An ILP refinement operator for biological grammar learning

Fredouille, DC; Bryant, CH; Jayawickreme, CK; Jupe, S; Topp, S

Abstract

We are interested in using Inductive Logic Programming (ILP) to infer grammars representing sets of biological sequences. We call these biological grammars. ILP systems are well suited to this task in the sense that biological grammars have been represented as logic programs using the Definite Clause Grammar or the String Variable Grammar formalisms. However, the speed at which ILP systems can generate biological grammars has been shown to be a bottleneck. This paper presents a novel refinement operator implementation, specialised to infer biological grammars with ILP techniques. This implementation is shown to significantly speed-up inference times compared to the use of the classical refinement operator: time gains larger than 5-fold were observed in 4/5 of the experiments, and the maximum observed gain is over 300-fold.

• Start Date: Aug 24, 2006
• End Date: Aug 26, 2006
• Publication Date: Jan 1, 2007
• Deposit Date: Feb 16, 2009
• Publicly Available Date: Feb 16, 2009
• Publisher: Springer
• Pages: 214-228
• Series Title: Lecture notes in artificial intelligence (subseries of Lecture notes in computer science)
• Series Number: 4455
• Book Title: Inductive logic programming
• ISBN: 9783540738466
• DOI: https://doi.org/10.1007/978-3-540-73847-3_24
• Publisher URL: http://dx.doi.org/10.1007/978-3-540-73847-3_24
• Additional Information: Paper originally presented at the 16th International Conference, ILP 2006, Santiago de Compostela, Spain, August 24-27 2006