Learning Intermediate Concepts in Constructing a Hierarchical Knowledge Base
Proceedings of the Ninth International Joint Conference on Artificial Intelligence. Learning and Acquisition. Pages 659-665.
Abstract
In expert systems. hierarchical reasoning can provide better accuracy a nd understandability. Here, we develop a method of learning hierarchical knowledge from a case library, in which each tr ain ing instance is described by low level features and high level concepts (e.g., manifestations and diseases) but not by intennediate concep ts (e.g., disease states). · Learning intennediate knowledge involves exploiting the o ld partial intennediate knowledge or creating new intennediate concepts by observing the relationship between the low level features and high level concepts. Experiments in the domain of diagnosing causes of jaundice validate the method.
@inproceedings{ijcai1985p128,
title = {Learning Intermediate Concepts in Constructing a Hierarchical Knowledge Base},
author = {Fu, LiMin and Buchanan, Bruce G.},
booktitle = {Proceedings of the Ninth International Joint Conference on
Artificial Intelligence, {IJCAI-85}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Aravind Joshi},
pages = {659--665},
year = {1985},
note = {Learning and Acquisition},
url = {https://ijcai.org/proceedings/1985/128},
}
TY - CPAPER TI - Learning Intermediate Concepts in Constructing a Hierarchical Knowledge Base AU - Fu, LiMin AU - Buchanan, Bruce G. T2 - Proceedings of the Ninth International Joint Conference on Artificial Intelligence, IJCAI-85 PB - International Joint Conferences on Artificial Intelligence PY - 1985 SP - 659 EP - 665 UR - https://ijcai.org/proceedings/1985/128 ER -Copied
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