Knowledge-Driven Learning and Discovery
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.
Cite
Text
Lambert and Fahlman. "Knowledge-Driven Learning and Discovery." AAAI Conference on Artificial Intelligence, 2007.Markdown
[Lambert and Fahlman. "Knowledge-Driven Learning and Discovery." AAAI Conference on Artificial Intelligence, 2007.](https://mlanthology.org/aaai/2007/lambert2007aaai-knowledge/)BibTeX
@inproceedings{lambert2007aaai-knowledge,
title = {{Knowledge-Driven Learning and Discovery}},
author = {Lambert, Benjamin and Fahlman, Scott E.},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2007},
pages = {1880-1881},
url = {https://mlanthology.org/aaai/2007/lambert2007aaai-knowledge/}
}