Knowledge Base Refinement as Improving an Incorrect, Inconsistent and Incomplete Domain Theory
Abstract
We view automation of knowledge base refinement as improvements to a domain theory. Techniques are described that we have developed to handle three types of domain theory pathologies: incorrectness, inconsistency, and incompleteness. The major sources of power of our learning method are a confirmation theory that connects the domain theory to underlying domain theories, the use of an explicit representation of the strategy knowledge for a generic problem class (e.g., heuristic classification) that is separate from the domain theory (e.g., medicine) to be improved, and lastly an explicit, modular, and declarative knowledge representation for the domain theory.
Cite
Text
Wilkins and Tan. "Knowledge Base Refinement as Improving an Incorrect, Inconsistent and Incomplete Domain Theory." International Conference on Machine Learning, 1989. doi:10.1016/B978-1-55860-036-2.50086-2Markdown
[Wilkins and Tan. "Knowledge Base Refinement as Improving an Incorrect, Inconsistent and Incomplete Domain Theory." International Conference on Machine Learning, 1989.](https://mlanthology.org/icml/1989/wilkins1989icml-knowledge/) doi:10.1016/B978-1-55860-036-2.50086-2BibTeX
@inproceedings{wilkins1989icml-knowledge,
title = {{Knowledge Base Refinement as Improving an Incorrect, Inconsistent and Incomplete Domain Theory}},
author = {Wilkins, David C. and Tan, Kok-Wah},
booktitle = {International Conference on Machine Learning},
year = {1989},
pages = {332-339},
doi = {10.1016/B978-1-55860-036-2.50086-2},
url = {https://mlanthology.org/icml/1989/wilkins1989icml-knowledge/}
}