Skip to main content

Abstract

Different communication mechanisms between ontologies and database (DB) systems have appeared in the last few years. However, several problems can arise during this communication, depending on the nature of the data represented and their representation structure, and these problems are often enhanced when a Fuzzy Database (FDB) is involved. An architecture that describes how such communication is established and which attends to all the particularities presented by both technologies, namely ontologies and FDB, is defined in this paper. Specifically, this proposal tries to solve the problems that emerge as a result of the use of heterogeneous platforms and the complexity of representing fuzzy data.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Astrova, I.: Reverse engineering of relational databases to ontologies. In: Bussler, C.J., Davies, J., Fensel, D., Studer, R. (eds.) ESWS 2004. LNCS, vol. 3053, pp. 327–341. Springer, Heidelberg (2004)

    Google Scholar 

  2. Barrasa, J., Corcho, O., Perez, A.G.: Fund finder: A case study of database to ontology mapping. In: Fensel, D., Sycara, K., Mylopoulos, J. (eds.) ISWC 2003. LNCS, vol. 2870, pp. 17–22. Springer, Heidelberg (2003)

    Google Scholar 

  3. Blanco, I., Martin-Bautista, M.J., Pons, O., Vila, M.A.: A mechanism for deduction in a fuzzy relational database. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 11, 47–66 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  4. Blanco, I., Martinez-Cruz, C., Serrano, J.M., Vila, M.A.: A first approach to multipurpose relational database server. Mathware and Soft Computing 12(2-3), 129–153 (2005)

    MATH  Google Scholar 

  5. Blanco, I., Martínez-Cruz, C., Vila, M.A.: Looking for Information in Fuzzy Relational Databases accessible via the Web. In: Handbook of Research on Web Information Systems Quality, pp. 300–324. Idea Group Ref. (2007)

    Google Scholar 

  6. Blanco, I.J., Vila, M.A., Martinez-Cruz, C.: The use of ontologies for representing database schemas of fuzzy information. International Journal of Intelligent Systems 23(4), 419–445 (2008)

    Article  MATH  Google Scholar 

  7. Bosc, P., Galibourg, M., Hamon, G.: Fuzzy querying with sql: Extensions and implementation aspects. Fuzzy Sets and Systems 28, 333–349 (1988)

    Article  MATH  MathSciNet  Google Scholar 

  8. Calero, C., Piattini, M.: Ontological Engineering: Principles, Methods, Tools and Languages. In: An Ontological Approach to SQL 2003, pp. 49–102. Springer, Heidelberg (2006)

    Google Scholar 

  9. Carrasco, R.A., Vila, M.A., Galindo, J.: Fsql: a flexible query language for data mining. In: Enterprise information systems IV, pp. 68–74 (2003)

    Google Scholar 

  10. Codd, E.F.: Extending the database relational model to capture more meaning. ACM Transactions on Database Systems 4, 262–296 (1979)

    Article  Google Scholar 

  11. Corcho, O., FernándezLópez, M., GómezPérez, A.: Ontological Engineering: Principles, Methods, Tools and Languages. In: Ontologies for Software Engineering and Software Technology, pp. 49–102. Springer, Heidelberg (2006)

    Google Scholar 

  12. International Organization for Standardization (ISO). Information Technology. Database language sql. parts 1 to 4 and 9 to 14. 9075-1:2003 to 9075-14:2003 International Standards Standard, No. ISO/IEC 9075: 2003 (September 2003)

    Google Scholar 

  13. Galindo, J., Medina, J.M., Pons, O., Cubero, J.C.: A server for fuzzy sql queries. In: Proceedings of the Third International Conference on Flexible Query Answering Systems, pp. 164–174 (1998)

    Google Scholar 

  14. Gómez-Pérez, A., Férnandez-López, M., Corcho-García, O.: Ontological Engineering. Springer, New york(2003)

    Google Scholar 

  15. Kacprzyk, J., Zadrozny, S.: Sqlf and fquery for access. In: IFSA World Congress and 20th NAFIPS International Conference. Joint 9th, vol. 4, pp. 2464–2469 (2001)

    Google Scholar 

  16. H. Knublauch. An ai tool for the real world. Knowledge modeling with protègè. Technical report, http://www.javaworld.com/javaworld/jw-06-2003/jw-0620-protege.html.

    Google Scholar 

  17. Ma, Z.: Fuzzy Database Modeling of Imprecise and Uncertain Engineering Information. Springer, Heidelberg (2006)

    MATH  Google Scholar 

  18. Medina, J.M., Pons, O., Vila, M.A.: Gefred. a generalized model of fuzzy relational databases. Information Sciences 76(1-2), 87–109 (1994)

    Article  Google Scholar 

  19. de Laborda Perez, C., Conrad, S.: Relational.owl: a data and schema representation format based on owl. In: CRPIT ’43: Proceedings of the 2nd Asia-Pacific conference on Conceptual modelling, pp. 89–96 (2005)

    Google Scholar 

  20. Raju, K.V.S.V.N., Majumdar, A.K.: Fuzzy functional dependencies and lossless join decomposition of fuzzy relational database systems. ACM Transactions on Database Systems 13(2), 129–166 (1988)

    Article  Google Scholar 

  21. Studer, R., Benjamins, V.R., Fensel, D.: Knowledge engineering: Principles and methods. IEEE Transactions on Data and Knowledge Eng. 25(1-2), 161–197 (1998)

    Article  MATH  Google Scholar 

  22. Vysniauskas, E., Nemuraite, L.: Transforming ontology representation from owl to relational database. Information Technology and Control 35(3A), 333–343 (2006)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Martínez-Cruz, C., Blanco, I.J., Vila, M.A. (2010). Describing Fuzzy DB Schemas as Ontologies: A System Architecture View. In: Hüllermeier, E., Kruse, R., Hoffmann, F. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications. IPMU 2010. Communications in Computer and Information Science, vol 81. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14058-7_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-14058-7_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-14057-0

  • Online ISBN: 978-3-642-14058-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics