Learning the Right Expansion-ordering Heuristics for Satisfiability
  Testing in OWL Reasoners
  Web Ontology Language (OWL) reasoners are used to infer new logical relations
from ontologies. While inferring new facts, these reasoners can be further
optimized, e.g., by properly ordering disjuncts in disjunction expressions of
ontologies for satisfiability testing of concepts. Different expansion-ordering
heuristics have been developed for this purpose. The built-in heuristics in
these reasoners determine the order for branches in search trees while each
heuristic choice causes different effects for various ontologies depending on
the ontologies' syntactic structure and probably other features as well. A
learning-based approach that takes into account the features aims to select an
appropriate expansion-ordering heuristic for each ontology. The proper choice
is expected to accelerate the reasoning process for the reasoners. In this
paper, the effect of our methodology is investigated on a well-known reasoner
that is JFact. Our experiments show the average speedup by a factor of one to
two orders of magnitude for satisfiability testing after applying learning
methodology for selecting the right expansion-ordering heuristics.
