Cognitive Filtering of Textual Information Agents Based Implementation

Cognitive Filtering of Textual Information ‎Agents Based Implementation

Omar Nouali1, Nadia Nouali-Taboudjemat1, and Bernard Toursel2‎
‎1Theory and computer engineering, DTISI, CERIST, Algeria‎
‎2University of Sciences and Technologies of Lille, France



Abstract: The study presented in this paper has multiple objectives. The first objective is to automate the information filtering process by ‎taking into account the relative importance of information and resources needed for its treatment. The second one is to ‎demonstrate the applicability and contribution of an agents based implementation to automatic information filtering. The third ‎one is to show how learning can improve the effectiveness of filtering and that automatic learning is necessary in the design of ‎automatic information filtering systems. We propose an open, dynamic and evolving solution that offers to the filtering process ‎the opportunity to learn, exploit the learned knowledge and adapt itself to the application environment. We have adopted ‎agents to improve the response time compared to a sequential algorithmic solution. To validate our filtering approach, we led ‎a set of experiments to evaluate performances of the techniques and tools we have developed.‎

Keywords: Information filtering, machine learning, linguistic agents, and filtering criteria.‎

Received March 25,  2009; accepted August 4, 2009‎

  

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