Solving Hanging Relevancy using Genetic Algorithm

Singh, Ashutosh Kumar; Ravi, Kumar; Goh, Kwang Leng Alex

doi:10.1109/URKE.2012.6319593

Access Status

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Authors

Singh, Ashutosh Kumar

Ravi, Kumar

Goh, Kwang Leng Alex

Date

2012

Type

Conference Paper

Metadata

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Citation

Singh, A.K. and Ravi, K. and Goh, K.L.A. 2012. Solving hanging relevancy using genetic algorithm, in 2nd IEEE International Conference on Uncertainty Reasoning and Knowledge Engineering (URKE), Aug 14-15 2012, pp. 9-12. Jakarta, Indonesia: Institute of Electrical and Electronics Engineers.

Source Title

Proceeding of 2nd IEEE International Conference on Uncertainty Reasoning and Knowledge Engineering

Source Conference

2nd IEEE International Conference on Uncertainty Reasoning and Knowledge Engineering

DOI

10.1109/URKE.2012.6319593

ISBN

978-1-4673-1459-6

School

Curtin Sarawak

URI

http://hdl.handle.net/20.500.11937/26122

Collection

Curtin Research Publications

Abstract

Continuous growth of hanging pages with Web makes a significant problem for ranking in the information retrieval. Exclusion of these pages in ranking calculation can give biased/inconsistent result. On the other hand inclusion of these pages will reduce the speed significantly. However most of the IR ranking algorithms exclude the hanging pages. But there are relevant and important hanging pages on the Web and they cannot be ignored because of the complexity in computation and time. In our proposed method, we include the relevant hanging pages in the ranking. Relevancy or non-relevancy of hanging pages is achieved by application of Genetic Algorithm (GA).