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)


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.


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


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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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