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Bryan Kisiel

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AAAI Conference 2015 Conference Paper

Never-Ending Learning

  • Tom Mitchell
  • William Cohen
  • Estevam Hruschka
  • Partha Talukdar
  • Justin Betteridge
  • Andrew Carlson
  • Bhavana Dalvi Mishra
  • Matthew Gardner

Whereas people learn many different types of knowledge from diverse experiences over many years, most current machine learning systems acquire just a single function or data model from just a single data set. We propose a never-ending learning paradigm for machine learning, to better reflect the more ambitious and encompassing type of learning performed by humans. As a case study, we describe the Never- Ending Language Learner (NELL), which achieves some of the desired properties of a never-ending learner, and we discuss lessons learned. NELL has been learning to read the web 24 hours/day since January 2010, and so far has acquired a knowledge base with over 80 million confidence-weighted beliefs (e. g. , servedWith(tea, biscuits)), while learning continually to improve its reading competence over time. NELL has also learned to reason over its knowledge base to infer new beliefs from old ones, and is now beginning to extend its ontology by synthesizing new relational predicates. NELL can be tracked online at http: //rtw. ml. cmu. edu, and followed on Twitter at @CMUNELL.

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AAAI Conference 2010 Conference Paper

Toward an Architecture for Never-Ending Language Learning

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

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.

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v2026.09.13