Learning the Right Expansion-ordering Heuristics for Satisfiability Testing in OWL Reasoners
This work addresses performance bottlenecks in ontology reasoning, which is incremental as it applies a learning-based approach to an existing optimization problem.
The paper tackles the problem of optimizing satisfiability testing in OWL reasoners by learning to select the best expansion-ordering heuristic for each ontology, resulting in an average speedup of one to two orders of magnitude.
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.