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Discovering Rules by Meta-level Abduction

  • Katsumi Inoue
  • Koichi Furukawa
  • Ikuo Kobayashi
  • Hidetomo Nabeshima
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5989)

Abstract

This paper addresses discovery of unknown relations from incomplete network data by abduction. Given a network information such as causal relations and metabolic pathways, we want to infer missing links and nodes in the network to account for observations. To this end, we introduce a framework of meta-level abduction, which performs abduction in the meta level. This is implemented in SOLAR, an automated deduction system for consequence finding, using a first-order representation for algebraic properties of causality and the full-clausal form of network information and constraints. Meta-level abduction by SOLAR is powerful enough to infer missing rules, missing facts, and unknown causes that involve predicate invention in the form of existentially quantified hypotheses. We also show an application of rule abduction to discover certain physical techniques and related integrity constraints within the subject area of Skill Science.

Keywords

Integrity Constraint Background Theory Horn Clause Causal Graph Clausal Theory 
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 2010

Authors and Affiliations

  • Katsumi Inoue
    • 1
  • Koichi Furukawa
    • 2
  • Ikuo Kobayashi
    • 2
  • Hidetomo Nabeshima
    • 3
  1. 1.National Institute of InformaticsTokyoJapan
  2. 2.SFC Research InstituteKeio UniversityFujisawaJapan
  3. 3.Division of Medicine and Engineering ScienceUniversity of YamanashiYamanashiJapan

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