Scene-Graph Design for Knowledge Memory SpaceBoonprasert Surakratanasakul 1 and Kazuhiko Hamamoto 2121

Graduate School of Science and Technology, Tokai University, Japan
School of Information and Telecommunication Engineering, Tokai University, Japan

Introduction

On knowledge management strategies, knowledge engineer
needs to explore a lot of past knowledge information according
to a current situation and then pile the new ideas upon. The
difficultly is to find an appropriate way for manage and maintainhuge knowledge’s information in a schematic way on anunderstanding view. Knowledge memory system on virtual space
is a scenic representation of huge information that is organizedspatially and temporally [1]. It enables knowledge engineer tovisually grasp and explore the information space foraccommodate new information at an appropriate place based onmanner understandable and knowledge management concept.

Domain knowledge
Relation

instance ofKnowledge baseRule TypeTo develop a knowledge memory space, we create aknowledge schema by with CommonKADS methodology [2], iscalled “Knowledge Landscape” schema [3]. The purpose of theschema is to explicate in detail the types and structures of theknowledge used in performing task and concentrate on theconceptual structure of knowledge. For simulation the virtual
memory space, we design a scene graph from the schema, to be
an imaginary guideline before implementation. The benefit frommanage knowledge on the virtual space are simple and wellknown by learning from experiences in a controlled spiral that
understandable for humans. In addition, it makes an importantvehicle role for communication with experts and users during
both development and system execution.
2structured

Concept

describe

Knowledge Instanceact ascomposed byInference knowledge
I/O role
Inference
Transferfunction

Roletransfersext. info.
Dynamicused

Task knowledge
Task

Staticrealized

Task Method

State of the Art

CommonKADS methodology originates from the need tobuild industry-quality knowledge systems on a large scale, in a
structured, controllable, and repeatable way. It has a predefined
set of models that focusing and providing a comprehensive viewfor developing knowledge system. The one important model is
the knowledge model, that we choose to apply for develop thevirtual space.
2.1

Knowledge Landscape Schema
Knowledge landscape is a technique that helps to clarify thestructure of a knowledge-intensive task and provide aspecification of data and knowledge structures. It has an essence
structure that similar to analysis models in software engineering.
So we apply an UML extension mechanism [4] to develop
schema for these system.
In knowledge landscape concept, it consists of three parts
each capturing a related group of knowledge structure called“Knowledge category”: Domain knowledge, Inference
knowledge, and Task knowledge. Domain knowledge specifies
the domain specific knowledge and information types. Its
modeling implies capturing the static structure of information andknowledge types. Inference knowledge describes the basic
inference steps that want to make using domain knowledge. It
describes how these static structures can be used to carry out areasoning process. Task knowledge describes what goals andapplication pursues, and how these goals can be realized throughdecomposition into subtasks and ultimately inferences. Fig. 1
shows the knowledge landscape schema that applied from theCommonKADS methodology.

Figure 1. Landscape of Knowledge Schema.

World Tool KitTM’s Scene graph
World Tool KitTM (WTK) is a virtual reality library written
in C/C++. It manages the details of reading sensor input,
rendering scene geometry, and loading databases. An
application developer only needs to worry about manipulating
the simulation and changing the scene graph based on user
inputs. In addition, it also allows the developer to edit the scenegraph by hand if that level of control is needed [5].
Generally, a common definition of a graph is a data structurecomposed of nodes and arcs. A node is a data element, and arc
is a relationship between data elements [6]. To render the scenegraph, WTK provides function for creating nodes and placing
them at specific positions in the scene graph. The developer
might be concerned the scene graph in common rules:• The scene graph is rendered automatically into the window
as the simulation runs.
• Different scene graphs may have common sub-trees. Thismeans that the same geometry can be referenced by more than
one scene graph.
• Each scene graph has a single root node.
• Traversal begins at the root node of the scene graph.
• The renderer traverses the tree from top to bottom and leftto right.
• Depending on the type of node, WTK will do different
things. Nodes in WTK can be grouped into three distinct types:
Geometry node, Attribute node, and Procedural node.
2.2

Geometry nodes contain the representation of visible entities
by draw the specified set of polygons. Attribute nodes used toaffect the way geometry nodes are rendered by modify thecurrent state, which determines the appearance of subsequent
geometry. Procedural nodes used to control the way a scenegraph is put together by process the children of this node,depending on the type of traversal directed by the node.
For managing the state of the scene graph, Separator andTransform Separator nodes are used to manage the state of thescene graph by isolating the effects of the attribute nodes.

for examples; the location, orientation, and properties ofknowledge. We develop the scene graph from the knowledge
landscape schema, which applies from the CommonKADS
knowledge model.
IItrr
3IIr

Functional view

Knowledge Memory Space’s Scene-graphLogical view

From the knowledge landscape schema, we design the scenegraph shown in Fig 2.

CC<Root>

kkkkka<Light><XForm><XForm Sep>

Landscape

Task K.

Inf. Groupaaa

Legend:

<Geo>

<Geo>

<XForm Sep>

<Geo>

Inf. K.

Trans fn.

K. BaseK. role

<Geo>

<LOD>

Concept

Domain K.

t

Task Knowledge LineI

Inference Knowledge Step

Transfer functionrKnowledge role

C Knowledge conceptk Knowledge instancea Attribute instance

Knowledge base

Figure 3. Simulation scene of Knowledge Memory Space
<Geo>

<Geo>

ReferencesK. Inst.

Attr. Inst.

[1]

Figure 2. Scene Graph of Knowledge Memory Space.

From the scene graph, it starts at the root node. The root node
has relationships with three nodes. Light node specifies a lightfor the landscape virtual space. Transform node sets position andorientation information for develop Task method into Task
knowledge. The transform separator manages the sub-graph ofInference knowledge level. In inference level, it consists ofgeometry node of Inference knowledge, Transfer function, andKnowledge role. The transform separator in this level separates
the Domain knowledge level by scope of the knowledge base. In
this level, it has concept node and level of detail (LOD) node that
describes the knowledge instance and attribute instance ingeometry node.
For imaginary outstanding, we show a draft of simple
simulation scene in Fig 3. This scene has two layers: functionalview layer and logical view layer. The functional view describes
the knowledge function with the Inference and Task knowledge.
The logical view describes the knowledge entity and clarifies theconcept of knowledge. Both of layers correlate by knowledge
instance (in logical view) and knowledge role (in functionalview).
4Conclusion

Knowledge memory space is a memory system that has an
objective view enables a user to edit contents on surface by using
geographical arrangement and topological connection. It provides
an overview of large data content and facilitate for knowledge
sharing by people and increasing their connectivity as alightweight activity.
To develop the virtual memory space, the scene graph is an
important tool for constructed render the simulation. It
completely specifies the state of information on knowledge node,

[2]

[3]

[4][5][6]

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