published - 2009

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10.1109/WI-IAT.2009.314

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Acampora, G., Lee, C.-S., & Wang, M.-H. (2009). FML-based ontological agent for healthcare application with diabetes. In Proceedings of the IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technologies, WI-IAT '09, 15-18 September 2009, Milan, Italy (pp. 413-416). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/WI-IAT.2009.314

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title = "FML-based ontological agent for healthcare application with diabetes",

abstract = "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{\textquoteright} 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.",

author = "G. Acampora and C.-S. Lee and M.-H. Wang",

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booktitle = "Proceedings of the IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technologies, WI-IAT '09, 15-18 September 2009, Milan, Italy",

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Acampora, G, Lee, C-S & Wang, M-H 2009, FML-based ontological agent for healthcare application with diabetes. in Proceedings of the IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technologies, WI-IAT '09, 15-18 September 2009, Milan, Italy. Institute of Electrical and Electronics Engineers, Piscataway, pp. 413-416. https://doi.org/10.1109/WI-IAT.2009.314

FML-based ontological agent for healthcare application with diabetes. / Acampora, G.; Lee, C.-S.; Wang, M.-H.
Proceedings of the IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technologies, WI-IAT '09, 15-18 September 2009, Milan, Italy. Piscataway: Institute of Electrical and Electronics Engineers, 2009. p. 413-416.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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AU - Wang, M.-H.

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N2 - 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.

AB - 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.

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SN - 978-1-4244-5331-3

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Acampora G, Lee CS, Wang MH. FML-based ontological agent for healthcare application with diabetes. In Proceedings of the IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technologies, WI-IAT '09, 15-18 September 2009, Milan, Italy. Piscataway: Institute of Electrical and Electronics Engineers. 2009. p. 413-416 doi: 10.1109/WI-IAT.2009.314

FML-based ontological agent for healthcare application with diabetes

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