An ontology for describing and synthesizing ecological observation data
An ontology for describing and synthesizing ecological observation data
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DOI：
10.1016/j.ecoinf.2007.05.004
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发表时间：
2007-10-01
影响因子：
5.1
通讯作者：
Villa, Ferdinando
中科院分区：
文献类型：
--
作者：
Madin, Joshua;Bowers, Shawn;Villa, Ferdinando
生态学的研究越来越多。它依赖于整合小型的重点研究，以产生更大的数据集，从而进行更强大的综合分析。这些综合分析的结果对于指导如何可持续地管理我们的自然环境的决策至关重要，因此研究人员必须有效地发现相关数据，并将这些数据适当地整合到他们的分析中。然而，生态数据包含非常广泛的数据类型、结构和语义概念。此外，生态数据分布很广，几乎没有建立完善的储存库或标准的存档和检索协议。这些因素使得生态数据集的发现和整合成为一项高度劳动密集型的任务。生态元数据语言和达尔文核心等元数据标准是提高我们发现和访问生态数据能力的重要步骤，但仅限于描述数据内容的几个相对具体的方面（例如，数据所有者和联系信息、变量“名称”、关键字描述等）。我们需要一种更灵活和强大的方法来捕捉复杂生态数据的语义微妙之处，它的结构和内容，以及数据变量之间的相互关系。本体提供了一个方便的基础上添加详细的语义注释的科学数据，这结晶的内在“意义”的观测数据。本体可以用于表征观察的上下文（例如，空间和时间），并阐明诸如依赖性层次结构（例如，嵌套的实验观察）和数据内有意义的维度（例如，交叉分类分类汇总的轴）。它还支持对测量单位的鲁棒描述（例如，克碳/升海水），并且可以促进自动单位转换（例如，磅至公斤）。本体可以很容易地扩展专门的领域词汇表，使它既广泛适用和高度定制。最后，我们描述了实用的本体丰富的数据发现和集成过程的能力。由爱思唯尔公司出版
Research in ecology increasingly. relies on the integration of small, focused studies, to produce larger datasets that allow for more powerful, synthetic analyses. The results of these synthetic analyses are critical in guiding decisions about how to sustainably manage our natural environment, so it is important for researchers to effectively discover relevant data, and appropriately integrate these within their analyses. However, ecological data encompasses an extremely broad range of data types, structures, and semantic concepts. Moreover, ecological data is widely distributed, with few well-established repositories or standard protocols for their archiving and retrieval. These factors make the discovery and integration of ecological data sets a highly labor-intensive task. Metadata standards such as the Ecological Metadata Language and Darwin Core are important steps for improving our ability to discover and access ecological data, but are limited to describing only a few, relatively specific aspects of data content (e.g., data owner and contact information, variable "names", keyword descriptions, etc.). A more flexible and powerful way to capture the semantic subtleties of complex ecological data, its structure and contents, and the interrelationships among data variables is needed.We present a formal ontology for capturing the semantics of generic scientific observation and measurement. The ontology provides a convenient basis for adding detailed semantic annotations to scientific data, which crystallize the inherent "meaning" of observational data. The ontology can be used to characterize the context of an observation (e.g., space and time), and clarify inter-observational relationships such as dependency hierarchies (e.g., nested experimental observations) and meaningful dimensions within the data (e.g., axes for cross-classified categorical summarization). It also enables the robust description of measurement units (e.g., grams of carbon per liter of seawater), and can facilitate automatic unit conversions (e.g., pounds to kilograms). The ontology can be easily extended with specialized domain vocabularies, making it both broadly applicable and highly custornizable. Finally, we describe the utility of the ontology for enriching the capabilities of data discovery and integration processes. Published by Elsevier B.V.