DocumentCode
1532194
Title
Uncertainty Reduction for Knowledge Discovery and Information Extraction on the World Wide Web
Author
Ji, Heng ; Deng, Hongbo ; Han, Jiawei
Author_Institution
Department of Computer Science, City University of New York, New York City, NY, USA
Volume
100
Issue
9
fYear
2012
Firstpage
2658
Lastpage
2674
Abstract
In this paper, we give an overview of knowledge discovery (KD) and information extraction (IE) techniques on the World Wide Web (WWW). We intend to answer the following questions: What kind of additional uncertainty challenges are introduced by the WWW setting to basic KD and IE techniques? What are the fundamental techniques that can be used to reduce such uncertainty and achieve reasonable KD and IE performance on the WWW? What is the impact of each novel method? What types of interactions can be conducted between these techniques and information networks to make them benefit from each other? In what way can we utilize the results in more interesting applications? What are the remaining challenges and what are the possible ways to address these challenges? We hope this can provide a road map to advance KD and IE on the WWW to a higher level of performance, portability and utilization.
Keywords
Analytical models; Hidden Markov models; Natural language processing; Text mining; Text processing; Uncertainty; World Wide Web; natural language processing; text analysis; text mining;
fLanguage
English
Journal_Title
Proceedings of the IEEE
Publisher
ieee
ISSN
0018-9219
Type
jour
DOI
10.1109/JPROC.2012.2190489
Filename
6212297
Link To Document