Refining Numerical Constants in First Order Logic Theories

摘要

This paper proposes a method for refining numerical constants occurring in rules of a knowledge base expressed in a first order logic language. The method consists in tuning numerical parameters by performing error gradient descent. The knowledge base to be refined can be manually handcrafted or automatically acquired by a symbolic relational learner, able to deal with numerical features. The results of an experimental analysis performed on four case studies show that the refinement step can be effective in improving classification performances.