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.

中文翻译：

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使用本体数据库进行可扩展的查询回答、不一致检测和数据集成

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，为了对包含层次结构中实例的传递闭包进行推理，本体数据库可以在查询时展开视图或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——使用触发器——我们证明了通过前向计算推理，我们不仅缩短了查询时间，而且改进似乎只花费了更多的空间（而不是时间）。然而，我们继续表明真正的惩罚对基准来说是不透明的，即基准不能充分捕捉加载时间成本。我们已将我们的方法应用于生物医学的两个案例研究，使用来自遗传学和神经科学的本体和数据来说明两个重要的应用：第一，本体数据库有效地回答基于本体的查询；其次，使用触发器，本体数据库检测基于实例的不一致——这是使用视图无法实现的。最后，我们演示了如何扩展我们的方法以跨多个分布式本体数据库执行数据集成。