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.
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Inoue, K., Furukawa, K., Kobayashi, I., Nabeshima, H. (2010). Discovering Rules by Meta-level Abduction. In: De Raedt, L. (eds) Inductive Logic Programming. ILP 2009. Lecture Notes in Computer Science(), vol 5989. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13840-9_6
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DOI: https://doi.org/10.1007/978-3-642-13840-9_6
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