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Combining Fuzzy Ontology Reasoning and Mamdani Fuzzy Inference System with HyFOM Reasoner

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Enterprise Information Systems (ICEIS 2013)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 190))

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Abstract

Representing and processing imprecise knowledge has been a requirement for a number of applications. Some real-world domains as well as human subjective perceptions are intrinsically fuzzy, therefore conventional formalisms may not be sufficient to capture the intended semantics. In this sense, fuzzy ontologies and Mamdani fuzzy inference systems have been successfully applied for knowledge representation and reasoning. Combining their reasoning approaches can lead to inferences involving fuzzy rules and numerical properties from ontologies, which can be required to perform other fuzzy ontology reasoning tasks such as the fuzzy instance check. To address this issue, this paper describes the HyFOM reasoner, which follows a hybrid architecture to combine fuzzy ontology reasoning with Mamdani fuzzy inference system. A real-world case study involving the domain of food safety is presented, including comparative results with a state-of-the-art fuzzy description logic reasoner.

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References

  1. Straccia, U.: A fuzzy description logic for the semantic web. In: Sanchez, E. (ed.) Fuzzy Logic and the Semantic Web. Capturing Intelligence, pp. 73–90. Elsevier, Amsterdam (2006)

    Chapter  Google Scholar 

  2. Zadeh, L.A.: Fuzzy sets. Inf. Control 3, 338–353 (1965)

    Article  MathSciNet  Google Scholar 

  3. Lukasiewicz, T., Straccia, U.: Managing uncertainty and vagueness in description logics for the semantic web. J. Web Semant. 6(4), 291–308 (2008)

    Article  Google Scholar 

  4. Mamdani, E.H., Assilian, S.: An experiment in linguistic synthesis with a fuzzy logic controller. Int. J. Man-Mach. Stud. 7(1), 1–13 (1975)

    Article  MATH  Google Scholar 

  5. Loia, V.: Fuzzy ontologies and fuzzy markup language: a novel vision in web intelligence. In: Mugellini, E., Szczepaniak, P.S., Pettenati, ChM, Sokhn, M. (eds.) AWIC 2011. AISC, vol. 86, pp. 3–10. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  6. Lee, C.S., Wang, M.H., Acampora, G., Hsu, C.Y., Hagras, H.: Diet assessment based on type-2 fuzzy ontology and fuzzy markup language. Int. J. Intel. Syst. 25(12), 1187–1216 (2010)

    Article  Google Scholar 

  7. Huang, H.D., Acampora, G., Loia, V., Lee, C.S., Kao, H.Y.: Applying FML and fuzzy ontologies to malware behavioural analysis. In: IEEE International Conference on Fuzzy Systems, pp. 2018–2025 (2011)

    Google Scholar 

  8. Bobillo, F., Straccia, U.: fuzzyDL: An expressive fuzzy description logic reasoner. In: International Conference on Fuzzy Systems, Hong Kong, China, pp. 923–930. IEEE Computer Society (2008)

    Google Scholar 

  9. Bobillo, F., Delgado, M., Gómez-Romero, J., López, E.: A semantic fuzzy expert system for a fuzzy balanced scorecard. Expert Syst. Appl. 36(1), 423–433 (2009)

    Article  Google Scholar 

  10. Bobillo, F., Straccia, U.: Fuzzy description logics with general t-norms and datatypes. Fuzzy Sets Syst. 160(23), 3382–3402 (2009)

    Article  MATH  MathSciNet  Google Scholar 

  11. Wlodarczyk, T.W., O’Connor, M., Rong, C., Musen, M.: SWRL-F: a fuzzy logic extension of the Semantic Web Rule Language. In: International Workshop on Uncertainty Reasoning for the Semantic Web (URSW), Shanghai, China. Springer (2010)

    Google Scholar 

  12. Bragaglia, S., Chesani, F., Ciampolini, A., Mello, P., Montali, M., Sottara, D.: An hybrid architecture integrating forward rules with fuzzy ontological reasoning. In: Graña Romay, M., Corchado, E., Garcia Sebastian, M.T. (eds.) HAIS 2010, Part I. LNCS, vol. 6076, pp. 438–445. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  13. de Maio, C., Fenza, G., Furno, D., Loia, V., Senatore, S.: OWL-FC: an upper ontology for semantic modeling of fuzzy control. Soft. Comput. 16(7), 1153–1164 (2012)

    Article  Google Scholar 

  14. Yaguinuma, C.A., de Magalhães Jr., W.C.P., Santos, M.T.P., Camargo, H.A., Reformat, M.: HyFOM reasoner: Hybrid integration of fuzzy ontology and Mamdani reasoning. In: International Conference on Enterprise Information Systems, Angers, France, vol. 1, pp. 372–380. SciTePress (2013)

    Google Scholar 

  15. Guillaume, S., Charnomordic, B.: Fuzzy inference systems: an integrated modeling environment for collaboration between expert knowledge and data using FisPro. Expert Syst. Appl. 39(10), 8744–8755 (2012)

    Article  Google Scholar 

  16. Acampora, G., Loia, V.: Fuzzy control interoperability and scalability for adaptive domotic framework. IEEE Trans. Ind. Inform. 1(2), 97–111 (2005)

    Article  Google Scholar 

  17. Marca, D.A., McGowan, C.L.: SADT: Structured Analysis and Design Technique. McGraw-Hill Inc., New York (1987)

    Google Scholar 

  18. Horridge, M., Bechhofer, S.: The OWL API: a Java API for OWL ontologies. Seman. Web 2(1), 11–21 (2011)

    Google Scholar 

  19. Motik, B., Shearer, R., Horrocks, I.: Hypertableau reasoning for description logics. J. Artif. Intell. Res. 36, 165–228 (2009)

    MATH  MathSciNet  Google Scholar 

  20. Orchard, R.: Fuzzy reasoning in Jess: the FuzzyJ Toolkit and Fuzzy Jess. In: International Conference on Enterprise Information Systems, Setubal, Portugal, pp. 533–542 (2001)

    Google Scholar 

  21. Bobillo, F., Straccia, U.: Fuzzy ontology representation using OWL 2. Int. J. Approximate Reasoning 52(7), 1073–1094 (2011)

    Article  MathSciNet  Google Scholar 

  22. de Magalhães Jr., W.C.P., Bonnet, M., Feijó, L.D., Santos, M.T.P.: Risk-off method: improving data quality generated by chemical risk analysis of milk. In: Cases on SMEs and Open Innovation: Applications and Investigations, pp. 40–64. IGI Global (2012)

    Google Scholar 

  23. de Magalhães Jr., W.C.P.: Chem-risk approach: assessment, management and communication of chemical risks in food by employing knowledge discovery in databases, fuzzy logics and ontologies. Master’s thesis, Federal University of São Carlos (2011) (in portuguese)

    Google Scholar 

  24. Angelov, P., Yager, R.: A new type of simplified fuzzy rule-based system. Int. J. Gen. Syst. 41(2), 163–185 (2012)

    Article  MATH  MathSciNet  Google Scholar 

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Acknowledgements

The authors would like to thank the Brazilian research agency CAPES for supporting this research. Special thanks to the Embrapa Dairy Cattle and the Brazilian Ministry of Agriculture, Livestock and Supply for providing real-world data and domain expert support for the case study.

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Authors and Affiliations

  1. Department of Computer Science, Federal University of São Carlos, São Carlos, SP, Brazil

    Cristiane A. Yaguinuma, Marilde T. P. Santos & Heloisa A. Camargo

  2. Embrapa Dairy Cattle, Juiz de Fora, MG, Brazil

    Walter C. P. Magalhães Jr.

  3. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada

    Marek Reformat

Authors
  1. Cristiane A. Yaguinuma
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  2. Walter C. P. Magalhães Jr.
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  3. Marilde T. P. Santos
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  4. Heloisa A. Camargo
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  5. Marek Reformat
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Corresponding author

Correspondence to Cristiane A. Yaguinuma .

Editor information

Editors and Affiliations

  1. Groupe ESEO, Angers, France

    Slimane Hammoudi

  2. Polytechnic Institute of Setúbal, Setúbal, Portugal & INSTICC, Setúbal, Portugal

    José Cordeiro

  3. Wroclaw University of Economics, Wroclaw, Poland & Macquarie University, Sydney, NSW, Australia

    Leszek A. Maciaszek

  4. Polytechnic Institute of Setúbal, Setúbal, Portugal & INSTICC, Setúbal, Poland

    Joaquim Filipe

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Yaguinuma, C.A., Magalhães, W.C.P., Santos, M.T.P., Camargo, H.A., Reformat, M. (2014). Combining Fuzzy Ontology Reasoning and Mamdani Fuzzy Inference System with HyFOM Reasoner. In: Hammoudi, S., Cordeiro, J., Maciaszek, L., Filipe, J. (eds) Enterprise Information Systems. ICEIS 2013. Lecture Notes in Business Information Processing, vol 190. Springer, Cham. https://doi.org/10.1007/978-3-319-09492-2_11

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  • DOI: https://doi.org/10.1007/978-3-319-09492-2_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-09491-5

  • Online ISBN: 978-3-319-09492-2

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