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Intersection of Fuzzy Homogeneous Classes of Objects

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Advances in Intelligent Systems and Computing V (CSIT 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1293))

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Abstract

Dynamic generation of new knowledge representation structures and their further integration into the knowledge base, while analys-ing and processing of new fuzzy knowledge and knowledge sources, is an important feature of modern intelligent systems. Such functionality can be implemented via operations defined over knowledge representation structures, in particular set-theoretic ones. Therefore concepts of fuzzy homogeneous class of objects and universal intersection exploiter of fuzzy homogeneous classes of objects within such a knowledge representation model as fuzzy object-oriented dynamic networks were introduced in the paper. Proposed universal exploiter computes the intersection of two fuzzy homogeneous classes of objects via construction of new fuzzy homogeneous class of object, which consists of their common (equivalent) properties and methods, if such class exists. To implement the introduced universal intersection exploiter of fuzzy homogeneous classes of objects, the corresponding algorithm was developed and described in the paper. Proposed approach provides an opportunity to compare new extracted or acquired fuzzy knowledge with previously obtained ones, and to detect their equivalent parts by creation of corresponding fuzzy homogeneous classes of objects. Computed intersection of fuzzy homogeneous classes of objects can be used for the efficient integration of the new fuzzy knowledge into the knowledge base, avoiding such kinds of redundancy as similarity and inclusion. The main idea of the proposed approach is illustrated with the particular example of intersection of fuzzy homogeneous classes of objects.

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References

  1. Berzal, F., Marín, N., Pons, O., Vila, M.A.: Using classical object-oriented features to build a fuzzy o-o database system. In: Lee, J. (ed.) Software Engineering with Computational Intelligence, Studies in Fuzziness and Soft Computing, vol. 121, pp. 131–155. Springer, Cham (2003). https://doi.org/10.1007/978-3-540-36423-8_6

  2. Berzal, F., Marín, N., Pons, O., Vila, M.A.: Managing fuzziness on conventional object-oriented platforms. Int. J. Intell. Syst. 22(7), 781–803 (2007). https://doi.org/10.1002/int.20228

    Article  MATH  Google Scholar 

  3. Booch, G., et al.: Object-Oriented Analysis and Design with Applications, 3rd edn. Object Technology Series, Addison-Wesley Professional, Boston (2007)

    Google Scholar 

  4. Bordogna, G., Pasi, G., Lucarella, D.: A fuzzy object-oriented data model for managing vague and uncertain information. Int. J. Intell. Syst. 14(7), 623–651 (1999). https://doi.org/10.1002/(SICI)1098-111X(199907)14:7<623::AID-INT1>3.0.CO;2-G

    Article  Google Scholar 

  5. Craig, I.D.: Object-Oriented Programming Languages: Interpretation. UTCS, Springer, London (2007)

    Google Scholar 

  6. Ma, Z.M., Mili, F.: Handling fuzzy information in extended possibility-based fuzzy relational databases. Int. J. Intell. Syst. 17(10), 925–942 (2002). https://doi.org/10.1002/int.10057

    Article  MATH  Google Scholar 

  7. Ma, Z.M., Yan, L., Zhang, F.: Modeling fuzzy information in UML class diagrams and object-oriented database models. Fuzzy Sets Syst. 186(1), 26–46 (2012). https://doi.org/10.1016/j.fss.2011.06.015

    Article  MathSciNet  Google Scholar 

  8. Ma, Z.M., Zhang, W.J., Ma, W.Y.: Assessment of data redundancy in fuzzy relational databases based on semantic inclusion degree. Inform. Process. Lett. 72(1–2), 25–29 (1999). https://doi.org/10.1016/S0020-0190(99)00124-6

    Article  MathSciNet  MATH  Google Scholar 

  9. Ma, Z.M., Zhang, W.J., Ma, W.Y.: Extending object-oriented databases for fuzzy information modeling. Inform. Syst. 29(5), 421–435 (2004). https://doi.org/10.1016/S0306-4379(03)00038-3

    Article  Google Scholar 

  10. Marín, N., Pons, O., Vila, M.A.: Fuzzy types: a new concept of type for managing vague structures. Int. J. Intell. Syst. 15(11), 1061–1085 (2000). https://doi.org/10.1002/1098-111X(200011)15:11<1061::AID-INT5>3.0.CO;2-A

    Article  MATH  Google Scholar 

  11. Marín, N., Pons, O., Vila, M.A.: A strategy for adding fuzzy types to an object-oriented database system. Int. J. Intell. Syst. 16(7), 863–880 (2001). https://doi.org/10.1002/int.1039

    Article  MATH  Google Scholar 

  12. Ndousse, T.D.: Intelligent systems modeling with reusable fuzzy objects. Int. J. Intell. Syst. 12(2), 137–152 (1997). https://doi.org/10.1002/(SICI)1098-111X(199702)12:2<137::AID-INT2>3.0.CO;2-R

    Article  MATH  Google Scholar 

  13. Terletskyi, D.: Object-Oriented Knowledge Extraction using Universal Exploiters. In: Proceedings of 12th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT), pp. 257–266. Lviv, Ukraine (September 2017). https://doi.org/10.1109/STC-CSIT.2017.8098782

  14. Terletskyi, D.A., Provotar, A.I.: Fuzzy object-oriented dynamic networks. I. Cybern. Syst. Anal. 51(1), 34–40 (2015). https://doi.org/10.1007/s10559-015-9694-0

    Article  MathSciNet  MATH  Google Scholar 

  15. Terletskyi, D.A., Provotar, A.I.: Fuzzy object-oriented dynamic networks. II. Cybern. Syst. Anal. 52(1), 38–45 (2016). https://doi.org/10.1007/s10559-016-9797-2

    Article  MathSciNet  MATH  Google Scholar 

  16. Terletskyi, D.O.: exploiters-based knowledge extraction in object-oriented knowledge representation. In: Suraj, Z., Czaja, L. (eds.) Proceedings of 24th International Workshop, Concurrency, Specification & Programming, CS&P 2015, vol. 2, pp. 211–221. Rzeszow, Poland (September 2015)

    Google Scholar 

  17. Terletskyi, D.O.: Algorithms for runtime generation of homogeneous classes of objects. In: Proceedings of International Conference Cyber Security Computer Science, ICONCS 2018. pp. 160–164. Safranbolu, Turkey (October 2018)

    Google Scholar 

  18. Terletskyi, D.O.: Run-time class generation: algorithms for intersection of homogeneous and inhomogeneous classes. In: Proceedings of IEEE 2019 14th International Computer Sciences and Information Technologies (CSIT), pp. 272–277. Lviv, Ukraine (September 2019). https://doi.org/10.1109/STC-CSIT.2019.8929736

  19. Terletskyi, D.O.: Run-time class generation: algorithms for union of homogeneous and inhomogeneous classes. In: Domaševičius, R., Vasiljevienė, G. (eds.) Information and Software Technologies. ICIST 2019, Communications in Computer and Information Science CCIS, vol. 1078, pp. 148–160. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30275-7_12

    Chapter  Google Scholar 

  20. Terletskyi, D.O., Provotar, O.I.: Algorithm for intersection of fuzzy homogeneous classes of objects. In: Proceedings of IEEE 2020 15th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT), vol. 2, pp. 314–317. Zbarazh, Ukraine (September 2020)

    Google Scholar 

  21. Thang, D.V.: Algebraic operations in fuzzy object-oriented databases based on hedge algebras. In: Cong Vinh P., Ha Huy Cuong N., V.E. (eds.) Context-Aware Systems and Applications, and Nature of Computation and Communication. ICTCC 2017, ICCASA 2017, LNICST, vol. 217, pp. 124–134. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-77818-1_12

  22. Yan, L., Ma, Z., Zhang, F.: Fuzzy XML Data Management, Studies in Fuzziness and Soft Computing, vol. 311. Springer, Berlin (2014). https://doi.org/10.1007/978-3-642-44899-7

    Book  Google Scholar 

  23. Yan, L., Ma, Z.M., Zhang, F.: Algebraic operations in fuzzy object-oriented databases. Inf. Syst. Front. 16, 543–556 (2014). https://doi.org/10.1007/s10796-012-9359-8

    Article  Google Scholar 

  24. Zadeh, L.A.: Fuzzy sets. Inform. Control 8(3), 338–353 (1965). https://doi.org/10.1016/S0019-9958(65)90241-X

  25. Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning. I. Inform. Sci. 8(3), 199–249 (1975). https://doi.org/10.1016/0020-0255(75)90036-5

    Article  MathSciNet  MATH  Google Scholar 

  26. Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning. II. Inform. Sci. 8(4), 301–357 (1975). https://doi.org/10.1016/0020-0255(75)90046-8

    Article  MathSciNet  MATH  Google Scholar 

  27. Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning. III. Inform. Sci. 9(1), 43–80 (1975). https://doi.org/10.1016/0020-0255(75)90017-1

    Article  MathSciNet  MATH  Google Scholar 

  28. Zimmermann, H.J.: Fuzzy Set Theory - and Its Applications, 4th edn. Springer, Dordrecht (2001)

    Book  Google Scholar 

  29. Zvieli, A., Chen, P.P.: Entity – Relationship modeling and fuzzy databases. In: Proceedings IEEE 2nd International Conference Data Engineering, pp. 320–327. Los Angeles, CA, USA (February 1986). https://doi.org/10.1109/ICDE.1986.7266236

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Authors and Affiliations

  1. Taras Shevchenko National University of Kyiv, Kyiv, Ukraine

    Dmytro O. Terletskyi & Oleksandr I. Provotar

Authors
  1. Dmytro O. Terletskyi
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  2. Oleksandr I. Provotar
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Correspondence to Dmytro O. Terletskyi .

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Editors and Affiliations

  1. Department of Artificial Intelligence, Lviv Polytechnic National University, Lviv, Ukraine

    Natalya Shakhovska

  2. Institute of Computer Science and Information Technologies, Lviv Polytechnic National University, Lviv, Ukraine

    Mykola O. Medykovskyy

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Terletskyi, D.O., Provotar, O.I. (2021). Intersection of Fuzzy Homogeneous Classes of Objects. In: Shakhovska, N., Medykovskyy, M.O. (eds) Advances in Intelligent Systems and Computing V. CSIT 2020. Advances in Intelligent Systems and Computing, vol 1293. Springer, Cham. https://doi.org/10.1007/978-3-030-63270-0_21

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