Knowledge-driven learning and discovery

Authors:
Benjamin Lambert;Scott E. Fahlman
Affiliations:
Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PA;Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PA
Venue:
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Year:
2007

Citing 3
Cited 0

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Abstract

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.