Finding related pages in the World Wide Web
WWW '99 Proceedings of the eighth international conference on World Wide Web
Discovering unexpected information from your competitors' web sites
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
A comparative web browser (CWB) for browsing and comparing web pages
WWW '03 Proceedings of the 12th international conference on World Wide Web
Topic-structure-based complementary information retrieval and its application
ACM Transactions on Asian Language Information Processing (TALIP)
Easiest-first search: towards comprehension-based web search
Proceedings of the 18th ACM conference on Information and knowledge management
Detecting Wikipedia vandalism with active learning and statistical language models
Proceedings of the 4th workshop on Information credibility
On measuring the quality of Wikipedia articles
Proceedings of the 4th workshop on Information credibility
Trust in wikipedia: how users trust information from an unknown source
Proceedings of the 4th workshop on Information credibility
Content hole search in community-type content using Wikipedia
Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services
Good quality complementary information for multilingual wikipedia
WISE'12 Proceedings of the 13th international conference on Web Information Systems Engineering
Extracting complementary information from Wikipedia articles of different languages
International Journal of Business Intelligence and Data Mining
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With the huge amount of data on the Web, looking for desired information can be a time consuming task. Wikipedia is a very helpful tool as it is the largest most popular general reference site on the internet. Most search engines rank Wikipedia pages among the top listed results. However, because many articles on Wikipedia are manually updated by users, there are several articles that lack information and need to be upgraded. Those lacking information can sometimes be found on the web. Uprooting this information from the web will involve a time consuming process of reading, analyzing and summarizing the information for the user. In order to support the user search process and help Wikipedia contributors in the updating process of articles, we propose a method of finding valuable complementary information on the web. Experiments showed that our method was quite effective in retrieving important complementary information from the web pages.