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Proceedings Paper

Knowledge-base browsing: an application of hybrid distributed/local connectionist networks
Author(s): Tariq Samad; Peggy Israel
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Paper Abstract

We describe a knowledge base browser based on a connectionist (or neural network) architecture that employs both distributed and local representations. The distributed representations are used for input and output thereby enabling associative noise-tolerant interaction with the environment. Internally all representations are fully local. This simplifies weight assignment and facilitates network configuration for specific applications. In our browser concepts and relations in a knowledge base are represented using " microfeatures. " The microfeatures can encode semantic attributes structural features contextual information etc. Desired portions of the knowledge base can then be associatively retrieved based on a structured cue. An ordered list of partial matches is presented to the user for selection. Microfeatures can also be used as " bookmarks" they can be placed dynamically at appropriate points in the knowledge base and subsequently used as retrieval cues. A proof-of-concept system has been implemented for an internally developed Honeywell-proprietary knowledge acquisition tool. 1.

Paper Details

Date Published: 1 August 1990
PDF: 12 pages
Proc. SPIE 1294, Applications of Artificial Neural Networks, (1 August 1990); doi: 10.1117/12.21192
Show Author Affiliations
Tariq Samad, Honeywell, Inc. (United States)
Peggy Israel, Tulane Univ. (United States)


Published in SPIE Proceedings Vol. 1294:
Applications of Artificial Neural Networks
Steven K. Rogers, Editor(s)

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