Paper Info
Title
Web article quality assessment in multi-dimensional space
Abstract
Nowadays user-generated content (UGC) such as Wikipedia, is emerging on the web at an explosive rate, but its data quality varies dramatically. How to effectively rate the article's quality is the focus of research and industry communities. Considering that each quality class demonstrates its specific characteristics on different quality dimensions, we propose to learn the web quality corpus by taking different quality dimensions into consideration. Each article is regarded as an aggregation of sections and each section's quality is modelled using Dynamic Bayesian Network(DBN) with reference to accuracy, completeness and consistency. Each quality class is represented by three dimension corpora, namely accuracy corpus, completeness corpus and consistency corpus. Finally we propose two schemes to compute quality ranking. Experiments show our approach performs well.
Year
DOI
Venue
2011
10.1007/978-3-642-23535-1_20
WAIM
Keywords
Field
DocType
quality class,accuracy corpus,web article quality assessment,web quality corpus,consistency corpus,explosive rate,different quality dimension,completeness corpus,quality ranking,data quality,dimension corpus,multi-dimensional space
Data mining,Multi dimensional,Data quality,Information retrieval,Ranking,Computer science,Artificial intelligence,Completeness (statistics),Machine learning,Dynamic Bayesian network,Beta distribution
Conference
Volume
ISSN
Citations 
6897
0302-9743
2
PageRank 
References 
Authors
0.38
8
4
Name
Order
Citations
PageRank
Jingyu Han1164.67
Xiong Fu292.65
Kejia Chen317915.82
Chuandong Wang450.78