Ontologies can be represented as graphs, since they essentially comprise a set of interconnected concepts describing a certain field of knowledge. Consequently, ontologies are often visualized as graphs, using different visual notations and common graph drawing techniques. The visualization of static graphs has been researched a lot, but when it comes to time-varying graphs, researchers face much more challenges in order to design useful, readable, and intuitive visualizations. If we have to deal with dynamic, i.e., evolving and time-dependent ontologies, we have to adapt existing visualization techniques to this challenging problem or develop new ones. In this position paper, we take a look at the visual representation of time-varying ontologies, and provide a discussion from a dynamic graph visualization perspective.
Visualization can be a powerful tool in the exploration and analysis of ontologies. Advanced visual designs are demanded to effectively and efficiently manage, browse, and navigate ontologies, and to finally gain insights and conclusions. The growing number and size of ontologies, on the other hand, require sophisticated visualization techniques that are capable to handle algorithmic, perceptual, and visual scalability problems. Consequently, developers of ontology visualizations need to enhance their visual designs by developing faster and better layouts, by providing more visual features, and by improving existing approaches based on user studies and other evaluation methods.
Although various techniques for ontology visualization have flourished over
the past years [
Since time-varying ontologies can be regarded as some kind of dynamic graph,
we discuss the applicability of existing visualization techniques for dynamic
graphs surveyed by Beck et al. [
Numerous approaches for ontology visualization have been presented in the last
couple of years [
Examples for force-directed graph visualizations of ontologies are provided by
TGViz [
Fu et al. [
Some approaches combine different techniques to visualize ontologies, such as
node-link diagrams and adjacency matrices [
Furthermore, there are diagrammatic approaches that use UML [
However, the visualization of time-varying ontologies has not received any
attention in all these approaches. Although the evolution of ontologies has been
subject to research [
Visualizing time-varying data is challenging due to the fact that users need to obtain an overview of a longer subsequence of individual time steps. This aspect must also be considered for dynamic, i.e., evolving ontologies. Usually, the users have to solve comparison tasks in order to reliably derive trends and countertrends or to find outliers and anomalies. In general, two paradigms for timeoriented visualizations are distinguished: 1) time-to-time mappings (animated diagrams), and 2) time-to-space mappings (static displays, often enhanced by interaction techniques). In the following, we discuss these two alternatives and address some of their benefits and drawbacks. 3.1
If ontologies are visualized as graphs in the form of node-link diagrams, the vertices and edges can have various visual appearances. The vertices usually carry different semantic information as well as additional attributes. If the attributes are of a rather categorical nature, color coding and shape can be used as visual features to support the viewer to efficiently distinguish vertices of different types. Quantities may be visually indicated by varying the sizes of the graphical primitives (circles, triangles, rectangles and the like, see Figure 1a).
Using too many visual features at once, however, can make it troublesome to
visually analyze the ontology for certain aspects. This can be seen as a
conjunction search that does not allow for preattentive processing [
Thing
Item (external) dateTime taggingIcre... SubclassIof (functional) taggedIres... (functional) taggedIwith hasIaccess Tagging grantIaccess creatorIof ha(fusnIcctrioenaatl)or SubclassIof taggingImod...
dateTime UserAccount (external) hasItag tagIof (functional) (c) PrivateITagging
AutomaticITag
Edges can be directed, weighted, attributed, and they can occur multiple times between the same pair of vertices, turning the ontology representation into a multigraph. Directions are typically indicated by arrowheads, color gradients, or with tapered representation styles. Weights can be expressed by color coding or by using differently thick link representations, which produce additional visual clutter on the negative side. Figure 1b illustrates several link representations for directed edges (namely, arrows, partial, tapered, animated, orthogonal, colored, curved, and dark-to-light links).
Figure 1c shows a node-link representation of the small MUTO ontology [
The topology of the graph representation is important, since it conveys useful information on the structure of subgraphs, clusters, or cliques. This aspect is also crucial in ontology visualization, as it can provide useful insights into the ontology structure.
In graph visualization (which is mainly applying node-link visual metaphors),
layout algorithms are typically following aesthetic graph drawing criteria that
produce diagrams with reduced visual clutter, which is “the state in which excess
items, or their representation or organization, lead to a degradation of
performance at some task” [
In many graph datasets, a hierarchical organization among the vertices is of special interest. Either it is inherently present in the data or it can be generated, for example, by a hierarchical clustering algorithm. Ontologies often contain concept hierarchies that have to be visualized in order to visually explore the ontology on different levels of hierarchical granularity or to use the hierarchy as a means to interact, filter, aggregate, and navigate in the ontology.
In Figure 1c, there are only two relatively flat hierarchies, each consisting of
three classes, which can be easily spotted due to the small number of well
arranged vertices. For ontologies with large inheritance trees, other graph layouts,
or even tree visualizations, might be more appropriate to clearly depict the
different hierarchy levels. However, since ontologies allow for multiple inheritance,
simple tree visualizations can be confusing [
The representation of time-varying ontologies demands for sophisticated visualization techniques that consider all the aforementioned features in order to sufficiently support the visual exploration of ontologies for dynamic patterns. Consequently, vertices, edges, the topology and structure, as well as any existing hierarchical organization among the vertices are of special interest.
One major type of tasks that users typically want to answer when inspecting
time-varying data are comparison tasks. Several time steps are visually compared
to derive insights by detecting changes or stabilities over time. To answer such
tasks, the human visual system has to rely on its short term memory allowing
to briefly remember visual patterns which are then compared at different spatial
positions. Only by this internal cognitive process we are able to come up with
the detection of trends, countertrends, or anomaly patterns over time [
If animated diagrams were used for the exploration of dynamic ontologies,
we soon reach a point where the cognitive load becomes high. The human viewer
may have difficulties to visually analyze the time-varying ontology for dynamic
patterns. Advanced and time-complex layout algorithms have to be used to
guarantee a high degree of dynamic stability [
Another option for displaying dynamic ontologies is by means of static
diagrams [
To illustrate this second approach, we created different versions of the MUTO
ontology [
In Figure 2, we see the VOWL representations of six of the MUTO versions, starting with version 0.1 in the upper left and ending with version 1.0 in the lower right. We can observe how the ontology has changed over time. At first, the key concepts and links are defined, which are gradually extended by further concepts and links. At some point, alignments to existing ontologies are added, followed by the definition of datatype properties describing attributes for the key concepts. Finally, subclasses are introduced providing specializations of the key concepts. 3.4
It must be noted that small multiples visualizations usually serve as an overview representation for the time dimension. If users detect a dynamic pattern of interest, they can apply interaction techniques to zoom and filter, and finally get details on demand also for individual ontology versions. The individual ontology
Thing versions might b e represented with existing ontology visualization techniques, as describ ed in Section 2 and applied in Figure 2.
However, interacting with visual representations of time-varying data can b e challenging, in particular, when the visualization is animated, i.e., when the single snapshots are changing over time. Users have to stop the animation when they like to interact with the visualization, otherwise a detected pattern might have already b een disapp eared until the users realize, for example, that they would like to select it for a more detailed exploration. Consequently, time-tospace mappings usually provide b etter means to visualize time-varying ontologies from the p ersp ective of applying interaction techniques, since the ontology sequence is shown in a static fashion. Different ontology versions can b e individually explored and visually connected by linking and brushing, even if this form of graphical representation usually do es not scale to very long time sequences. 4
In this p osition pap er, we discussed the challenge of visualizing time-varying ontologies. We describ ed b enefits and drawbacks of common visualization techniques for dynamic graphs, since ontologies can b e considered as a sp ecial typ e of graphs with additional attributes attached to the vertices and edges.
The visualization of ontologies has mainly b een researched from the p ersp ective of static graphs so far, which is – at least in our opinion – only half of the truth. Ontologies – as many other data structures – are typically not staying static but evolve over time. Consequently, the visualization of ontology evolution is a topic of its own and worth discussing.
For future work, we plan to apply dynamic graph visualization techniques to time-varying ontologies. Typically, we first need an overview representation which shows dynamic patterns in a static view, allowing the user to effectively and efficiently dig deep er and analyze the time-varying ontology.