A family-based framework of quality assurance for biomedical ontologies

基于家族的生物医学本体质量保证框架

• 批准号: 8802486
• 负责人: Yehoshua Perl
• 金额: $ 59.8万
• 依托单位: NEW JERSEY INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-03-04 至 2018-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8802486
• 关键词: Beds; Biomedical Research; Budgets; Classification; Clinical Research; Complex; Computational Technique; Derivation procedure; Detection; Effectiveness; Electronic Health Record; Ensure; Evaluation; Evaluation Studies; Family; Funding; Interdisciplinary Study; Knowledge; Light; Malignant Neoplasms; Methodology; Methods; National Cancer Institute; Ontology; Probability; Process; Property; Publishing; Recruitment Activity; Research; Research Design; Research Project Grants; Semantics; Software Tools; Structure; System; Techniques; Testing; Thesauri; United States; War; Work; anticancer research; base; biomedical ontology; design; empowered; expectation; health care delivery; improved; information organization; innovation; prototype; public health relevance; quality assurance; repository; theories; tool; usability

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.

描述(申请人提供)：我们将为生物医学本体论开发一个基于家庭的质量保证(QA)框架。本体质量保证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来识别错误概率较高的概念，以提高本体问答的效率和效果。生物医学本体是大型的、复杂的知识表示系统，它能够集成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论中心的Bioptal，它包含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，在生物门户网站的本体论中发现了许多错误。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错误可能性很高的概念集，以增强本体QA。在过去的研究中，我们为单一本体设计了许多QA技术，并表明复杂和不常见的分类概念集具有明显更高的错误百分比。我们的QA的理论基础是抽象网络(ABN)，它以一种简洁的方式总结了本体。使用ABN，我们确定了许多容易出错的概念。在这个项目中，我们将对整个本体论家族进行QA。我们已经根据结构属性确定了七个初步族。如果概念的分类在F族的几个本体中产生比通常更高的错误率，那么我们假设这将适用于F的大多数本体的这种分类。我们将构建一个原型软件工具(BLUOWL)来确定每个家族的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，国家癌症研究所辞典(NCIT)，具有不同的属性和目的。一些非癌症本体论也将被包括在内。我们已经发布了四个这样的本体的初步QA结果。在评估研究中，我们将制定和测试假设，统计表达 对各种概念的误差预期。本体论的策展人被招募来审查可疑概念，我们将其确定为其常规QA工作的一部分(在我们的 预算)。总而言之，我们将：基于本体结构识别生物门户本体论家族，并设计用于推导其抽象网络的统一方法；为每个家族的QA构建软件工具(BLUOWL)；调查每个家族中更有可能出错的概念分类；对我们的QA方法进行评估，并对BLUOWL进行可用性研究。