Discovering Regularities from Large Knowledge Bases
Abstract
Large knowledge bases open new horizons for machine learning research. One challenge is to design learning programs to expand the knowledge base using the knowledge that is currently available. This paper addresses the problem of discovering regularities from such knowledge bases. We begin with a definition of regularities and its motivations. We then give a brief description of a framework that integrates induction with knowledge. The performance of this approach will be illustrated by its applications to an existing large knowledge base.
Cite
Text
Shen. "Discovering Regularities from Large Knowledge Bases." International Conference on Machine Learning, 1991. doi:10.1016/B978-1-55860-200-7.50110-0Markdown
[Shen. "Discovering Regularities from Large Knowledge Bases." International Conference on Machine Learning, 1991.](https://mlanthology.org/icml/1991/shen1991icml-discovering/) doi:10.1016/B978-1-55860-200-7.50110-0BibTeX
@inproceedings{shen1991icml-discovering,
title = {{Discovering Regularities from Large Knowledge Bases}},
author = {Shen, Wei-Min},
booktitle = {International Conference on Machine Learning},
year = {1991},
pages = {539-543},
doi = {10.1016/B978-1-55860-200-7.50110-0},
url = {https://mlanthology.org/icml/1991/shen1991icml-discovering/}
}