Abstract
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.
Abstract (translated)
Web本体语言（OWL）推理器用于从本体推断新的逻辑关系。在推断新事实的同时，可以进一步优化这些推理器，例如，通过对本体的分离表达式中的分离进行适当的排序，以便对概念进行满足性测试。为此，开发了不同的扩展排序启发式算法。这些推理机中内置的启发式方法决定了搜索树中分支的顺序，而每个启发式选择对不同的本体会产生不同的影响，这取决于本体的句法结构和可能的其他特性。一种基于学习的方法，考虑到这些特性，目的是为每个本体选择一个适当的扩展排序启发式。正确的选择将加速推理者的推理过程。本文研究了我们的方法论对一个著名的推理者JFACT的影响。我们的实验表明，在应用学习方法选择正确的展开排序启发式后，满足性测试的平均加速率提高了1到2个数量级。
URL
https://arxiv.org/abs/1904.09443
