Conference Proceeding Article
Wikipedia is presently the largest free-and-open online encyclopedia collaboratively edited and maintained by volunteers. While Wikipedia offers full-text search to its users, the accuracy of its relevance-based search can be compromised by poor quality articles edited by non-experts and inexperienced contributors. In this paper, we propose a framework that re-ranks Wikipedia search results considering article quality. We develop two quality measurement models, namely Basic and PeerReview, to derive article quality based on co-authoring data gathered from articles' edit history. Compared with Wikipedia's full-text search engine, Google and Wikiseek, our experimental results showed that (i) quality-only ranking produced by PeerReview gives comparable performance to that of Wikipedia and Wikiseek; (ii) PeerReview combined with relevance ranking outperforms Wikipedia's full-text search significantly, delivering search accuracy comparable to Google.
Databases and Information Systems | Numerical Analysis and Scientific Computing
Data Management and Analytics
WIDM '07: Proceedings of the 9th Annual ACM International Workshop on Web Information and Data Management: November 9, 2007, Lisbon, Portugal
City or Country
HU, Meiqun; LIM, Ee Peng; SUN, Aixin; LAUW, Hady Wirawan; and VUONG, Ba-Quy.
On Improving Wikipedia Search using Article Quality. (2007). WIDM '07: Proceedings of the 9th Annual ACM International Workshop on Web Information and Data Management: November 9, 2007, Lisbon, Portugal. 145-152. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/1264