Healthcare data and the knowledge gleaned from it play a key role in improving the health of current and future patients. These knowledge sources are regularly represented as ‘linked’ resources based on the Resource Description Framework (RDF). Making resources ‘linkable’ to facilitate their interoperability is especially important in the rare-disease domain, where health resources are scattered and scarce. However, to benefit from using RDF, resources need to be of good quality. Based on existing metrics, we aim to assess the quality of RDF resources related to rare diseases and provide recommendations for their improvement. Sixteen resources of relevance for the rare-disease domain were selected: two schemas, three metadatasets, and eleven ontologies. These resources were tested on six objective metrics regarding resolvability, parsability, and consistency. Any URI that failed the test based on any of the six metrics was recorded as an error. The error count and percentage of each tested resource were recorded. The assessment results were represented in RDF, using the Data Quality Vocabulary schema. For three out of the six metrics, the assessment revealed quality issues. Eleven resources have non-resolvable URIs with proportion to all URIs ranging from 0.1% (6/6,712) in the Anatomical Therapeutic Chemical Classification to 13.7% (17/124) in the WikiPathways Ontology; seven resources have undefined URIs; and two resources have incorrectly used properties of the ‘owl:ObjectProperty’ type. Individual errors were examined to generate suggestions for the development of high-quality RDF resources, including the tested resources. We assessed the resolvability, parsability, and consistency of RDF resources in the rare-disease domain, and determined the extent of these types of errors that potentially affect interoperability. The qualitative investigation on these errors reveals how they can be avoided. All findings serve as valuable input for the development of a guideline for creating high-quality RDF resources, thereby enhancing the interoperability of biomedical resources.

中文翻译：

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评估 RDF 资源的可解析性、可解析性和一致性：罕见病的一个用例


医疗保健数据和从中收集的知识在改善当前和未来患者的健康状况方面发挥着关键作用。这些知识源通常表示为基于资源描述框架 （RDF） 的“链接”资源。使资源“可链接”以促进其互操作性在卫生资源分散且稀缺的罕见病领域尤为重要。但是，要从使用 RDF 中受益，资源需要具有良好的质量。根据现有指标，我们的目标是评估与罕见病相关的 RDF 资源的质量，并为其改进提供建议。选择了 16 个与罕见病领域相关的资源：2 个模式、3 个元数据集和 11 个本体。这些资源根据有关可解析性、可解析性和一致性的六个客观指标进行了测试。根据 6 个指标中的任何一个未通过测试的任何 URI 都会被记录为错误。记录每个测试资源的错误计数和百分比。评估结果使用 Data Quality Vocabulary 模式在 RDF 中表示。对于六项指标中的三项，评估揭示了质量问题。11 个资源具有不可解析的 URI，占所有 URI 的比例范围从解剖治疗化学分类中的 0.1% （6/6,712） 到 WikiPathways 本体中的 13.7% （17/124）;7 个资源具有未定义的 URI;两个资源错误地使用了 'owl：ObjectProperty' 类型的属性。检查各个错误，以生成开发高质量 RDF 资源（包括测试的资源）的建议。 我们评估了罕见病领域中 RDF 资源的可解析性、可解析性和一致性，并确定了可能影响互操作性的这些类型错误的程度。对这些错误的定性调查揭示了如何避免它们。所有发现都为制定创建高质量 RDF 资源的指南提供了宝贵的输入，从而提高了生物医学资源的互操作性。