Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI-10) Toward an Architecture for Never-Ending Language Learning
BibTeX
@MISC{Carlson_proceedingsof,
author = {Andrew Carlson and Justin Betteridge and Bryan Kisiel and Burr Settles and Estevam R. Hruschka and Tom M. Mitchell},
title = {Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI-10) Toward an Architecture for Never-Ending Language Learning},
year = {}
}
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Abstract
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.
