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9

AAAI
2007

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Knowledge-Driven Learning and Discovery

13 years 7 months ago

Knowledge-Driven Learning and Discovery

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The goal of our current research is machine learning with the help and guidance of a knowledge base (KB). Rather than learning numerical models, our approach generates explicit symbolic hypotheses. These hypotheses are subject to the constraints of the KB and are easily human-readable and verifiable. Toward this end, we have implemented algorithms that hypothesize new relations and new types of entities in a KB by examining structural regularities in the KB that represent implicit knowledge. We evaluate these algorithms on a publications KB and a zoology KB.

Benjamin Lambert, Scott E. Fahlman

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AAAI 2007 | Explicit Symbolic Hypotheses | Intelligent Agents | Publications Kb | Zoology Kb |

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Added	02 Oct 2010
Updated	02 Oct 2010
Type	Conference
Year	2007
Where	AAAI
Authors	Benjamin Lambert, Scott E. Fahlman

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