A General Framework for Supporting Relational Concept Learning

  • L. Saitta
Conference paper
Part of the International Centre for Mechanical Sciences book series (CISM, volume 382)


This paper describes a general representation framework that offers a unifying platform for a number of systems learning concepts in First Order Logic. The main aspects of this framework are discussed, specifically, the separation between the hypothesis logical language and the representation of data by means of a relational database, and the introduction of a functional layer between data and hypotheses, which makes the data accessible by the logical level through a set of abstract properties.

A novelty, in the hypothesis representation language, is the introduction of the construct of internal disjunction; such a construct, first used by the AQ and Induce systems, is here made operational via a set of algorithms, capable to learn it, for both the discrete and the continuous-valued attributes case. These algorithms are embedded in learning systems using different paradigms, such as symbolic, genetic or connectionist ones.


First Order Logic Relational Algebra Inductive Logic Programming Horn Clause Learning Instance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Wien 1997

Authors and Affiliations

  • L. Saitta
    • 1
  1. 1.University of TurinTurinItaly

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