Computer Science and Software Engineering Department, Monmouth University, West Long Branch, NJ, USA.
Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, USA.
J Biomed Inform. 2021 Aug;120:103861. doi: 10.1016/j.jbi.2021.103861. Epub 2021 Jul 2.
The current intensive research on potential remedies and vaccinations for COVID-19 would greatly benefit from an ontology of standardized COVID terms. The Coronavirus Infectious Disease Ontology (CIDO) is the largest among several COVID ontologies, and it keeps growing, but it is still a medium sized ontology. Sophisticated CIDO users, who need more than searching for a specific concept, require orientation and comprehension of CIDO. In previous research, we designed a summarization network called "partial-area taxonomy" to support comprehension of ontologies. The partial-area taxonomy for CIDO is of smaller magnitude than CIDO, but is still too large for comprehension. We present here the "weighted aggregate taxonomy" of CIDO, designed to provide compact views at various granularities of our partial-area taxonomy (and the CIDO ontology). Such a compact view provides a "big picture" of the content of an ontology. In previous work, in the visualization patterns used for partial-area taxonomies, the nodes were arranged in levels according to the numbers of relationships of their concepts. Applying this visualization pattern to CIDO's weighted aggregate taxonomy resulted in an overly long and narrow layout that does not support orientation and comprehension since the names of nodes are barely readable. Thus, we introduce in this paper an innovative visualization of the weighted aggregate taxonomy for better orientation and comprehension of CIDO (and other ontologies). A measure for the efficiency of a layout is introduced and is used to demonstrate the advantage of the new layout over the previous one. With this new visualization, the user can "see the forest for the trees" of the ontology. Benefits of this visualization in highlighting insights into CIDO's content are provided. Generality of the new layout is demonstrated.
当前,针对 COVID-19 的潜在疗法和疫苗的密集研究将极大地受益于标准化 COVID 术语本体。冠状病毒传染病本体 (CIDO) 是几个 COVID 本体中最大的一个,并且还在不断发展,但它仍然是一个中等规模的本体。复杂的 CIDO 用户,他们需要的不仅仅是搜索特定的概念,还需要对 CIDO 进行定位和理解。在之前的研究中,我们设计了一个名为“局部区域分类法”的摘要网络来支持本体的理解。CIDO 的局部区域分类法的规模比 CIDO 小,但对于理解来说仍然太大。我们在这里提出 CIDO 的“加权聚合分类法”,旨在为我们的局部区域分类法(和 CIDO 本体)的各种粒度提供紧凑的视图。这种紧凑的视图提供了本体内容的“全貌”。在之前的工作中,在用于局部区域分类法的可视化模式中,节点是根据其概念的关系数量按层次排列的。将此可视化模式应用于 CIDO 的加权聚合分类法会导致过于冗长和狭窄的布局,由于节点名称几乎无法读取,因此不支持定位和理解。因此,我们在本文中引入了加权聚合分类法的创新可视化,以更好地定位和理解 CIDO(和其他本体)。引入了一种用于布局效率的度量标准,并用于证明新布局相对于旧布局的优势。通过这种新的可视化,用户可以“一叶障目,不见泰山”地了解本体。提供了这种可视化在突出 CIDO 内容见解方面的好处。演示了新布局的通用性。