SEKE 2012 Proceedings - Knowledge Systems Institute

shown at Figure 5. Objectives are shown as cylinders under
the node called Ob jectiveList. Tasks are represen ted as
cones and placed u nder the objectives they are as sociated
with. The relationships among objectives and tas ks are
linked by lines. The tasks are sorted by the task priority,
which means a tas k located at a h igher position has a
higher priority than the task or tasks located at t he lower
positions. Moreover, the priority of a task is co rrelated to
the size of the represented node. A task that has a higher
priority is larger than the task that has a low priority.
Figure 4. Visualized static knowledge model of CO 2 capture ontology
Figure 5. Visualized dynamic knowledge model of CO 2 capture ontology
V. DISCUSSION
A survey of Onto3DViz has been conducted with three
users, who completed a qu estionnaire with 20 questions
about the tool’s support for knowledge engineering, user
experience, and user recommendations. T he results
suggested that this tool with the developed visualized
conceptual models is helpful for enhancing understanding
of domain knowledge in the process of knowledge
acquisition.
Some strengths of Onto3DViz are summarized as
follows. Onto3DViz supports the IMT during the
knowledge acquisition process and e nables visualizing
complex domain knowledge in a 3D model. The visualized
model of the domain knowledge supports design of the
KBS because the visualized knowledge model can help the
knowledge engineer gain quicker understanding of the
concepts and relationships among the concepts. It supports
communication between the domain expert and knowledge
engineer so t hat it is not limited to oral discus sion and
textual recording. Hence, it can be used for bridging the
knowledge gap between the domain expert and knowledge
engineer in the knowledge acquisition process. Mo reover,
the preliminary design of the knowledge model can be
visualized in Onto3DViz. Then, the domain expert and
knowledge engineer can discuss and fine tune the
visualized model to become the final design.
Several weaknesses of the current version of
Onto3DViz are also n oted. First, the visualized concepts
are not easy to identify in the 3D model, especially when
the knowledge model is complex. The control actions of
rotating, zooming, and translating of the 3D model requires
some learning time. Secondly, the visual objects and labels
are too close together in some places, red ucing the
expressiveness of the model. Furthermore, the nodes and
lines overlap at some spots in the 3D visualized model; this
problem needs to be a ddressed in the next version of
Onto3DViz. Thirdly, Onto3DViz lacks a search function
for concepts.
VI. CONCLUSIONS AND FUTURE WORK
The survey of existing ontology visualization tools
revealed that they are i nadequate in dynamic knowledge
visualization and visualization of a larg e amount of
information due to li mitations of the 2D g raphical space.
Onto3DViz is a n ew ontology visualization tool th at is
designed to address these weaknesses. As On to3DViz is
developed according to t he knowledge engineering
technique of the IMT methodology, it supports both static
and dynamic knowledge visualization. By allocating visual
objects to 3 different planes in a 3D space, Onto3DViz can
support visualization of a large amount of information.
Onto3DViz can represent a complex knowledge
model by rendering the concepts using objects of different
shapes and placing them at designated 3D position s.
Visualization of the CO 2 capture ontology demonstrates
Onto3DViz’s capabilities in generating a 3D model of
static knowledge and dynamic knowledge. We believe that
722