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The cooperative system for information retrieval

Authors: Abdelouahab Mekaouche;

The cooperative system for information retrieval

Abstract

We describe our information retrieval system which allows a convivial access to databases. It is based on a multi-expert architecture using a database management system and a blackboard to control the progressive analysis of the user's sentence. We have integrated a numerical method based on fuzzy rules to deal with uncertainty. This method is used to optimize the analysis process and the cooperation between the different experts. On the other hand, the goal of our recent research is to ease or eliminate the knowledge-acquisition bottleneck for expert system creation and to make a connectionist model behave as much as possible like an expert system. We describe our experience using neural networks to represent the knowledge bases of the different experts (i.e., lexical entries expert, homographs expert, template expert, grammatical word expert and words expert).

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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Average
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