Improving the Compression Efficiency for News Web Service Using Semantic Relations Among Webpages

Xiao Wei, Xiangfeng Luo and Qing Li
Additional contact information
Xiao Wei: Shanghai University, Shanghai, China & Shanghai Institute of Technology, Shanghai, China & City University of Hong Kong, Hong Kong
Xiangfeng Luo: Shanghai University, Shanghai, China
Qing Li: City University of Hong Kong, Hong Kong

International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 2013, vol. 7, issue 2, 49-64

Abstract: Both compression and decompression play important roles in a web service system. High compression ratio helps to save the storage, while fast decompression contributes to decreasing the response time of service. Specifically focusing on the news web service, this paper proposes a compression mechanism to improve the efficiency of compression and decompression simultaneously by taking advantage of the semantic relations among webpages. Firstly, webpages are clustered into news topics according to the similarity semantic relation among webpages. Webpages belonging to the same topic have much duplicate content, which can improve the compression ratio when using delta-compression. Secondly, associated news topics are detected with the help of multiple-semantics link network of news topics. Associated topics are compressed into the same zip file which may decrease the times of decompression according to the habit of a user’s reading news on the Web. The authors apply the proposed compression mechanism to a practical news search engine and the experimental results show that it has high compression ratio and fast decompression speed as well.

Date: 2013
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 18/ijcini.2013040104 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jcini0:v:7:y:2013:i:2:p:49-64

Access Statistics for this article

International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) is currently edited by Kangshun Li

More articles in International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) from IGI Global
Bibliographic data for series maintained by Journal Editor ().