Studying the surface water systems of the earth is important for many fields, from biology to agriculture to tourism. Much of the data relevant to surface water systems are stored in isolated repositories that interface with different ontologies, such as the US Geological Survey’s Surface Water Ontology or the Environment Ontology (ENVO). Effectively using these data requires integrating the ontologies so that the data can be seamlessly queried and analyzed. Automated alignment algorithms exist to facilitate this data integration challenge. In this paper we examine the utility of two leading automated alignment systems to integrate four pairs of ontologies from the surface water domain. We show that the performance of such systems in this domain lags behind their results on popular benchmarks, and therefore incorporate the alignment task described here into the set of benchmarks used by the alignment community. We also show that with minor modifications, existing alignment algorithms can be used effectively within a semi-automated system for the surface water domain. In addition, we analyze the unique challenges of this domain with respect to data integration and discuss possible solutions to pursue in order to address these challenges.

中文翻译：

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对齐地表水本体：手动和自动方法的比较

从生物学到农业再到旅游业，研究地球的地表水系统对于许多领域都很重要。与地表水系统相关的许多数据都存储在与不同本体（例如美国地质调查局的地表水本体或环境本体（ENVO））交互的独立存储库中。有效使用这些数据需要集成本体，以便可以无缝查询和分析数据。存在自动对齐算法来促进这种数据集成挑战。在本文中，我们研究了两个领先的自动对准系统对整合地表水域中的四对本体的实用性。我们表明，此类系统在此领域的性能落后于其在流行基准测试中的结果，并因此将此处描述的对齐任务合并到对齐社区使用的一组基准中。我们还表明，只需稍作修改，就可以在地表水域的半自动系统内有效地使用现有的比对算法。此外，我们分析了该领域在数据集成方面的独特挑战，并讨论了为解决这些挑战而可能采取的解决方案。