Knowledge base browsing. An application of hybrid distributed/local connectionist networks

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.