A PSO-Based web document query optimization algorithm

Authors:
Ziqiang Wang;Xin Li;Dexian Zhang;Feng Wu
Affiliations:
School of Information Science and Engineering, Henan University of Technology, Zheng Zhou, China;School of Information Science and Engineering, Henan University of Technology, Zheng Zhou, China;School of Information Science and Engineering, Henan University of Technology, Zheng Zhou, China;School of Information Science and Engineering, Henan University of Technology, Zheng Zhou, China
Venue:
ASWC'06 Proceedings of the First Asian conference on The Semantic Web
Year:
2006

Citing 5
Cited 0

Machine learning for information retrieval: neural networks, symbolic learning, and genetic algorithms

Journal of the American Society for Information Science
Pivoted document length normalization

SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Optimizing similarity using multi-query relevance feedback

Journal of the American Society for Information Science
Applying genetic algorithms to query optimization in document retrieval

Information Processing and Management: an International Journal
A vector space model for automatic indexing

Communications of the ACM

Quantified Score

Hi-index	0.00

Visualization

Abstract

The particle swarm optimization(PSO) algorithm is a robust stochastic evolutionary algorithm based on the movement and intelligence of swarms.To efficiently retrieve relevant documents from the explosive growth of the Internet and other sources of information access,a PSO-based algorithm for Web document query optimization is presented Experimental results show that the proposed algorithm can improve the precision of document retrieval markedly compared with relevant feedback and genetic algorithm.