Learning intermediate concepts in constructing a hierarchical knowledge base

Abstract

In expert systems, hierarchical reasoning can provide better accuracy and understandability. Here, we develop a method of learning hierarchical knowledge from a case library, in which each training instance is described by low level features and high level concepts (e.g., manifestations and diseases) but not by intermediate concepts (e.g., disease states). Learning intermediate knowledge involves exploiting the old partial intermediate knowledge or creating new intermediate concepts by observing the relationship between the low level features and high level concepts. Experiments in the domain of diagnosing causes of jaundice validate the method.