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Completeness Knowledge Representation in Fuzzy Description Logics

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Knowledge Technology (KTW 2011)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 295))

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Abstract

Semantic Web is increasingly becoming the best extension of World Wide Web which enables machines to be more interpretable and present information in less ambiguous process. Web Ontology Language (OWL) is based on all the knowledge representation formalisms of Description Logics (DLs) that has the W3C standard. DLs are the families of formal knowledge representation languages that have high expressive power in reasoning concepts. However, DLs are unable to express a number of vague or imprecise knowledge and thereby cannot handle more uncertainties. To reduce this problem, this paper focuses on the reasoning processes with knowledge-base representation in fuzzy description logics. We consider Gödel method in solving the completeness of our deductive system. We also discuss the desirable concepts based on entailment to fuzzy DL knowledge-base satisfiability. Indeed, fuzzy description logic is the suitable formalism to represent this category of knowledge.

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References

  1. Bobillo, F., Delgado, M., Gómez-Romero, J., Straccia, U.: Fuzzy description logics under Gödel semantics. International Journal of Approximate Reasoning 50, 494–514 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  2. Danyaro, K.U., Jaafar, J., Liew, M.S.: Tractability method for Ontology Development of Semantic Web. In: Proceedings of the International Conference on Semantic Technology and Information Retrieval, STAIR 2011, pp. 28–29 (2011)

    Google Scholar 

  3. Zhao, J., Boley, H.: Knowledge Representation and Reasoning in Norm-Parameterized Fuzzy Description Logics. In: Du, W., Ensan, F. (eds.) Canadian Semantic Web Technologies and Applications, pp. 27–53. Springer, Heidelberg (2010)

    Google Scholar 

  4. Gruber, T.R.: A Translation Approach to Portable Ontology Specifications. Knowledge Acquisitions 5, 199–220 (1993)

    Article  Google Scholar 

  5. OWL Web Ontology Language Overview, http://www.w3.org/TR/owl-features/

  6. Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach, 2nd edn. PearsonEducation, New Jersey (2010)

    Google Scholar 

  7. Royden, H.L., Fitzpatrick, P.M.: Real Analysis, 4th edn. Pearson, Prentice Hall (2010)

    Google Scholar 

  8. Hitzler, P., Krötzsch, M., Rudolph, S.: Foundations of Semantic Web Technologies. CRC Press, New York (2010)

    Google Scholar 

  9. Zadeh, L.A.: Is There a Need for Fuzzy Logic? Information Sciences 178, 2751–2779 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  10. Bobilloa, F., Straccia, U.: Fuzzy Description Logics with General t-norms and Data Types. Fuzzy Sets and Systems 160, 3382–3402 (2009)

    Article  MathSciNet  Google Scholar 

  11. Lukasiewicz, T., Straccia, U.: Description Logic Programs Under Probabilistic Uncertainty and Fuzzy Vagueness. International Journal of Approximate Reasoning 50, 837–853 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  12. Grimm, S., Hitzler, P., Abecker, A.: Knowledge Representation and Ontologies, Logic, Ontologies and Semantic Web Languages. In: Studer, R., Grimm, S., Abecker, A. (eds.) Semantic Web Services, pp. 51–105. Springer, New York (2007)

    Google Scholar 

  13. Yeung, C.A., Leung, H.: A Formal Model of Ontology for Handling Fuzzy Membership and Typicality of Instances. The Computer Journal 53(3), 316–341 (2010)

    Article  Google Scholar 

  14. Lukasiewicz, T.: Probabilistic Description Logic Programs under Inheritance with Overriding for the Semantic Web. International Journal of Approximate Reasoning 49, 18–34 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  15. Garcia-Cerdaňa, À., Armengol, E., Esteva, F.: Fuzzy Description Logics and t-norm Based Fuzzy Logics. International Journal of Approximate Reasoning 51, 632–655 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  16. Stoilos, G., Stamou, G., Pan, J.Z.: Fuzzy extensions of OWL: Logical Properties and Reduction to Fuzzy Description Logics. International Journal of Approximate Reasoning 51, 656–679 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  17. Józefowska, J., Lawrynowicz, A., Lukaszewski, T.: The Role of Semantics in Mining Frequent Patterns from Knowledge Bases in Description Logics with Rules. Theory and Practice of Logic Programming (TPLP) 2, 1–40 (2010)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer & Information Sciences, Universiti Teknologi PETRONAS, Seri Iskandar, 31750, Tronoh, Perak, Malaysia

    Kamaluddeen Usman Danyaro & Jafreezal Jaafar

  2. Department of Civil Engineering, Universiti Teknologi PETRONAS, Seri Iskandar, 31750, Tronoh, Perak, Malaysia

    Mohd Shahir Liew

Authors
  1. Kamaluddeen Usman Danyaro
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  2. Jafreezal Jaafar
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  3. Mohd Shahir Liew
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Editor information

Editors and Affiliations

  1. MIMOS Berhad, Knowledge Technology, Technology Park Malaysia, 57000, Kuala Lumpur, Malaysia

    Dickson Lukose

  2. Department of Systems and Network,College of Information Technology, Jalan IKRAM-UNITEN, Universiti Tenaga Nasional, 43000, Kajang, Malaysia

    Abdul Rahim Ahmad  & Azizah Suliman  & 

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© 2012 Springer-Verlag Berlin Heidelberg

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Danyaro, K.U., Jaafar, J., Liew, M.S. (2012). Completeness Knowledge Representation in Fuzzy Description Logics. In: Lukose, D., Ahmad, A.R., Suliman, A. (eds) Knowledge Technology. KTW 2011. Communications in Computer and Information Science, vol 295. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32826-8_17

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  • DOI: https://doi.org/10.1007/978-3-642-32826-8_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32825-1

  • Online ISBN: 978-3-642-32826-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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