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Ripple down rules: Turning knowledge acquisition into knowledge maintenance

Journal Article journal-article Artificial Intelligence · Artificial Intelligence in Medicine

Abstract

The most successful applications of medical expert systems seem to be in the interpretation of laboratory data. However, even in this domain, knowledge acquisition and maintenance are major problems. We have developed a knowledge acquisition technique (‘ripple down rules’) based on using knowledge only in the context in which it is acquired. The method also guides the expert to enter rules that are valid. This method trivialises knowledge acquisition so that building a pathology expert system becomes the minor daily task for the expert of correcting wrong interpretations and tuning the knowledge base to current expertise. A major expert system based on this technique, PEIRS (Pathology Expert Interpretative Reporting System), is now in use. The current limitations of the technique are that the underlying tree structure of the knowledge base may require the expert to re-enter some knowledge and that multiple diseases are handled as composite diseases.

Authors

Keywords

  • Knowledge acquisition
  • knowledge engineering
  • context
  • medical expert systems
  • ripple down rules

Context

Venue
Artificial Intelligence in Medicine
Archive span
1989-2026
Indexed papers
2812
Paper id
714803913871894093
v2026.09.13