Designing interfaces for guided collection of knowledge about everyday objects from volunteers

A new generation of intelligent applications can be enabled by broad-coverage knowledge repositories about everyday objects. We distill lessons in design of intelligent user interfaces which collect such broad-coverage knowledge from untrained volunteers. We motivate the knowledge-driven template-based approach adopted in Learner2, a second generation proactive acquisition interface for eliciting such knowledge. We present volume, accuracy, and recall of knowledge collected by fielding the system for 5 months. Learner2 has so far acquired 99,018 general statements, emphasizing knowledge about parts of and typical uses of objects.