Neural Networks and Structured Knowledge: Rule Extraction and Applications

Franz J. Kurfess, Concordia University

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DOI: http://dx.doi.org/10.1023/A:1008344602888.

NOTE: At the time of publication, the author Franz Kurfess was affiliated with Concordia University - Montreal, Quebec, Canada. Currently, August 2008, he is Professor of Computer Science at California Polytechnic State University - San Luis Obispo .

Abstract

As the second part of a special issue on "Neural Networks and Structured Knowledge," the contributions collected here concentrate on the extraction of knowledge, particularly in the form of rules, from neural networks, and on applications relying on the representation and processing of structured knowledge by neural networks. The transformation of the low-level internal representation in a neural network into higher-level knowledge or information that can be interpreted more easily by humans and integrated with symbol-oriented mechanisms is the subject of the first group of papers. The second group of papers uses specific applications as starting point, and describes approaches based on neural networks for the knowledge representation required to solve crucial tasks in the respective application.