Toward an Architecture for Never-Ending Language Learning
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.
Cite
Text
Carlson et al. "Toward an Architecture for Never-Ending Language Learning." AAAI Conference on Artificial Intelligence, 2010. doi:10.1609/AAAI.V24I1.7519Markdown
[Carlson et al. "Toward an Architecture for Never-Ending Language Learning." AAAI Conference on Artificial Intelligence, 2010.](https://mlanthology.org/aaai/2010/carlson2010aaai-architecture/) doi:10.1609/AAAI.V24I1.7519BibTeX
@inproceedings{carlson2010aaai-architecture,
title = {{Toward an Architecture for Never-Ending Language Learning}},
author = {Carlson, Andrew and Betteridge, Justin and Kisiel, Bryan and Settles, Burr and Jr., Estevam R. Hruschka and Mitchell, Tom M.},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2010},
pages = {1306-1313},
doi = {10.1609/AAAI.V24I1.7519},
url = {https://mlanthology.org/aaai/2010/carlson2010aaai-architecture/}
}