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A Firefly Algorithm-based Approach for Pseudo-Relevance Feedback: Application to Medical Database.

Ilyes Khennak¹, Habiba Drias²

¹Laboratory for Research in Artificial Intelligence, Computer Science Department, USTHB, BP 32 El Alia, 16111, Bab Ezzouar, Algiers, Algeria. ikhennak@usthb.dz.

Journal of Medical Systems

|September 29, 2016

Summary

This summary is machine-generated.

This study introduces a novel Firefly Algorithm for query expansion, improving search engine accuracy by adding relevant keywords. The method enhances information retrieval effectiveness with low computational cost.

Keywords:

Firefly Algorithm MEDLINE Medical data management Pseudo-relevance feedback Query expansion Swarm intelligence Web intelligence

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Area of Science:

Information Retrieval
Computer Science
Artificial Intelligence

Background:

Search queries often use imprecise keywords, leading to retrieval failures.
Query expansion is a key method to improve search engine performance by augmenting original queries.

Purpose of the Study:

To propose a new Firefly Algorithm-based approach for query expansion.
To enhance retrieval effectiveness and maintain low computational complexity.

Main Methods:

Utilized a Firefly Algorithm to select the optimal expanded query from candidate sets.
Determined the expanded query length empirically.
Evaluated the approach on the MEDLINE database.

Main Results:

The proposed Firefly Algorithm approach demonstrated superior effectiveness compared to existing methods.
The approach proved more efficient than state-of-the-art techniques.

Conclusions:

The Firefly Algorithm is effective for query expansion in information retrieval.
This method offers an efficient way to improve search engine performance and user satisfaction.