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Association Rules Transformation for Knowledge Integration and Warehousing

  • Rim Ayadi
  • Yasser Hachaichi
  • Jamel Feki
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 736)

Abstract

Knowledge management process is a set of procedures and tools applied to facilitate capturing, sharing and effectively using knowledge. However, knowledge collected from organizations is generally expressed in various formalisms, therefore it is heterogeneous. Thus, a Knowledge Warehouse (KW), which is a solution for implementing all phases of the knowledge management process, should solve this structural heterogeneity before loading and storing knowledge. In this paper, we are interested in knowledge normalization. More accurately, we firstly introduce our proposed architecture for a KW, and then we present the MOT (Modeling with Object Types) language for knowledge representation. Since our objective is to transform heterogeneous knowledge into MOT, as a pivot model, we suggest a meta-model for the MOT and another for the explicit knowledge extracted through the association rules technique. Thereafter, we define eight transformation rules and an algorithm to transform an association rules model into the MOT model.

Keywords

Knowledge Warehouse MOT language Transformation rules Data mining Heterogeneous knowledge Knowledge Normalization 

References

  1. 1.
    Nonaka, I., Takeuchi, H.: The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation. Oxford University Press, New York (1995)Google Scholar
  2. 2.
    Nemati, H.R., Steiger, D.M., Iyer, L.S., Herschel, R.T.: Knowledge warehouse: an architectural integration of knowledge management, decision support, artificial intelligence and data warehousing. Decis. Support Syst. 33(2), 143–161 (2002)CrossRefGoogle Scholar
  3. 3.
    Liebowitz, J., Frank, M.: Knowledge Management and E-Learning. CRC Press, Boca Raton (2016)Google Scholar
  4. 4.
    Michael, Y.: The knowledge warehouses reusing knowledge components. Perform. Improv. Q. 12(3), 132–140 (1999)Google Scholar
  5. 5.
    Dymond, A.: The knowledge warehouse: the next step beyond the data warehouse. In: Data Warehousing and Enterprise Solutions, SAS Users Group International 27 (2002)Google Scholar
  6. 6.
    Ayadi, R., Hachaichi, Y., Feki, J.: Towards knowledge warehouses: definition and architecture. In: 7th Edition of the Conference on Advances in Decisional Systems, Marrakech, Morocco (2013) (In French)Google Scholar
  7. 7.
    Basciani, F., Di Rocco, J., Di Ruscio, D., Iovino, L., Pierantonio, A.: Automated Clustering of Metamodel Repositories, pp. 342–358. Springer International Publishing, Cham (2016)Google Scholar
  8. 8.
    Zaki, M.J., Meira, W.: Data Mining and Analysis: Fundamental Concepts and Algorithms. Cambridge University Press, Cambridge (2014)zbMATHGoogle Scholar
  9. 9.
    Paquette, G.: Knowledge and Skills Modeling: A Graphical Language for Designing and Learning. University of Quebec Press, Sainte-Foy (2002). (In French)Google Scholar
  10. 10.
    Ayadi, R., Hachaichi, Y., Alshomrani, S., Feki, J.: Decision tree transformation for knowledge warehousing. In: Proceedings of the 17th International Conference on Enterprise Information Systems, ICEIS 2015, Barcelona, Spain, 27–30 April 2015, vol. 1, pp. 616–623 (2015)Google Scholar
  11. 11.
    Ayadi, R., Hachaichi, Y., Feki, J.: MOT knowledge model integration rules for knowledge warehousing. In: Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 21st International Conference KES-2017, Marseille, France, 6–8 September 2017, pp. 544–553 (2017)Google Scholar
  12. 12.
    Héon, M., Basque, J., Paquette, G.: Semantics validation of a semi-formal knowledge model with ontocase. In: Act of 21st Francophone Days of Knowledge Engineering, Nimes, France, pp. 55–66 (2010). (In French)Google Scholar
  13. 13.
    Dinarelli, M., Moschitti, A., Riccardi, G.: Hypotheses selection for re-ranking semantic annotations. In: 2010 IEEE Spoken Language Technology Workshop, pp. 407–411 (2010)Google Scholar
  14. 14.
    Canas, A.J., Ford, K.M., Novak, J.D., Hayes, P., Reichherzer, T.R., Suri, N.: Online concept maps: enhancing collaborative learning by using technology with concept maps. Sci. Teach. 68, 49–51 (2001)Google Scholar
  15. 15.
    Collins, A., Quillian, M.: Retrieval time from semantic memory. J. Verbal Learn. Verbal Behav. 8(2), 240–247 (1969)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Multimedia Information Systems and Advanced Computing LaboratoryUniversity of SfaxSfaxTunisia
  2. 2.Department of Computer Science and Quantitative MethodsHigher Institute of Business Administration of SfaxSfaxTunisia
  3. 3.Faculty of Computing and ITUniversity of JeddahJeddahSaudi Arabia

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