TY  - CONF
ID  - www200919
UR  - http://www2009.eprints.org/19/
A1  - Yang, Jiang-Ming
A1  - Cai, Rui
A1  - Wang, Yida
A1  - Zhu, Jun
A1  - Zhang, Lei
A1  - Ma, Wei-Ying
Y1  - 2009/04//
N2  - Web forums have become an important data resource for many web applications, but extracting structured data from unstructured web forum pages is still a challenging task due to both complex page layout designs and unrestricted user created posts. In this paper, we study the problem of structured data extraction from various web forum sites. Our target is to ?nd a solution as general as possible to extract structured data, such as post title, post author, post time, and post content from any forum site. In contrast to most existing information extraction methods, which only leverage the knowledge inside an individual page, we incorporate both page-level and site-level knowledge and employ Markov logic networks (MLNs) to effectively integrate all useful evidence by learning their importance automatically. Site-level knowledge includes (1) the linkages among different object pages, such as list pages and post pages, and (2) the interrelationships of pages belonging to the same object. The experimental results on 20 forums show a very encouraging information extraction performance, and demonstrate the ability of the proposed approach on various forums. We also show that the performance is limited if only page-level knowledge is used, while when incorporating the site-level knowledge both precision and recall can be signi?cantly improved.
TI  - Incorporating Site-Level Knowledge to Extract Structured Data from Web Forums
SP  - 181
M2  - Madrid, Spain
AV  - public
EP  - 181
T2  - 18th International World Wide Web Conference
ER  -