Learning probabilistic logic models from probabilistic examples

We revisit an application developed originally using abductive Inductive Logic Programming (ILP) for modeling inhibition in metabolic networks. The example data was derived from studies of the effects of toxins on rats using Nuclear Magnetic Resonance (NMR) time-trace analysis of their biofluids tog...

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Bibliographic Details
Published in:Machine learning Vol. 73; no. 1; pp. 55 - 85
Main Authors: Chen, Jianzhong, Muggleton, Stephen, Santos, José
Format: Journal Article
Language:English
Published: Boston Springer US 01.10.2008
Springer Nature B.V
Subjects:
Artificial Intelligence
Computer Science
Control
Logic programming
Mechatronics
Natural Language Processing (NLP)
Probability
Robotics
Simulation and Modeling
Probabilistic inductive logic programming
Stochastic logic programs
Abduction
Probabilistic examples
ISSN:0885-6125, 1573-0565
Online Access:Get full text
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Summary:We revisit an application developed originally using abductive Inductive Logic Programming (ILP) for modeling inhibition in metabolic networks. The example data was derived from studies of the effects of toxins on rats using Nuclear Magnetic Resonance (NMR) time-trace analysis of their biofluids together with background knowledge representing a subset of the Kyoto Encyclopedia of Genes and Genomes (KEGG). We now apply two Probabilistic ILP (PILP) approaches—abductive Stochastic Logic Programs (SLPs) and PRogramming In Statistical modeling (PRISM) to the application. Both approaches support abductive learning and probability predictions. Abductive SLPs are a PILP framework that provides possible worlds semantics to SLPs through abduction. Instead of learning logic models from non-probabilistic examples as done in ILP, the PILP approach applied in this paper is based on a general technique for introducing probability labels within a standard scientific experimental setting involving control and treated data. Our results demonstrate that the PILP approach provides a way of learning probabilistic logic models from probabilistic examples, and the PILP models learned from probabilistic examples lead to a significant decrease in error accompanied by improved insight from the learned results compared with the PILP models learned from non-probabilistic examples.
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cjz@doc.ic.ac.uk, shm@doc.ic.ac.uk, jcs06@doc.ic.ac.uk
ISSN:0885-6125
1573-0565
DOI:10.1007/s10994-008-5076-4