Applying Machine Learning to Enhance Optimization Techniques for OWL Reasoning

Various (tableau) optimization techniques have been integrated into OWL reasoners to speed up reasoning. Many of the techniques rely on heuristics that have been manually fine tuned for achieving a good performance but might fail dramatically when encountering ontologies exhibiting unexpected design patterns. A typical example are heuristics applied to disjunctions in order to select a disjunct to be added to the tableau. Evidences indicate that the order of selecting disjuncts can have a significant impact on reasoning speed. Our approach presented in this paper applies machine learning to make the selection process more effective and removes the need for manual fine tuning. We extended the OWL reasoner JFact accordingly to control the disjunct selection process. We demonstrate that one can successfully learn to choose a disjunct based on the most effective heuristic method. As a first step we focused on propositional SAT testing. Our results show that machine learning can speed up JFact by one to two orders of magnitude.