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This paper describes the design and implementation of a computational model for Arabic natural language semantics, a semantic parser for capturing the deep semantic representation of Arabic text. The parser represents a major part of an Interlingua-based machine translation system for translating Arabic text into Sign Language. The parser follows a frame-based analysis to capture the overall meaning of Arabic text into a formal representation suitable for NLP applications that need for deep semantics representation, such as language generation and machine translation. We will show the representational power of this theory for the semantic analysis of texts in Arabic, a language which differs substantially from English in several ways. We will also show that the integration of WordNet and FrameNet in a single unified knowledge resource can improve disambiguation accuracy. Furthermore, we will propose a rule based algorithm to generate an equivalent Arabic FrameNet, using a lexical resource alignment of FrameNet1.3 LUs and WordNet3.0 synsets for English Language. A pilot study of motion and location verbs was carried out in order to test our system. Our corpus is made up of more than 2000 Arabic sentences in the domain of motion events collected from Algerian first level educational Arabic books and other relevant Arabic corpora.

Many Intelligent Tutoring Systems (ITS) using a leaming machine companion called LearningCompanion Systems (LCS) has been developed these last years. These systems involve three agents: a teacher, a student and a companion. These systems use the idea that the social interaction has an influence on the cognitive development. The basic idea is that the student can learn from his mistakes and can beneflt from the companion s mistakes. The companion is looked for assistance, and other times as a troublemaker. The goal of the learning companion is to stimulate through collaboration competition and demonstration. This paper outlines the architecture of an intelligent tutoring system based on the Double Test Leaming (DTL) strategy and using two companions as a eo-students where one of both is a troublemaker. The system called DB-TUTOR is proposed to teaching the databases for the students of the third year of the graduate cycle.The DTL strategy introduces a co-student in the learning session that receives the same training as the human student. The idea of the DTL strategy, which contains four phases, is based on that the student will benefit from the eo-students s mistakes and their behavior. When the training phase is completed, the tutor will test the eo-students. In this phase called Post-Testl, the student will follow the interaction between the tutor and the eo-students -Mohamed Tayeb LASKRI

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This article presents OntoCell, a cellular ontology that we constructed and edited under Protege2000. It regroups and unifies the main concepts and relations related to the cell's structure and behavior. OntoCell has been validated by experts in biology (by the UMRS INSERM 514). Moreover, it will be validated in the context of the development of a multi-agent system simulating the behavior of a cellular population

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