Abstract
Semantic Web and Ontologies has increased the interest in the use of Knowledge-based systems in order to allow automated processing of, and reasoning with, information on the Web. However, it is widely pointed out that standard ontologies are not sufficient to deal with imprecise and vague knowledge for some real world applications, but the fuzzy ontologies can effectively model data and knowledge with uncertainty. In particular, in this paper will be introduced a collection of real-world applications based on the integration of different web-oriented frameworks such as the ontology − based intelligent fuzzy agents (OIFAs) and the Fuzzy Markup Language (FML) capable of generating fuzzy inference mechanisms and semantic decision making systems for an efficient modeling of real scenarios. In detail, hereafter our web intelligence approach will be applied to medical semantic decision making systems, computer go framework and so on.The experimental results show that the proposed method is feasible for different real-word scenarios.
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References
Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions Systems, Man & Cybernetics 15(1), 116–132 (1985)
Mamdani, E.H.: Applications of fuzzy algorithms for simple dynamic plants. Proc. IEE 121, 1585–1588 (1974)
Acampora, G., Loia, V., Vitiello, A.: Enhancing Transparent Fuzzy Controllers through Temporal Concepts: An Application to Computer Games. In: International Workshop on Computer Games (IWCG 2010), Conference on Technologies and Applications of Artificial Intelligence (TAAI 2010) (November 2010)
Zadeh, L.A.: Fuzzy sets. Information and Control 8, 338–353 (1965)
Calegari, S., Ciucci, D.E.: Fuzzy Ontology, Fuzzy Description Logics and Fuzzy-OWL. In: Masulli, F., Mitra, S., Pasi, G. (eds.) WILF 2007. LNCS (LNAI), vol. 4578, pp. 118–126. Springer, Heidelberg (2007)
Lee, C.C.: Fuzzy Logic in Control System: Fuzzy Logic Controller - Part I and Part II. IEEE Transactions on SMC 20, 404–435 (1990)
Acampora, G., Loia, V., Vitiello, A.: Hybridizing fuzzy control and timed automata for modeling variable structure fuzzy systems. In: IEEE International Conference on Fuzzy Systems (FUZZ 2010), July 18-23, pp. 1–8 (2010), doi:10.1109/FUZZY.2010.5584393.
Acampora, G., Lee, C.S., Wang, M.H.: FML-Based Ontological Agent for Healthcare Application with Diabetes. In: Web Intelligence/IAT Workshops, pp. 413–416 (2009)
Acampora, G., Loia, V.: An Open Integrated Environment for Transparent Fuzzy Agents Design. In: IFIP International Federation for Information Processing, vol. 275, pp. 249–255. Springer, Boston (2008)
Wang, Y.L., Chen, H.C., Liu, W.K.: A Parallel Algorithm for Constructing a Labeled Tree. IEEE Transactions on Parallel and Distributed Systems 8, 1236–1240
Acampora, G., Loia, V.: Fuzzy control interoperability and scalability for adaptive domotic framework. IEEE Transactions on Industrial Informatics 1(2), 97–111 (2005)
Lee, C.S., Wang, M.H., Acampora, G., Loia, V., Hsu, C.Y.: Ontology-based intelligent fuzzy agent for diabetes application. In: IEEE Symposium on Intelligent Agents (IA 2009), pp. 16–22 (2009), doi:10.1109/IA.2009.4927495.
Lee, C.S., Wang, M.H., Acampora, G., Loia, V., Hsu, Hagras, H.: Diet assessment based on type-2 fuzzy ontology and fuzzy markupl language. Int. J. Intell. Syst. 25(12), 1187–1216 (2010), http://dx.doi.org/10.1002/int.v25:12 , doi:10.1002/int.v25:12
Hagras, H.: Type -2 FLCs: A new Generation of Fuzzy Controllers. IEEE Computational Intelligence Magazine 2, 30–43 (2007)
Lee, C.S., Wang, M.H., Yan, Z.R., Chen, Y.J., Doghmen, H., Teytaud, O.: FML-based type-2 fuzzy ontology for computer go knowledge representation. In: 2010 International Conference on System Science and Engineering (ICSSE), July 1-3, pp. 63–68 (2010), doi:10.1109/ICSSE.2010.5551703
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Loia, V. (2011). Fuzzy Ontologies and Fuzzy Markup Language: A Novel Vision in Web Intelligence. In: Mugellini, E., Szczepaniak, P.S., Pettenati, M.C., Sokhn, M. (eds) Advances in Intelligent Web Mastering – 3. Advances in Intelligent and Soft Computing, vol 86. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18029-3_1
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DOI: https://doi.org/10.1007/978-3-642-18029-3_1
Publisher Name: Springer, Berlin, Heidelberg
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