A family-based framework of quality assurance for biomedical ontologies

描述（由申请人提供）：我们将为生物医学本体开发一个基于家族的质量保证（QA）框架。本体QA对于增加本体在跨学科研究和电子健康记录（EHR）中的使用至关重要。我们将开发计算技术，用于识别具有高错误概率的概念，以提高本体QA的效率和有效性。 生物医学本体是一种大型的、复杂的知识表示系统，它能够整合来自不同领域的知识。最大、最知名的本体库是国家生物医学本体中心的Bioportal，包含300多个本体和用于编辑、浏览和可视化这些本体的工具。 然而，在BioPortal的本体中发现了许多错误。BioPortal中的QA主要集中在用例和特别技术上。我们的计算技术将自动识别概念集的错误的可能性很高，使本体QA。 在过去的研究中，我们已经设计了许多QA技术的单一本体，并已表明，复杂的和不常见的分类概念集有显着较高的错误率。我们的QA的理论基础是抽象网络（AbN），它以紧凑的方式总结本体。使用AbN，我们识别了许多容易出错的概念。 在这个项目中，我们将执行QA的本体的整个家庭。我们已经根据结构特性确定了七个初步的族。如果一个概念的分类在一个F族的几个本体中产生比通常更高的错误率，那么我们假设这对于F族的大多数本体的分类都是正确的。我们将建立一个原型软件工具（BLUOWL），用于确定每个家庭的AbN，以支持其本体的QA。 我们的主要测试平台将是七个与癌症相关的本体，例如，美国国家癌症研究所词库（NCIt），具有不同的属性和目的。一些非癌症的本体论也将包括在内。我们已经公布了初步的QA结果，为四个这样的本体。 在评估研究中，我们将制定和测试假设，统计表示 各种概念的错误预期。Ontologies的策展人被招募来审查我们将识别的可疑概念，作为他们定期QA工作的一部分（在我们的 预算）。 总之，我们将：根据本体结构识别BioPortal本体的家族，并设计一种统一的方法来推导其抽象网络;为每个家族的QA构建一个软件工具（BLUOWL）;调查每个家族中更可能出错的概念分类;对我们的QA方法和BLUOWL的可用性研究进行评估。

DESCRIPTION (provided by applicant): We will develop a family-based Quality Assurance (QA) framework for biomedical ontologies. Ontology QA is critical for increasing the use of ontologies in interdisciplinary research and in electronic health records (EHRs). We will develop computational techniques for identifying concepts with high probability of errors to improve efficiency and effectiveness of ontology QA. Biomedical ontologies are large, complex knowledge representation systems that enable the integration of knowledge from different fields. The largest, best-known ontology repository is the Bioportal of the National Center for Biomedical Ontologies, containing more than 300 ontologies and tools for editing, browsing, and visualizing these ontologies. However, many errors have been discovered in BioPortal's ontologies. QA in BioPortal has been mostly focused on use-cases and ad hoc techniques. Our computational techniques will automatically identify sets of concepts with a high likelihood of errors to empower ontology QA. In past research, we have designed many QA techniques for single ontologies and have shown that sets of complex and uncommonly classified concepts have significantly higher percentages of errors. The theoretical bases for our QA are Abstraction Networks (AbNs), which summarize ontologies in a compact way. Using AbNs, we identified many error-prone concepts. In this project, we will perform QA for whole families of ontologies. We have already identified seven preliminary families, based on structural properties. If a classification of concepts yields higher than usual error rates in several ontologies of a family F then we hypothesize that this will be true for such classifications for most ontologies of F. We will build a prototype software tool (BLUOWL) for determining AbNs for each family, to support QA of its ontologies. Our primary test beds will be seven cancer-related ontologies, e.g., the National Cancer Institute thesaurus (NCIt), with different properties and purposes. Some non-cancer ontologies will also be included. We have published preliminary QA results for four such ontologies. In evaluation studies, we will formulate and test hypotheses, statistically expressing the error expectations for various kinds of concepts. Ontologies' curators were recruited to review the suspicious concepts we will identify as part of their regular QA efforts (outside of our budget). In summary, we will: Identify families of BioPortal ontologies based on ontology structure and design a unified methodology for deriving their abstraction networks; Build a software tool (BLUOWL) for QA of each family; Investigate concept classifications more likely to be erroneous in each family; Perform evaluation of our QA methodologies and usability studies for BLUOWL.