Building hierarchical keyword level association link networks for web events semantic analysis

Xuan, J; Luo, X; Zhang, S; Xu, Z; Liu, H; Ye, F

Building hierarchical keyword level association link networks for web events semantic analysis

Xuan, J

Luo, X Zhang, S Xu, Z Liu, H Ye, F

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Publication Type:: Conference Proceeding
Citation:: Proceedings - IEEE 9th International Conference on Dependable, Autonomic and Secure Computing, DASC 2011, 2011, pp. 987 - 994
Issue Date:: 2011-12-01

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Field	Value	Language
dc.contributor.author	Xuan, J https://orcid.org/0000-0002-8367-6908	en_US
dc.contributor.author	Luo, X	en_US
dc.contributor.author	Zhang, S	en_US
dc.contributor.author	Xu, Z	en_US
dc.contributor.author	Liu, H	en_US
dc.contributor.author	Ye, F	en_US
dc.date.issued	2011-12-01	en_US
dc.identifier.citation	Proceedings - IEEE 9th International Conference on Dependable, Autonomic and Secure Computing, DASC 2011, 2011, pp. 987 - 994	en_US
dc.identifier.isbn	9780769546124	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120602
dc.description.abstract	With the increase of information scale of web events on the time, it is extremely difficult and challenging to grasp the semantics of web events artificially, because of the limitation of the time and energy of human beings. Herein, we propose a method to map the web event to keyword level association link network (KALN) for deep analysis of the semantics of web events, such as the evolution semantics of web events. Firstly, the original KALN is constructed at a given time by traditional data mining technologies. Then, the hierarchical KALN, consisted of Theme Layer Network, Backbone Layer Network and Tidbit Layer Network, is built based on the original KALN by information entropy to identify the different semantic levels of the web event, including stable semantics, sub-stable semantics and unstable semantics. With the semantic analysis of hierarchical KALN, human could easily gain a thorough understanding of the web event. Finally, experiments show that our method can effectively capture the different level semantics of web events. © 2011 IEEE.	en_US
dc.relation.ispartof	Proceedings - IEEE 9th International Conference on Dependable, Autonomic and Secure Computing, DASC 2011	en_US
dc.relation.isbasedon	10.1109/DASC.2011.163	en_US
dc.title	Building hierarchical keyword level association link networks for web events semantic analysis	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

With the increase of information scale of web events on the time, it is extremely difficult and challenging to grasp the semantics of web events artificially, because of the limitation of the time and energy of human beings. Herein, we propose a method to map the web event to keyword level association link network (KALN) for deep analysis of the semantics of web events, such as the evolution semantics of web events. Firstly, the original KALN is constructed at a given time by traditional data mining technologies. Then, the hierarchical KALN, consisted of Theme Layer Network, Backbone Layer Network and Tidbit Layer Network, is built based on the original KALN by information entropy to identify the different semantic levels of the web event, including stable semantics, sub-stable semantics and unstable semantics. With the semantic analysis of hierarchical KALN, human could easily gain a thorough understanding of the web event. Finally, experiments show that our method can effectively capture the different level semantics of web events. © 2011 IEEE.

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