SEKE 2012 Proceedings - Knowledge Systems Institute

collected in preparation for the drawing algorithm in the
next.
2) Recursive drawing algorithm: for each v isual object.
According to th e information that is collected from the
previous step, the drawing algorithm assigns coordinate for
each visual object so as to optimize the graphical space. In
such a way that the children classes are placed un der their
parent classes with suitable length of distance.
3) Post-process rendering information: t he visual
representation of the visualized ontology is further
optimized so as to minimize the visual nodes crossing each
other, separate nodes colliding together, and eli minate
redundant visual objects.
E. User Interaction
Interactions between the 3D model generated by
Onto3DViz and t he user are effected using the physical
devices of computer mouse and keyboard. Both the mouse
and keyboard can be used for controlling operation of the
3D model in Onto3DViz. By combining these user control
actions, users can manipulate the 3D ontology model and
obtain multiple perspectives of the 3D model of an
application ontology.
IV. APPLICATION CASE STUDY
In order to demonstrate functionalities of Onto3DViz,
the tool was applied for visualization of CO 2 capture
process system ontology model. The ontology of carbon
dioxide capture process system was developed and
implemented on Protégé and D yna at th e Energy
Informatics Laboratory at the University of Regina, Canada.
The knowledge modeling process was originally conducted
based on the IMT, and th e knowledge model consists of
both static and dynamic knowledge. The static knowledge
includes the information on constructive components of the
reaction instruments, fluids, and the control devices in the
CO 2 capture system. The dynamic knowledge specified
operation tasks of the CO 2 capture process system, which
are expressed as the control strategies for dealing with 25
critical process parameters when a fault condition emerges.
Onto3DViz was applied for visualizing this ontology so as
to test and verify the visualization capability of Onto3DViz
in a co mplex industrial domain. An overview hierarchical
representation of the concepts in the CO 2 capture ontology
is shown in Figure 2.
CO 2 capture Plant
Static Objects
Dynamic Objects
Valves Pumps Water Solvent
Reaction Instruments
Gases
Flue Gas Off Gas CO 2 Steam
Figure 2. Concepts in CO 2 capture plant [9]
The ontology of the CO 2 capture process generated by
Protégé and Dyna is stored into two files, an OWL and an
XML file. After loading these files into Onto3DViz, a 3D
ontology visualization is generated in Onto3DViz, as
shown in Figure 3. Figure 3 shows a co mplete visualized
model of carbon dioxide capture ontology, which consists
of both static and dynamic knowledge. Static knowledge is
represented by the process param eters shown in the
foreground. The spherical objects represent the class
hierarchy of the knowledge structure. Dynamic knowledge
is displayed in the background and is rep resented by
cylinders and cones. To further explore the carbon dioxide
capture ontology in this model, a user can choose to f ilter
out some context.
Figure 3. 3D Visualized model of CO 2 capture ontology
By filtering out the dynamic knowledge, a visualized
static knowledge model of carbon dioxide capture ontology
is shown in Figure 4. Classes are shown as spheres in the
foreground, and insta nces are shown as box es in the
background. To obtain a clearer view, the user may need to
perform the actions of zooming, rotation, and translation.
By filtering out the static knowledge, a visualized dynamic
knowledge model of carbon diox ide capture ontology is
721