III: Small: Methods for Auditing and Enhancing Completeness of Ontologies

本体是通过一组概念（术语）和专业领域内这些概念之间的关系对知识的正式表示。本体已被广泛用于编排的编码，管理，交换和共享的生物医学研究企业产生的越来越多的大量的数字数据。例如，SNOMED CT是最大和最复杂的生物医学本体之一，支持开发高质量的电子健康记录，并促进患者亚组识别，临床决策支持和医疗保健服务质量测量。鉴于生物医学本体发挥着如此重要的作用，质量问题（如子类覆盖的不完整性），如果不解决，可能会影响诊断和决策的质量。然而，由于生物医学本体的规模和复杂性，仅通过手工工作来解决诸如缺失层次关系和缺失概念之类的不完整性问题是不可行的。该项目的目标是开发自动化和可扩展的方法来识别潜在的不完整性问题，并提出解决方案来修复它们。本项目将把计算方面的工作纳入与数据科学相关的课程开发和教育中，并促进女性参与数据科学。为了审计和提高本体的完整性，本项目探讨了以下研究任务：（1）开发一个强大的推理框架，用于检测和修复缺失的子类或层次关系。这将导致直接增强本体的子类完整性的建议;（2）开发新的方法来识别丢失的概念，并为识别出的丢失概念创建适当的名称标签。这将导致本体的概念完整性的增强;（3）通过利用丰富的外部知识为建议的解决方案生成支持证据。为了进一步验证所提出的方法的鲁棒性，领域专家将参与验证所发现的不完整性问题。所提出的方法被应用到生物医学中的三个大型本体：SNOMED CT，基因本体和NCI词库。建议的本体更改将传达给相应的本体所有者，以纳入后续版本。该项目网站将包括有关该项目的更多信息，并提供出版物，软件，数据集和课程材料的访问。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

An ontology is a formal representation of the knowledge by a set of concepts (terms) and the relationships between those concepts within a domain of specialty. Ontologies have been widely used for orchestrating the coding, management, exchange, and sharing of the increasingly large amounts of digital data produced by the biomedical research enterprise. For example, SNOMED CT, one of the largest and most complex biomedical ontologies, supports the development of high-quality electronic health records and facilitates patient subgroup identification, clinical decision support, and healthcare delivery quality measurement. Given such important roles that biomedical ontologies play, quality issues (such as incompleteness in coverage of subclasses), if not addressed, can affect the quality of diagnoses and decisions. However, incompleteness issues such as missing hierarchical relations and missing concepts are infeasible to be addressed by manual work alone due to the size and complexity of biomedical ontologies. The goal of this project is to develop automated and scalable approaches for identifying potential incompleteness issues as well as suggesting solutions to fix them. This project will incorporate the computational aspects of the proposed work into curriculum development and educational offerings related to data science and promote women participation in data science.To audit and enhance completeness of ontologies, this project explores the following research tasks: (1) Development of a robust reasoning framework for detecting and repairing missing subclass or hierarchical relations. This will result in suggestions that directly enhances the subclass completeness of ontologies; (2) Development of novel methods for identifying missing concepts and creating appropriate name labels for the identified missing concepts. This will result in enhancement in the concept completeness of ontologies; (3) Generation of supporting evidence for suggested solutions by leveraging rich extrinsic knowledge. For further verification of the robustness of the proposed approaches, domain experts will be involved in validation of the discovered incompleteness issues. The proposed approaches are applied to three large ontologies in biomedicine: SNOMED CT, Gene Ontology, and NCI Thesaurus. Suggested ontology changes will be communicated to the respective ontology owners for incorporation in subsequent versions. The project website will include further information about this project, and provide access to publications, software, datasets and curriculum material.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.