Big knowledge visualization of the COVID-19 CIDO ontology evolution

Abstract

Background: The extensive international research for medications and vaccines for the devastating COVID-19 pandemic requires a standard reference ontology. Among the current COVID-19 ontologies, the Coronavirus Infectious Disease Ontology (CIDO) is the largest one. Furthermore, it keeps growing very frequently. Researchers using CIDO as a reference ontology, need a quick update about the content added in a recent release to know how relevant the new concepts are to their research needs. Although CIDO is only a medium size ontology, it is still a large knowledge base posing a challenge for a user interested in obtaining the "big picture" of content changes between releases. Both a theoretical framework and a proper visualization are required to provide such a "big picture".

Methods: The child-of-based layout of the weighted aggregate partial-area taxonomy summarization network (WAT) provides a "big picture" convenient visualization of the content of an ontology. In this paper we address the "big picture" of content changes between two releases of an ontology. We introduce a new DIFF framework named Diff Weighted Aggregate Taxonomy (DWAT) to display the differences between the WATs of two releases of an ontology. We use a layered approach which consists first of a DWAT of major subjects in CIDO, and then drill down a major subject of interest in the top-level DWAT to obtain a DWAT of secondary subjects and even further refined layers.

Results: A visualization of the Diff Weighted Aggregate Taxonomy is demonstrated on the CIDO ontology. The evolution of CIDO between 2020 and 2022 is demonstrated in two perspectives. Drilling down for a DWAT of secondary subject networks is also demonstrated. We illustrate how the DWAT of CIDO provides insight into its evolution.

Conclusions: The new Diff Weighted Aggregate Taxonomy enables a layered approach to view the "big picture" of the changes in the content between two releases of an ontology.

Keywords: Aggregate partial-area taxonomy; Big knowledge visualization; Big picture evolution; CIDO ontology; COVID-19 ontology; Coronavirus ontology; Evolution of ontologies; Summarization network.

Fig. 1

The derivation of two summarization networks for a CIDO excerpt: a A hierarchy excerpt of 18 process concepts from CIDO. Concepts, denoted by rounded corner boxes, are linked by hierarchical IS-A relationships represented by thin black upward arrows. Root concepts are denoted by a bold frame. Each rectangle indicates a group of concepts with exactly the same set of lateral (non-hierarchical semantic) relationship types (i.e., same structure.) Rectangles are color coded according to the number of lateral relationship types that their concepts have. The names of the lateral relationships are shown in bold inside the colored rectangles. b The area taxonomy summarization network for (a). The nodes, displayed as rectangular boxes filled with solid colors, represent areas and the links connecting area nodes are the hierarchical child-of relationships (shown as thick black upward arrows.) The area node without an emanating child-of link is called a root area node. Area nodes are color-coded in the same way as the colored rectangles in (a) and organized based on the length of their path to the root area node. c The partial-area taxonomy summarization network for (a). The partial-area taxonomy is a refinement of the area taxonomy. The nodes representing partial-areas are illustrated as white boxes within area nodes. A number in () indicates the number of concepts summarized by a partial-area. As in (b), partial-area nodes are connected by hierarchical child-of links shown as thick upward arrows

Fig. 2

The aggregation process of an excerpt of the CIDO partial-area taxonomy: a An excerpt of the partial-area taxonomy for the June 14, 2020 release of CIDO. b The weighted aggregate taxonomy obtained for the partial-area taxonomy in (a) using parameter b = 10. The aggregate partial-area nodes are shown with rounded-corner rectangles

Fig. 3

a The excerpt of the partial-area taxonomy for the August 1, 2022 release of CIDO, corresponding to Fig. 2. b The weighted aggregate taxonomy obtained for (a) using b = 10

Fig. 4

The Diff Weighted Aggregate Taxonomy between the two WATs of Figs. 2(b) and 3(b). Modified nodes, unmodified nodes, introduced nodes, and removed nodes are shown with a yellow, white, green, and red background, respectively. Child-of relationships connecting diff nodes are colored green, red, or black if they were introduced, removed, or unmodified, respectively. A summary of content changes is shown below the label of each area and partial-area. For the areas containing diff nodes, the global changes of the number of relationships and concepts are shown in bold below the label of such areas. To avoid information redundancy, we do not show such information for areas which have changes all due to introduced nodes or removed nodes, since it is obvious that all different concepts are new or removed from this area, respectively

Fig. 5

The weighted aggregate taxonomy for the June 2020 release of CIDO using b = 29

Fig. 6

The weighted aggregate taxonomy for the August 2022 release of CIDO using b = 29 same as the value of b for the old release in Fig. 5

Fig. 7

The Diff Weighted Aggregate Taxonomy between the two WATs of Figs. 5 and 6

Fig. 8

The weighted aggregate taxonomy for the August 2022 release of CIDO using b = 55

Fig. 9

The Diff Weighted Aggregate Taxonomy between the two WATs of Figs. 5 and 8

Fig. 10

a the secondary subject WAT (b = 5) under the major subject ‘peptide’ in Fig. 5. b the secondary subject WAT (b = 5) under the major subject ‘peptide’ in Fig. 6. c the Diff Weighted Aggregate Taxonomy between the two WATs of (a) and (b)