Toward an Architecture for Never-Ending Language Learning

We consider here the problem of building a never-ending language learner; that is, an intelligent computer agent that runs forever and that each day must (1) extract, or read, information from the web to populate a growing structured knowledge base, and (2) learn to perform this task better than on the previous day. In particular, we propose an approach and a set of design principles for such an agent, describe a partial implementation of such a system that has already learned to extract a knowledge base containing over 242, 000 beliefs with an estimated precision of 74% after running for 67 days, and discuss lessons learned from this preliminary attempt to build a never-ending learning agent.

Authors

• Andrew Carlson
• Justin Betteridge
• Bryan Kisiel
• Burr Settles
• Estevam Hruschka
• Tom Mitchell

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