Discovering Rules by Meta-level Abduction

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 me...

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Bibliographic Details
Published inInductive Logic Programming Vol. 5989; pp. 49 - 64
Main Authors Inoue, Katsumi, Furukawa, Koichi, Kobayashi, Ikuo, Nabeshima, Hidetomo
Format Book Chapter
LanguageEnglish
Published Germany Springer Berlin / Heidelberg 2010
Springer Berlin Heidelberg
SeriesLecture Notes in Computer Science
Subjects
Background Theory
Causal Graph
Clausal Theory
Horn Clause
Integrity Constraint
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Summary: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.
ISBN:9783642138393
364213839X
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-642-13840-9_6