Optimizing Heuristics for Tableau-based OWL Reasoners
  Optimization techniques play a significant role in improving description
logic reasoners covering the Web Ontology Language (OWL). These techniques are
essential to speed up these reasoners. Many of the optimization techniques are
based on heuristic choices. Optimal heuristic selection makes these techniques
more effective. The FaCT++ OWL reasoner and its Java version JFact implement an
optimization technique called ToDo list which is a substitute for a traditional
top-down approach in tableau-based reasoners. The ToDo list mechanism allows
one to arrange the order of applying different rules by giving each a priority.
Compared to a top-down approach, the ToDo list technique has a better control
over the application of expansion rules. Learning the proper heuristic order
for applying rules in ToDo lis} will have a great impact on reasoning speed. We
use a binary SVM technique to build our learning model. The model can help to
choose ontology-specific order sets to speed up OWL reasoning. On average, our
learning approach tested with 40 selected ontologies achieves a speedup of two
orders of magnitude when compared to the worst rule ordering choice.
