Theory Completion Using Inverse Entailment
ILP '00 Proceedings of the 10th International Conference on Inductive Logic Programming
Learning probabilistic logic models from probabilistic examples
Machine Learning
State of the nation in data integration for bioinformatics
Journal of Biomedical Informatics
Does multi-clause learning help in real-world applications?
ILP'11 Proceedings of the 21st international conference on Inductive Logic Programming
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In several recent papers ILP has been applied to Systems Biology problems, in which it has been used to fill gaps in the descriptions of biological networks. In the present paper we describe two new applications of this type in the area of plant biology. These applications are of particular interest to the agrochemical industry in which improvements in plant strains can have benefits for modelling crop development. The background knowledge in these applications is extensive and is derived from public databases in a Prolog format using a new system called Ondex (developers BBSRC Rothamsted). In this paper we explore the question of how much of this background knowledge it is beneficial to include, taking into account accuracy increases versus increases in learning time. The results indicate that relatively shallow background knowledge is needed to achieve maximum accuracy.