To address the questions above, the Open IE project has been developing a Web-scale information extraction system that reads arbitrary text from any domain on the Web, extracts meaningful information and stores in a unified knowledge base for efficient querying. In contrast to traditional information extraction, the Open Information Extraction paradigm attempts to overcome the knowledge acquisition bottleneck by extracting a large number of relations at once.

Demo: TextRunner extracted over 500,000,000 assertions from 100 million Web pages.
Software: ReVerb Open Information Extraction Software and additional information.
Data: Horn-clause inference rules learned by the Sherlock system.
Demo: Selectional Preferences from Web Text compute admissible argument values for a relation.
Data: 10,000 Functional Relations learned from Web Text predict the functionality of a phrase.