VisualWikiCurator: human and machine intelligence for organizing wiki content

Abstract

Corporate wikis are affected by poor adoption rates. The high interaction costs to organize and maintain information in the wikis is a key factor that limits broader adoption. We present VisualWikiCurator, a wiki extension designed to lower such costs by (a) recommending new content to easily update a wiki page, and (b) extracting structured data from the wiki page and providing visualizations of the data. The extracted semantic data and visualization act both as alternative views and as tools to organize the page content. Since no information extraction algorithm is perfect with generic unstructured data, we use a mixed-initiative approach to allow users to refine machine-extracted metadata and easily re-organize the content in wiki pages.