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Knowledge extraction: a comparison between symbolic and connectionist methods.

C Nobre1, E Martineli, A Braga

  • 1Computer Science Department, PUC-MG, Brazil. nobre@betim.pucminas.br

International Journal of Neural Systems
|November 24, 1999
PubMed
Summary
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This study explores three artificial intelligence knowledge extraction techniques: C4.5, CN2, and TREPAN. These methods aim to represent learned knowledge linguistically for better user understanding and system adoption.

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Knowledge Representation

Background:

  • Effective knowledge representation is crucial for user understanding and adoption of AI systems.
  • Both symbolic and connectionist AI approaches offer techniques for knowledge extraction.
  • Existing methods vary in their ability to translate learned information into accessible formats.

Purpose of the Study:

  • To investigate and compare three distinct knowledge extraction techniques.
  • To evaluate methods based on symbolic learning and neural network knowledge extraction.
  • To assess the suitability of different techniques for generating linguistic knowledge representations.

Main Methods:

  • The study examines the C4.5 algorithm for decision tree and rule induction.

Related Experiment Videos

  • It analyzes the CN2 algorithm for inducing if...then rules from datasets.
  • The TREPAN algorithm is investigated for extracting decision trees from trained neural networks.

    • Main Results:

    • C4.5 and CN2 extract knowledge directly from datasets, producing rules or decision trees.
    • TREPAN extracts knowledge in the form of decision trees from pre-trained neural networks.
    • All three methods utilize decision trees or rules as their knowledge representation.

    Conclusions:

    • The C4.5, CN2, and TREPAN algorithms offer diverse approaches to knowledge extraction in AI.
    • Understanding these techniques is key to developing AI systems that are transparent and user-friendly.
    • The choice of technique impacts how learned knowledge is represented and communicated.