Infoboxer: Using Statistical and Semantic Knowledge to Help Create Wikipedia Infoboxes

Wikipedia infoboxes serve as input in the creation of knowledge bases such as DBpedia, Yago, and Freebase. Current creation of Wikipedia infoboxes is manual and based on templates that are created and maintained collaboratively. However, these templates pose several challenges:

• Different communities use different infobox templates for the same category articles
• Attribute names differ (e.g., date of birth vs. birthdate)
• Templates are restricted to a single category, making it harder to find a template for an article that belongs to multiple categories (e.g., actor and politician)
• Templates are free form in nature and no integrity check is performed on whether the value filled by the user is of appropriate type for the given attribute

In this talk, we present Infoboxer, a tool which overcomes these challenges using statistical and semantic knowledge from linked data sources to ease the process of creating Wikipedia infoboxes. Infoboxer creates dynamic and semantic templates by suggesting attributes common for similar articles and controlling the expected values semantically. We will give an overview of our approach and demonstrate how Infoboxer can be used to create infoboxes for new Wikipedia articles as well as update erroneous values in existing infoboxes. We will also discuss our proposed extensions to the project.

Visit http://ebiq.org/p/668 for more information about Infoboxer. A demo can be found here.