Neural-Symbolic Machine-Learning for Knowledge Discovery and Adaptive Information Retrieval

In this paper, a model for an information retrieval system is proposed which takes into account that knowledge about documents and information need of users are dynamic. Two methods are combined, one qualitative or symbolic and the other quantitative or numeric, which are deemed suitable for many clustering contexts, data analysis, concept exploring and knowledge discovery. These two methods may be classified as inductive learning techniques. In this model, they are introduced to build “long term" knowledge about past queries and concepts in a collection of documents. The “long term" knowledge can guide and assist the user to formulate an initial query and can be exploited in the process of retrieving relevant information. The different kinds of knowledge are organized in different points of view. This may be considered an enrichment of the exploration level which is coherent with the concept of document/query structure.

Authors:

• Hager Kammoun
• Jean Charles Lamirel
• Mohamed Ben Ahmed

Keywords:

• Information Retrieval Systems
• machine learning
• classification
• Galois lattices
• Self Organizing Map.

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