A case study of knowledge representation in UC

Authors:
David N. Chin
Affiliations:
Division of Computer Science, Department of EECS, University of California, Berkeley, Berkeley, CA
Venue:
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 1
Year:
1983

Citing 2
Cited 1

Knowledge structures in UC, the UNIX Consultant

ACL '83 Proceedings of the 21st annual meeting on Association for Computational Linguistics
PHRAN: a knowledge-based natural language understander

ACL '80 Proceedings of the 18th annual meeting on Association for Computational Linguistics

Combination of Bayesian Network and Overlay Model in User Modeling

ICCS 2009 Proceedings of the 9th International Conference on Computational Science

Quantified Score

Hi-index	0.00

Visualization

Abstract

The knowledge representation used in UC provides a flexible framework suitable for a large variety of natural language processing tasks including parsing, inference, planning, goal analysis, and generation. Although many of the knowledge structures are specific to the UNIX Consultant domain, a common design goal is the use of associative processing. By providing direct links between related knowledge structures, inference and other processing can be done very efficiently. Access to representations in UC is by hash indexing which simulates a real associative memory.