Using ontology databases for scalable query answering, inconsistency detection, and data integration.

Using ontology databases for scalable query answering, inconsistency detection, and data integration.
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DOI:
10.1007/s10844-010-0133-4
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发表时间:
2011-10
影响因子:
3.4
通讯作者:
Dou, Dejing
Dou, Dejing
中科院分区:
计算机科学3区
文献类型:
--
作者:
LePendu, Paea;Dou, Dejing

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本体数据库是一个基本的关系数据库管理系统,它对本体及其实例进行建模。例如,为了推理包容层次结构中实例的传递闭包,本体数据库可以在查询时展开视图,或者在加载时使用触发器传播断言。在本文中,我们使用现有的基准来评估我们的方法,使用bumerers,我们证明,通过向前计算推理,我们不仅提高了查询时间,但改进似乎只花费更多的空间(而不是时间)。然而,我们继续表明,真正的惩罚只是不透明的基准,即,该基准不能充分捕获加载时间成本。我们已经应用我们的方法在生物医学的两个案例研究中,使用本体和遗传学和神经科学的数据来说明两个重要的应用:第一,本体数据库回答基于本体的查询有效;第二,使用触发器,本体数据库检测基于实例的不一致的东西不可能使用视图。最后,我们演示了如何扩展我们的方法,跨多个分布式本体数据库进行数据集成。
An ontology database is a basic relational database management system that models an ontology plus its instances. To reason over the transitive closure of instances in the subsumption hierarchy, for example, an ontology database can either unfold views at query time or propagate assertions using triggers at load time. In this paper, we use existing benchmarks to evaluate our method—using triggers—and we demonstrate that by forward computing inferences, we not only improve query time, but the improvement appears to cost only more space (not time). However, we go on to show that the true penalties were simply opaque to the benchmark, i.e., the benchmark inadequately captures load-time costs. We have applied our methods to two case studies in biomedicine, using ontologies and data from genetics and neuroscience to illustrate two important applications: first, ontology databases answer ontology-based queries effectively; second, using triggers, ontology databases detect instance-based inconsistencies—something not possible using views. Finally, we demonstrate how to extend our methods to perform data integration across multiple, distributed ontology databases.
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