Applied Intelligence

, Volume 12, Issue 1, pp 7–13

Neural Networks and Structured Knowledge: Rule Extraction and Applications

Authors

  • Franz J. Kurfess
    • Department of Computer ScienceConcordia University
Article

DOI: 10.1023/A:1008344602888

Cite this article as:
Kurfess, F.J. Applied Intelligence (2000) 12: 7. doi:10.1023/A:1008344602888

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.

The companion first part of the special issue [1] contains papers dealing with representation and reasoning issues on the basis of neural networks.

neural networksrule extractionknowledge representationstructured knowledgeconnectionismhybrid systems

Copyright information

© Kluwer Academic Publishers 2000