用于普及服务发现的经济高效、自适应推理策略

Luke Steller, S. Krishnaswamy, M. Gaber
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引用次数: 5

摘要

随着高端智能手机/ pda的出现,通过语义推理丰富移动/普及服务的机会正在出现。本文提出了优化语义推理的新策略,以实现移动设备上的语义应用和服务。我们的mTableaux算法优化了推理过程,以方便服务选择。由于即使是优化的推理也可能过于资源密集而无法完成,这取决于本体的大小和资源的可用性,我们还概述了我们的自适应推理策略,该策略在资源变得低时降低了结果的准确性。我们还评估了我们的策略对性能和准确性的影响,并表明mTableaux显着提高了性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cost efficient, adaptive reasoning strategies for pervasive service discovery
With the emergence of high-end smart phones/PDAs there is an emerging opportunity to enrich mobile/pervasive services with semantic reasoning. This paper presents novel strategies for optimising semantic reasoning for realising semantic applications and services on mobile devices. Our mTableaux algorithm optimises the reasoning process to facilitate service selection. Since even optimised reasoning may be too resource intensive to complete, depending on ontology size and resource availability, we also outline our adaptive reasoning strategy which reduces result accuracy when resources become low. We also evaluate the impact of our strategies on performance and accuracy and show that mTableaux significantly improves performance.
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