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.
[1]
R. Lippmann,et al.
An introduction to computing with neural nets
,
1987,
IEEE ASSP Magazine.
[2]
Geoffrey E. Hinton,et al.
Learning internal representations by error propagation
,
1986
.
[3]
Geoffrey E. Hinton,et al.
A Learning Algorithm for Boltzmann Machines
,
1985,
Cogn. Sci..
[4]
Jerome A. Feldman,et al.
Neural Representation of Conceptual Knowledge.
,
1986
.
[5]
T. Samad.
Towards connectionist rule-based systems
,
1988,
IEEE 1988 International Conference on Neural Networks.
[6]
WILLIAM P. JONES,et al.
On the Applied Use of Human Memory Models: The Memory Extender Personal Filing System
,
1986,
Int. J. Man Mach. Stud..