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Fuzzy query processing for document retrieval based on extended fuzzy concept networks.

S M Chen¹, Y J Horng

¹Dept. of Electron. Eng., Nat. Taiwan Univ. of Sci. & Technol., Taipei.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|February 7, 2008

Summary

This summary is machine-generated.

This study introduces a novel fuzzy query processing method for document retrieval using extended fuzzy concept networks. This approach enables more flexible and intelligent fuzzy querying through various relationship types.

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Area of Science:

Information Science
Computer Science
Artificial Intelligence

Background:

Traditional document retrieval methods often lack flexibility in handling ambiguous or imprecise user queries.
Existing fuzzy query techniques may not fully capture the complex relationships between concepts within a knowledge domain.

Purpose of the Study:

To propose a new method for fuzzy query processing in document retrieval.
To leverage extended fuzzy concept networks for enhanced query capabilities.
To enable more flexible and intelligent fuzzy querying for users.

Main Methods:

Modeling extended fuzzy concept networks using relation and relevance matrices.
Representing four types of fuzzy relationships: positive association, negative association, generalization, and specialization.
Inferring implicit fuzzy relationships and relevance degrees via transitive closure of matrices.

Main Results:

The proposed method supports positive, negative, generalization, and specialization queries.
Implicit relationships and relevance degrees between concepts can be effectively inferred.
Demonstrates a more flexible and intelligent approach to fuzzy query processing.

Conclusions:

Extended fuzzy concept networks provide a robust framework for advanced document retrieval.
The developed method enhances user interaction by allowing nuanced and complex query formulations.
This research contributes to more sophisticated and user-centric information retrieval systems.