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Management of Large Spatial Ontology Bases

机译:大型空间本体基础管理

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In this paper we propose a method for efficient management of large spatial ontologies. Current spatial ontologies are usually represented using an ontology language, such as OWL and stored as OWL files. However, we have observed some shortcomings using this approach especially in the efficiency of spatial query processing. This fact motivated the development of a hybrid approach that uses an R-tree as a spatial index structure. In this way we are able to support efficient query processing over large spatial ontologies, maintaining the benefits of ontological reasoning. We present a case study for emergency teams during Search and Rescue (SaR) operations showing how an Ontology Data Service (SHARE-ODS) can benefit from a spatial index. Performance evaluation shows the superiority of our proposed technique compared to the original approach. To the best of our knowledge, this is the first attempt to address the problem of efficient management of large spatial ontology bases.
机译:在本文中,我们提出了一种有效管理大型空间本体的方法。当前的空间本体通常使用本体语言表示,例如猫头鹰并作为猫头鹰文件存储。但是,我们已经观察到一些使用这种方法的缺点,特别是在空间查询处理的效率中。这一事实激励了一种混合方法的开发,它使用R树作为空间指数结构。通过这种方式,我们能够在大型空间本体上支持高效的查询处理,维持本体理论推理的好处。我们在搜索和救援期间为紧急团队提供了一个案例研究,显示了本体数据服务(共享ODS)如何从空间索引中受益。与原始方法相比,绩效评估显示了我们所提出的技术的优越性。据我们所知,这是第一次解决大型空间本体基础的有效管理问题的尝试。

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