Learning probabilistic logic models from probabilistic examples

Abstract. We revisit an application developed originally using Inductive Logic Programming (ILP) by replacing the underlying Logic Program (LP) description with Stochastic Logic Programs (SLPs), one of the underlying Probabilistic ILP (PILP) frameworks. In both the ILP and PILP cases a mixture of abduction and induction are used. The abductive ILP approach used a variant of ILP for modelling 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). The ILP approach learned logic models from non-probabilistic examples. The PILP approach applied in this paper is based on a general approach to introducing probability labels within a standard scientific experimental setting involving control and treatment data. Our results demonstrate that the PILP appr...

• Type: Journal
• Year: 2008
• Where: ML
• Authors: Jianzhong Chen, Stephen Muggleton, José Carlos Almeida Santos
• Keywords: ILP Approach, Logic, Logic Programs, Machine Learning, ML 2008