PERO2: Machine Teaching based on a Normalized Ontological Knowledge Base

M. Chahbar¹ , A. Elhore² , Y. Askane³

1. Dept. Computer Science, ChouaĂŻb Doukkali University, El-Jadida, Morocco.
2. Dept. Computer Science, ChouaĂŻb Doukkali University, El-Jadida, Morocco.
3. Dept. Computer Science, ChouaĂŻb Doukkali University, El-Jadida, Morocco.

Correspondence should be addressed to: mostafa.chahbar1@gmail.com.

Section:Research Paper, Product Type: Isroset-Journal
Vol.5 , Issue.5 , pp.63-74, Oct-2017

CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v5i5.6374

Online published on Oct 30, 2017

Copyright © M. Chahbar, A. Elhore, Y. Askane . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
In order to extend the deployment of Machines Teaching in educational institutions, by facilitating their appropriation by human learners (teachers and students) and to explore the motivation of interactive Machines Teaching to solve problems by making a human as an indispensable pillar of the teaching process. This research framework is part of PERO2 project (intelligent system for learning of reasoning and problem-solving dedicated to the physical science domain specifically the teaching subdomain “Electricity”) which is about the integration of a semantic layer within the architecture of PERO2 this by means of representing the system’s knowledge base via a normalized domain ontology and then integrating the exploitable ontological knowledge base instead of a relational database. To design this ontological knowledge base, we proposed in the current research work a hybrid construction method taking into account two main phases: (*) Conception phase of our domain ontology called “OntoPhyScEx”. (**) Normalization also called Semantic validation phase of this domain ontology. As for the integration and exploitation of this domain ontology, it‘s been discussed in another paper.

Key-Words / Index Term :
Domain Ontology, Normalization, Machines Teaching, Knowledge Base, Learning of Reasoning, Problem-Solving

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