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A Framework for Set-Oriented Computation in Inductive Logic Programming and Its Application in Generalizing Inverse Entailment

  • Héctor Corrada Bravo
  • David Page
  • Raghu Ramakrishnan
  • Jude Shavlik
  • Vitor Santos Costa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3625)

Abstract

We propose a new approach to Inductive Logic Programming that systematically exploits caching and offers a number of advantages over current systems. It avoids redundant computation, is more amenable to the use of set-oriented generation and evaluation of hypotheses, and allows relational DBMS technology to be more easily applied to ILP systems. Further, our approach opens up new avenues such as probabilistically scoring rules during search and the generation of probabilistic rules. As a first example of the benefits of our ILP framework, we propose a scheme for defining the hypothesis search space through Inverse Entailment using multiple example seeds.

Keywords

Logic Program Single Seed Inductive Logic Programming Horn Clause Hypothesis Space 
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 Berlin Heidelberg 2005

Authors and Affiliations

  • Héctor Corrada Bravo
    • 1
  • David Page
    • 1
    • 2
  • Raghu Ramakrishnan
    • 1
  • Jude Shavlik
    • 1
    • 2
  • Vitor Santos Costa
    • 2
    • 3
  1. 1.Department of Computer Sciences 
  2. 2.Department of Biostatistics and Medical InformaticsUniversity of Wisconsin-MadisonUSA
  3. 3.COPPE/Sistemas UFRJBrasil

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