Acquiring Commonsense Knowledge for a Cognitive Agent

A critical prerequisite for human-level cognitive systems is having a rich conceptual understanding of the world. We describe a system that learns conceptual knowledge by deep understanding of WordNet glosses. While WordNet is often criticized for having a too fine-grained approach to word senses, the set of glosses do generally capture useful knowledge about the world and encode a substantial knowledge base about everyday concepts. Unlike previous approaches that have built ontologies of atomic concepts from the provided WordNet hierarchies, we construct complex concepts compositionally using description logic and perform reasoning to derive the best classification of knowledge. We view this work as simultaneously accomplishing two goals: building a rich semantic lexicon useful for natural language processing, and building a knowledge base that encodes common-sense knowledge.