A Browser for Large Knowledge Bases Based on a Hybrid Distributed/Local Connectionist Architecture

A browser concept based on a connectionist architecture is presented. The concept utilizes both distributed and local representations. A proof-of-concept system is implemented for an integrally developed, Honeywell-proprietary knowledge acquisition tool. In the 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. The browser concept can be applied wherever there is a need for conveniently inspecting and manipulating structured information. >

[1]  Stephen Grossberg Review of Perceptrons , 1989, AI Mag..

[2]  T. Samad Hybrid distributed/local connectionist architectures , 1989, International 1989 Joint Conference on Neural Networks.

[3]  J. Fodor,et al.  Connectionism and cognitive architecture: A critical analysis , 1988, Cognition.

[4]  Geoffrey E. Hinton,et al.  Distributed Representations , 1986, The Philosophy of Artificial Intelligence.

[5]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[6]  Y. L. Cun Learning Process in an Asymmetric Threshold Network , 1986 .

[7]  Michael D. Gordon Probabilistic and genetic algorithms in document retrieval , 1988, CACM.

[8]  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..

[9]  S. Pinker,et al.  On language and connectionism: Analysis of a parallel distributed processing model of language acquisition , 1988, Cognition.

[10]  T. Samad Towards connectionist rule-based systems , 1988, IEEE 1988 International Conference on Neural Networks.

[11]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[12]  Terrence J. Sejnowski,et al.  A Learning Algorithm for Boltzmann Machines , 1985, Cognitive Sciences.

[13]  Richard P. Lippmann,et al.  An introduction to computing with neural nets , 1987 .

[14]  P. Werbos,et al.  Beyond Regression : "New Tools for Prediction and Analysis in the Behavioral Sciences , 1974 .

[15]  Geoffrey E. Hinton,et al.  A Distributed Connectionist Production System , 1988, Cogn. Sci..

[16]  Gerard Salton,et al.  Parallel text search methods , 1988, CACM.

[17]  Stephen I. Gallant,et al.  Connectionist expert systems , 1988, CACM.

[18]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[19]  Michael McGill,et al.  Introduction to Modern Information Retrieval , 1983 .

[20]  Craig Stanfill,et al.  Parallel free-text search on the connection machine system , 1986, CACM.

[21]  David L. Waltz,et al.  Toward memory-based reasoning , 1986, CACM.

[22]  W. Daniel Hillis,et al.  The connection machine , 1985 .

[23]  Harold S. Stone,et al.  Parallel Querying of Large Databases: A Case Study , 1987, Computer.

[24]  James L. McClelland,et al.  James L. McClelland, David Rumelhart and the PDP Research Group, Parallel distributed processing: explorations in the microstructure of cognition . Vol. 1. Foundations . Vol. 2. Psychological and biological models . Cambridge MA: M.I.T. Press, 1987. , 1989, Journal of Child Language.

[25]  Gerard Salton,et al.  Another look at automatic text-retrieval systems , 1986, CACM.