Webnox: web knowledge extraction

Urbansky, D, Thom, J and Feldmann, M 2008, 'Webnox: web knowledge extraction', in R. McArthur, P. Thomas, A. Turpin, M. Wu (ed.) Proceedings of the Thirteenth Australasian Document Computing Symposium, Hobart, Tasmania, 8 December 2008, pp. 27-37.


Document type: Conference Paper
Collection: Conference Papers

Title Webnox: web knowledge extraction
Author(s) Urbansky, D
Thom, J
Feldmann, M
Year 2008
Conference name Thirteenth Australian Document Computing Symposium (ADCS 2008)
Conference location Hobart, Tasmania
Conference dates 8 December 2008
Proceedings title Proceedings of the Thirteenth Australasian Document Computing Symposium
Editor(s) R. McArthur, P. Thomas, A. Turpin, M. Wu
Publisher RMIT University
Place of publication Australia
Start page 27
End page 37
Total pages 11
Abstract The paper describes and evaluates a system for extracting knowledge from the web that uses a domain independent fact extraction approach and a self supervised learning algorithm. Using a trust algorithm, the precision of the system is improved to over 70% compared with a baseline of 52%. The paper describes and evaluates a system for extracting knowledge from the web that uses a domain independent fact extraction approach and a self supervised learning algorithm. Using a trust algorithm, the precision of the system is improved to over 70% compared with a baseline of 52%. The paper describes and evaluates a system for extracting knowledge from the web that uses a domain independent fact extraction approach and a self supervised learning algorithm. Using a trust algorithm, the precision of the system is improved to over 70% compared with a baseline of 52%. The paper describes and evaluates a system for extracting knowledge from the web that uses a domain independent fact extraction approach and a self supervised learning algorithm. Using a trust algorithm, the precision of the system is improved to over 70% compared with a baseline of 52%. The paper describes and evaluates a system for extracting knowledge from the web that uses a domain independent fact extraction approach and a self supervised learning algorithm. Using a trust algorithm, the precision of the system is improved to over 70% compared with a baseline of 52%.
Subjects Information Retrieval and Web Search
Keyword(s) information extraction
web mining
ISBN 139781921426216
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