Dórea Fernanda C, Vial Flavie, Hammar Karl, Lindberg Ann, Lambrix Patrick, Blomqvist Eva, Revie Crawford W
Department of Disease Control and Epidemiology, National Veterinary Institute, Sweden.
Epi-Connect, Skogås, Sweden.
Prev Vet Med. 2019 May 1;166:39-48. doi: 10.1016/j.prevetmed.2019.03.002. Epub 2019 Mar 9.
Comprehensive reviews of syndromic surveillance in animal health have highlighted the hindrances to integration and interoperability among systems when data emerge from different sources. Discussions with syndromic surveillance experts in the fields of animal and public health, as well as computer scientists from the field of information management, have led to the conclusion that a major component of any solution will involve the adoption of ontologies. Here we describe the advantages of such an approach, and the steps taken to set up the Animal Health Surveillance Ontological (AHSO) framework. The AHSO framework is modelled in OWL, the W3C standard Semantic Web language for representing rich and complex knowledge. We illustrate how the framework can incorporate knowledge directly from domain experts or from data-driven sources, as well as by integrating existing mature ontological components from related disciplines. The development and extent of AHSO will be community driven and the final products in the framework will be open-access.
对动物健康综合征监测的全面综述强调了，当数据来自不同来源时，各系统在整合及互操作性方面存在的障碍。与动物健康和公共卫生领域的综合征监测专家，以及信息管理领域的计算机科学家进行的讨论得出结论，任何解决方案的一个主要组成部分都将涉及采用本体论。在此，我们描述这种方法的优势，以及建立动物健康监测本体论（AHSO）框架所采取的步骤。AHSO框架以OWL建模，OWL是W3C标准语义网语言，用于表示丰富和复杂的知识。我们举例说明该框架如何直接纳入来自领域专家或数据驱动源的知识，以及如何通过整合相关学科现有的成熟本体论组件来实现这一点。AHSO的开发和范围将由社区驱动，框架中的最终产品将开放获取。
