MOT Knowledge Model Integration Rules for Knowledge Warehousing | Zendy

Rim Ayadi | Zendy; Yasser Hachaichi | Zendy; Jamel Feki | Zendy

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MOT Knowledge Model Integration Rules for Knowledge Warehousing

Author(s) -

Rim Ayadi,

Yasser Hachaichi,

Jamel Feki

Publication year - 2017

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2017.08.164

Subject(s) - computer science , knowledge integration , process (computing) , knowledge modeling , knowledge management , knowledge based systems , data mining , knowledge engineering , domain knowledge , programming language

A knowledge warehousing process aims to build an intelligent decision support system. It collects, homogenizes, integrates and stores knowledge for a decision-making process. In this paper, we are interested in knowledge integration. More accurately, we propose an integration process for knowledge homogenized/modeled according to the MOT (Modeling with Object Types) knowledge model. This integration process consists of three ordered steps based on the type of schemas to integrate and their similarity. For this process, we define five integration rules based on semantic relationships between elements of MOT knowledge models, and then we develop an algorithm using these integration rules.

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