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SEE: structured representation of scientific evidence in the biomedical domain using Semantic Web techniques

机译:SEE:使用语义网技术在生物医学领域对科学证据进行结构化表示

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Background Accounts of evidence are vital to evaluate and reproduce scientific findings and integrate data on an informed basis. Currently, such accounts are often inadequate, unstandardized and inaccessible for computational knowledge engineering even though computational technologies, among them those of the semantic web, are ever more employed to represent, disseminate and integrate biomedical data and knowledge. Results We present SEE ( S emantic E videnc E ), an RDF/OWL based approach for detailed representation of evidence in terms of the argumentative structure of the supporting background for claims even in complex settings. We derive design principles and identify minimal components for the representation of evidence. We specify the Reasoning and Discourse Ontology (RDO), an OWL representation of the model of scientific claims, their subjects, their provenance and their argumentative relations underlying the SEE approach. We demonstrate the application of SEE and illustrate its design patterns in a case study by providing an expressive account of the evidence for certain claims regarding the isolation of the enzyme glutamine synthetase. Conclusions SEE is suited to provide coherent and computationally accessible representations of evidence-related information such as the materials, methods, assumptions, reasoning and information sources used to establish a scientific finding by adopting a consistently claim-based perspective on scientific results and their evidence. SEE allows for extensible evidence representations, in which the level of detail can be adjusted and which can be extended as needed. It supports representation of arbitrary many consecutive layers of interpretation and attribution and different evaluations of the same data. SEE and its underlying model could be a valuable component in a variety of use cases that require careful representation or examination of evidence for data presented on the semantic web or in other formats.
机译:背景证据对评估和复制科学发现以及在知情的基础上整合数据至关重要。当前,即使计算技术(其中包括语义网的计算技术)被越来越多地用来表示,传播和整合生物医学数据和知识,但对于计算知识工程来说,此类帐户通常仍不足够,不规范且无法访问。结果我们提出了SEE(语义E Videnc E),这是一种基于RDF / OWL的方法,即使在复杂的环境中,也可以根据索赔支持背景的论证结构来详细表示证据。我们得出设计原则,并确定用于表示证据的最少组件。我们指定了推理与话语本体论(RDO),它是科学主张模型,其主题,其出处和它们在SEE方法基础上的论证关系的OWL表示。我们通过案例研究来证明SEE的应用并说明其设计模式,该案例通过提供有关谷氨酰胺合成酶分离的某些声明的证据来表达。结论SEE适合通过采用对科学结果及其证据的一致主张基于观点的观点,来提供与证据有关的信息的连贯和可计算访问的表示形式,例如用于建立科学发现的材料,方法,假设,推理和信息来源。 SEE允许可扩展的证据表示形式,在其中可以调整详细程度并可以根据需要进行扩展。它支持任意多个连续的解释和归因层表示,以及对同一数据的不同评估。 SEE及其基础模型可能是各种用例的重要组成部分,这些用例需要仔细表示或检查语义网上或其他格式的数据。

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