This comprehensive study conducts an in-depth analysis of existing COVID-19 ontologies, scrutinizing their objectives, classifications, design methodologies, and domain focal points. The study is conducted through a dual-stage approach, commencing with a systematic review of relevant literature and followed by an ontological assessment utilizing a parametric methodology. Through this meticulous process, twenty-four COVID-19 Ontologies (CovOs) are selected and examined. The findings highlight the scope, intended purpose, granularity of ontology, modularity, formalism, vocabulary reuse, and extent of domain coverage. The analysis reveals varying levels of formality in ontology development, a prevalent preference for utilizing OWL as the representational language, and diverse approaches to constructing class hierarchies within the models. Noteworthy is the recurrent reuse of ontologies like OBO models (CIDO, GO, etc.) alongside CODO. The METHONTOLOGY approach emerges as a favored design methodology, often coupled with application-based or data-centric evaluation methods. Our study provides valuable insights for the scientific community and COVID-19 ontology developers, supplemented by comprehensive ontology metrics. By meticulously evaluating and documenting COVID-19 information-driven ontological models, this research offers a comparative cross-domain perspective, shedding light on knowledge representation variations. The present study significantly enhances understanding of CovOs, serving as a consolidated resource for comparative analysis and future development, while also pinpointing research gaps and domain emphases, thereby guiding the trajectory of future ontological advancements.

翻译：本研究对现有新冠肺炎本体进行了深入分析，审视了其目标、分类、设计方法论及领域关注焦点。研究采用双阶段方法进行：首先对相关文献进行系统性综述，继而利用参数化方法开展本体评估。通过这一严谨流程，我们筛选并剖析了二十四种新冠肺炎本体（CovOs）。研究结果揭示了本体的范围、预期用途、粒度、模块化程度、形式化程度、词汇复用情况以及领域覆盖广度。分析表明，本体开发中形式化程度各异，普遍倾向使用OWL作为表征语言，且模型内类层次结构的构建方法不尽相同。值得关注的是，OBO模型（如CIDO、GO等）及CODO等本体的复用现象屡见不鲜。METHONTOLOGY方法成为首选的设计方法论，常与基于应用或数据驱动的评估方法相结合。本研究为科学界及新冠肺炎本体开发者提供了宝贵见解，并辅以全面的本体度量指标。通过细致评估与记录新冠肺炎信息驱动的本体模型，本研究提供了跨领域比较视角，揭示了知识表征的差异性。当前研究显著加深了对CovOs的理解，作为比较分析与未来开发的整合资源，同时指明了研究空白与领域重点，从而引导未来本体发展的方向。