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.
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Notes
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The editor is distributed by the LICEF research center and it is the most recent contribution comparing to editors MOT2.3 and MOTplus. It is available on http://poseidon.licef.ca/gmot/.
References
Nonaka, I., Takeuchi, H.: The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation. Oxford University Press, New York (1995)
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)
Liebowitz, J., Frank, M.: Knowledge Management and E-Learning. CRC Press, Boca Raton (2016)
Michael, Y.: The knowledge warehouses reusing knowledge components. Perform. Improv. Q. 12(3), 132–140 (1999)
Dymond, A.: The knowledge warehouse: the next step beyond the data warehouse. In: Data Warehousing and Enterprise Solutions, SAS Users Group International 27 (2002)
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)
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)
Zaki, M.J., Meira, W.: Data Mining and Analysis: Fundamental Concepts and Algorithms. Cambridge University Press, Cambridge (2014)
Paquette, G.: Knowledge and Skills Modeling: A Graphical Language for Designing and Learning. University of Quebec Press, Sainte-Foy (2002). (In French)
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)
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)
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)
Dinarelli, M., Moschitti, A., Riccardi, G.: Hypotheses selection for re-ranking semantic annotations. In: 2010 IEEE Spoken Language Technology Workshop, pp. 407–411 (2010)
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)
Collins, A., Quillian, M.: Retrieval time from semantic memory. J. Verbal Learn. Verbal Behav. 8(2), 240–247 (1969)
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Ayadi, R., Hachaichi, Y., Feki, J. (2018). Association Rules Transformation for Knowledge Integration and Warehousing. In: Abraham, A., Muhuri, P., Muda, A., Gandhi, N. (eds) Intelligent Systems Design and Applications. ISDA 2017. Advances in Intelligent Systems and Computing, vol 736. Springer, Cham. https://doi.org/10.1007/978-3-319-76348-4_37
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DOI: https://doi.org/10.1007/978-3-319-76348-4_37
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