Conceptual graph knowledge systems as problem context for neural networks

For a connectionist network to be able to learn to generalize well, there must be some correspondence between the structure/constraints of the net's architecture and those of the given problem space. Therefore, recourse to experiments with real-world problems will always be required in connectionist research. The author gives an outline of a problem area for which connectionist nets hold great promise: knowledge systems, where the knowledge is encoded/represented using conceptual graphs. Certain aspects of this problem context are already known, and these are probed for possible implementation by connection is nets. The approach used is to present some basic properties of conceptual graphs, indicate operations important in their application, and point out those that might be candidates for implementation with neural nets. A special representation schema for conceptual graphs is used for their implementation by neural nets.<
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