RakSOR: Ranking of Ontology Reasoners Based on Predicted Performances

N. Alaya, S. Yahia, M. Lamolle
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引用次数: 5

Abstract

Over the last decade, several ontology reasoners have been proposed to overcome the computational complexity of inference tasks on expressive ontology languages. Nevertheless, it is well-accepted that there is no outstanding reasoner that can outperform in all input ontologies. Thus, an algorithm selection problem have emerged in this field of study. In this paper, we describe first steps to develop a new system to provide user support when looking for guidance over ontology reasoners. Our main goal is to be able to automatically rank a set of candidate reasoners for any given ontology. Robustness standing for the ability of reasoner to correctly achieve a reasoning task within a fixed time limit is our primary ranking criterion. Our ranking method follows a meta-learning approach and applies bucket order rules. An extensive experiments covering over 2500 well selected real-world ontologies and six state-of-the-art of the most performing reasoners was carried out to provide enough data for the study. Our prediction and ranking results are encouraging, witnessing the potential benefits of the proposed approach.
RakSOR:基于预测性能的本体推理器排序
在过去的十年中,已经提出了一些本体推理器来克服在表达本体语言上推理任务的计算复杂性。然而,人们普遍认为,没有一个杰出的推理器可以在所有输入本体中都表现出色。因此,在这一研究领域中出现了一个算法选择问题。在本文中,我们描述了开发一个新系统的第一步,该系统在寻找本体推理器的指导时为用户提供支持。我们的主要目标是能够对任意给定本体的一组候选推理器进行自动排序。鲁棒性是指推理器在固定时间内正确完成推理任务的能力,这是我们的主要排名标准。我们的排名方法遵循元学习方法,并应用桶顺序规则。为了为研究提供足够的数据,我们进行了广泛的实验,涵盖了2500多个精心挑选的现实世界本体和六个最先进的表现最好的推理器。我们的预测和排名结果令人鼓舞,见证了所提出的方法的潜在好处。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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