Investigating Visualization Ontologies
Gao Shu1
Nick J.Avis2 Omer F. Rana2
1
School of Computer Science, Wuhan University of Technology, Hubei 430063, China
2
School of Computer Science, Cardiff University, U.K
Abstract
The advent of the Grid Computing paradigm and the link to Web Services provides fresh
challenges and opportunities for collaborative visualization. Ontologies are a central building
block of the Semantic Web. Ontologies define domain concepts and the relationships between
them, and thus provide a domain language that is meaningful to both humans and machines. In
this paper, the analysis and design of a prototype ontology for visualization are discussed, whose
purpose is to provide the semantics for the discovery of visualization services, support
collaborative work, curation, and underpinning visualization research and education. Relevant
taxonomies are also reviewed and a formal framework is developed.
1.Introduction
systems
Visualization is a powerful tool for analyzing
developing Grid enabled visualization services
data and presenting results across a wide range
has focused on resource discovery and remote
of disciplines. Grid computing offers many new
rendering. The Resource Aware Visualization
opportunity
for
Environment
associated
with
visualization
the
[19],[20],
present experience of
(RAVE)
allows
adaptation
and
depending on resource availability and load to
orchestration of the required physical resources.
deliver visualization results to end users in a
Perhaps the greatest potential of the Grid with
timely and transparent manner [20]. However,
respect to visualization is its capability to
with this experience, we recognize it is
support
teams
imperative to establish common vocabularies
collaborative visualization
and capture and organise visualization domain
geographically
participating in
marshalling
[20]. Our
separated
efforts [1],[20]. This involves the use of
knowledge
resources needed to produce visualizations, both
machine-to-machine negotiations necessary for
computing (software system, HPC, networking)
next generation Grid enabled visualization
and humans (visualization specialization and end
systems. An ontology defines domain concepts
user domain experts and users) [2]. The
and the relationships between them, and thus
capability to interact with resources that are
provides a domain language that is meaningful
geographically
distributed,
and
allow
the
via
to both humans and machines. An ontology may
also be used to deliver significantly improved
services, remote hosting environments, etc),
(semantic) search and browsing, integration of
allows remote services to be accessed and used
heterogeneous
on demand. Such a computing model assumes
improved analytics and knowledge discovery
that a user is interested in making use of services
capabilities. In this paper we describe the
that are not owned locally and/or cannot be
development of an ontology for visualization.
provisioned at their own local site. The viability
This ontology is designed to
of such a model has already been demonstrated
common vocabulary for: describing visualization
through the use of numerical services [3] and
data; processes; products; and support the
some
description and discovery of Web Services;
Grid-enabled
enabled
inform
Grid/eScience infrastructure (such as registry
emerging
as
to
visualization
information
sources
and
provide a
sharing of process models between visualization
Ontology” held in April 2004, which identified
developers and users; curation and provenance
the
management of visualization processes and data;
visualization, and investigated the structure for
and collaboration and interaction between
such an ontology, giving an overall description
distributed sites offering visualization services
of what such an ontology should contain [5].
[2]. At the same time, Semantic Web Service
However, there was agreement that these initial
technologies, such as the Ontology Web
efforts were both tentative and incomplete
Language (OWL), are developing the means by
because the creation of an ontology is an
which services can be given richer semantic
iterative activity and the establishment of the
specifications. Richer semantics can enable
ontology for visualization needs consensus
more flexible automation of service provision
within the visualization community itself.
and use, and support the construction of more
Brodlie at the University of Leeds proposed the
powerful tools and methodologies. This also
notion of a “scientific visualization taxonomy”
makes it possible for users to define their
using the concept of an “E-notation” [6][7],
requirements and subsequently connect to
which was first developed at the AGOCG
services that may be adopted in their work.
visualization meeting in 1992. This is seen as a
Ontologies are a central building block of the
useful classification and model of the underlying
Semantic Web: they provide formal models of
sampled
domain knowledge that can be exploited by
subsequently visualizing this data, however it
intelligent agents. A visualization ontology
failed to capture details about how such samples
would provide a domain language that is
were distributed, or how the visualization was
meaningful to humans and machines, describes
carried out – both fundamental issues in
the configuration of a visualization system and
visualization.
supports the discovery of available Web
Melanie Tory and Torsten Möller in Simon
Services.
Fraser
We show how a prototype ontology for
significant work on “visualization taxonomies”
visualization may be developed using Protégé. It
[8]. Recently, they presented a novel high-level
is extendable, and as such, our initial efforts can
visualization taxonomy. The taxonomy classifies
be viewed as a starting point and catalyst for
visualization
experts in visualization to create and agree a
Algorithms are categorized based on the
standard ontology. In Section 2, a summary of
assumptions they make about the data being
related work is provided. Section 3 introduces
visualized. As the taxonomy is based on design
the Ontology Web Language and Section 4
models, it is more flexible and considers the
describes a popular ontology editing tool --
user’s conceptual model, emphasizing the human
Protégé. The development of ontology for
aspect of visualization [9].
visualization using Protégé will be discussed in
Ed H. Chi at Xerox Parc put forward a new way
the Section 5. Conclusions and future work are
to
presented in Section 6.
techniques by using the Data State Model
need
to
establish
data
which
University
an
may
have
algorithms
taxonomize
ontology
be
also
rather
information
for
used
for
undertaken
than
data.
visualization
approach [10]. This research shows that the Data
2.Related Work
State
Two workshops have recently been held at the
understand the design space of visualization
UK’s National e-Science Centre (NeSC), one on
algorithms,
but
“Visualization for eScience”, held in January
understand
how
2003 [4] and the other on “Visualization
techniques can be applied more broadly [11].
Model
not
only
also
helps
researchers
helps
implementers
information
visualization
Additionally, in an article describing the design
inconsistencies in an ontology that conforms to
space of information visualization techniques,
OWL-DL.
Card and Mackinlay constructed a data-oriented
in [13]. This taxonomy divides the field of
4.Building OWL Ontology with
Protégé
visualization
subcategories:
An OWL ontology can be regarded as a network
Scientific Visualization, GIS, Multi-dimensional
of classes, properties, and individuals. Classes
Plots, Multi-dimensional Tables, Information
define names of the relevant domain concepts
Landscapes and Spaces, Node and Link, Trees,
and their logical characteristics. Properties
and Text Transforms. OLIVE is a taxonomy
(sometimes also called slots, attributes or roles)
assembled
Shneiderman’s
define the relationships between classes, and
information visualization class [14], and divides
allow the assignment of primitive values to
information visualization techniques into eight
instances. Individuals are instances of the classes
visual data types: temporal, 1D, 2D, 3D, multi-D,
with specific values for the properties. Our
Tree, Network, and Workspace.
visualization ontology (called here “VO” for
taxonomy [12], which is subsequently expanded
into
by
several
students
in
short) is developed using Protégé_3.1_beta with
3.Ontology Web Language (OWL)
OWL-plugin. Protégé is an open platform for
The OWL language is designed for use by
ontology modeling and knowledge acquisition.
applications that need to process the content of
The OWL Plugin [18] can be used with Protégé,
information,
presenting
and enables a user to load and save OWL files in
information to humans. OWL facilitates greater
various formats, to edit OWL ontologies with
machine interpretation of Web content than that
custom-tailored
supported by XML, RDF, and RDF Schema
provide access to reasoning based on description
(RDF-S) by providing additional vocabulary
logic. The OWL Plugin user interface provides
along with a formal semantics. OWL can be
various default tabs for editing OWL classes,
used to explicitly represent the meaning of terms
properties, forms, individuals, and ontology
in vocabularies and define the relationships
metadata.
between those terms. OWL is part of the
As an extension of Protégé, the OWL Plugin has
growing stack of W3C recommendations related
a large and active user community, a library of
to the Semantic Web. Compared with XML,
reusable components, and a flexible architecture.
XML-Schema, RDF, and RDF-Schema, OWL
The OWL Plugin therefore has the potential to
adds
describing
become a standard infrastructure for building
properties and classes: among others, relations
ontology-based Semantic Web applications [16].
instead
additional
of
vocabulary
just
for
graphical
widgets,
and
to
between classes (e.g. disjointedness), cardinality
(e.g. “exactly one”), equality, richer typing of
5.Ontology for visualization
properties, characteristics of properties (e.g.
symmetry), and enumerated classes. OWL has
5.1 The overview of VO
three
sub-languages:
A taxonomy is a good mental starting point for
OWL Lite, OWL DL, and OWL Full [15]. Our
building an ontology. Unfortunately, there has
ontology for visualization is represented in OWL
not been a universally, well-accepted taxonomy
DL-- because OWL DL is much more expressive
for visualization developed as yet. In our opinion,
than OWL-Lite and is based on Description
the ontology for visualization must be a
Logics, it is therefore possible to automatically
compromise between function, understandability
compute a classification hierarchy and check for
and uniformity. As a starting point therefore, we
increasingly-expressive
synthesize some existing taxonomies, mainly
concepts and their relations as possible to
based on Ken Brodlie [6] and Melanie Tory,
provide machine-readable formal specifications
Torsten Möller’s [8] taxonomies, and present
for the discovery of visualization services.
these in an organized structure which highlights
Moreover, some of decisions made in the
Data_Model
chosen for /by
has-component
transform from/by/into
Primitive_Set
has-component
has-component
Data_ Representation
Visualization_
Techniques
used for/by
Figure 1 Relationship between the four main classes in the VO
the
connection
visualization
between
techniques
data
and
models,
process of building VO were not rigorously
data
justified but rather based on the authors’
the
representation. Taking the data model for
intuition
example, firstly, it is categorized into two types:
limitations of the tools currently available.
discrete model and continuous model, and then
The VO consists of classes, properties and
the continuous model is classified according to
individuals. Classes are interpreted as sets that
the type of each variable: scalar, vector, tensor,
contain individuals. They are described using
point or multivariate, and each of which is
formal descriptions that state precisely the
broken
the
requirements for membership of the class.
dimensionality and number of variables, and is
Properties illustrate relationships between two
denoted by the E-notation [6]. The factor of time
individuals. And individuals represent objects in
is neglected here, and the relationship between
the domain that we are interested in.
the
corresponding
The VO has four abstract classes representing
visualization techniques should be considered
the main concepts in the visualization domain:
mainly in the process of category. The discrete
Data_Model,
model is classified into either a connected or
Data_Representation, and Primitive_Set. Their
unconnected model, and the unconnected model
relationship is shown in figure 1.
is
to
Data_Model describes the user’s data model,
dimensionality of the data involved, each of
Visualization_Techniques defines a variety of
which is also denoted by the E-notation. A more
techniques and algorithms used to transform or
detail description is presented in Section 5.3.
visualize the data model, Data_Representation
Our VO is a web-accessible and was envisioned
contains multimodal attributes, which enables a
to be a prototype ontology used to enable
user to chose an appropriate representation,
automatic
whereas Primitive_Set includes the elements
down
data
broken
further
model
down
and
according
the
further
composition
to
according
of
Web-based
visualization services. So we do not attempt to
capture all knowledge about the visualization
domain, rather our main goal is to cover as many
and
sometimes
directed
by
the
Visualization_Techniques,
used as the building block of above classes.
5.2 Primitive Set class
5.3 Data_Model class
The subclasses of Primitive_Set class are the
The hierarchies within the Data_Model class,
basic concepts which might be linked through
which are shown in figure 3, are basically the
has- property with corresponding classes. In a
same as our taxonomy for visualization.
sense they serve as a set of basic concepts upon
However, we can go further with an ontology.
which the ontology is built. Figure 2 shows the
portion of Primitive_Set as corresponding to
blocks for the Data_Model.
Figure 2. The contents of the class Primitive_Set
To create any ontology, it is necessary to provide
concise definitions of the base concepts involved.
Figure 3. The content of Data_Model
It is often the case, however, that some of these
Where: EnSm denotes a (n) scalar entity on a
concepts come from different fields and it is
m-dimensional domain.
better to have them formalized by experts in
EnVm denotes an entity has (n-)vector data on a
these fields. VO may need some fundamental
m-dimensional domain.
mathematical concepts to be included. One way
En_nTm denotes an entity has a (n;n) tensor
to do this is to link the ontology to another
data on a m-dimensional domain(where n=3).
already developed ontology. For example, the
EPm denotes a m-dimensional domain of
Mathematics on the Net [MONET 2004][17]
point-data (where n=1,2).
project is developing OWL-based mathematical
EMulti_var
ontologies using the OpenMath and MathML
multi-dependent variables.
initiatives. However, at the moment, since they
Em
are still being refined, we hope to investigate the
unconnected data on m-dimensional domain.
denotes
denotes
an
entity
an
entity
has
has
discrete
and
option of using them at a later time, so VO
defines some simple mathematical concepts on
We can put more complex and meaningful
its own. But the approach seems to be most
machine understandable knowledge into the
promising and is likely to be used in the future
VO by using stronger semantic connections in
when ontologies from base knowledge domains
the OWL-based ontology: OWL is used to relate
are finalized.
classes
through
relationships
and
simple
superclass/subclass
properties,
and
also
distinguishes
between
necessary
(inherited)
visualized by algorithms included in classes
conditions (superclasses) and necessary and
AnVm and A3V3, which relates this data model
sufficient
to the corresponding algorithm. Meanwhile, it
conditions
(equivalent
classes).
∀ has-DatatypeSet
Furthermore, an OWL-based description allows
inherits restriction condition
users to restrict classes or related classes with
Vector and ∀ has-StatespaceSet Continuous
from its superclass EV, which is shown in figure
description
logic
∀, ∃, ¬,∩,∪, where:
symbols,
such
as
4. Through these restrictions on properties and
∀ means allValuesFrom,
logic statements, our VO can provide Semantic
∃ someValuesFrom,
Web agents with background knowledge about
¬ complementOf,
domain concepts and their relationships. This
∩ intersectionOf,
knowledge can be exploited in various ways, for
∪ unionOf.
example
At the same time, one of the key features of an
functions, i.e. agents can use their ontological
ontology that are described using OWL-DL is
knowledge to match a user data model with the
that they can be processed by a reasoner, such as
available visualization services offering the
RACER in Protégé. This allows checks for
corresponding algorithms [21]. Taking EnS3 as
inconsistencies,
an
hidden
dependencies,
to
drive
context-sensitive
search
example, if the user’s data model is EnS3,
redundancies, and misclassification. Taking
the matchmaking agent can search the services
E3V3 as an example, we define its necessary &
which offer the algorithms, included in the class
sufficient condition as EV, has-Dimension = 3,
AnS3
has-Vector = 3, which mean that E3V3 is the
Marching_Cube or Contour_Connecting and so
subclass of EV, the number of its vector
on, to visualize the data model. All of these can
argument equals 3 and dimension is 3. We can
be done by means of EnS3’s necessary condition
also define that its necessary condition is
∀ is-transformed-by AnS3.
∀ is-transformed-by (AnVm
5.4 Visualization_Techniques class
The subclasses of Visualization_Techniques
∪ A3V3), where
n , m=2,3, which means it can be transformed or
shown
in
figure
Figure 4 The definition of the class E3V3 in Protégé OWL plugin
5,
such
as
define a set of techniques and algorithms which
5.5 Data_ Representation class
can be used to transform or create visual
The reason that Data_Representation class
representations of data using a data model. So
should be included is that some models of
techniques/algorithms are categorized based on
representation are specific to classes of data, for
the data model rather than on the data itself,
example work in flow visualization and graph
which
visualization
facilitates
the
transformation
or
have
distinct
categories
of
visualization of the data model. The relationship
representations. So users should be allowed to
between techniques/algorithms and data model is
make the appropriate representation choice
achieved by three properties: is-transformed-by,
based on their information need. Its subclasses
transform-from and transform-into-model. The
contain multimodal attributes, i.e. visual, haptic,
property transform-from is an inverse property
sonic, olfactory, physical attribute etc. The visual
of is-transformed-by. The former means an
attribute is further split into four subclasses
algorithm
spatialization, timing, color and transparency
can
be
used
to
visualize
the
corresponding data model, the latter the inverse.
[7].
The range of the property transform-into-model
is Visualization_Techniques, which means we
6.Conclusion and future work
can use an algorithm to transform a data model
In this paper, we have described our experience
into a simpler form. It can therefore be seen that
of building an ontology for visualization,
it is possible to match data for specific
although it is accepted that this is both
applications to the most appropriate visualization
incomplete and tentative. Our VO is designed as
techniques/algorithms by means of the above
an OWL-based prototype ontology whose
three properties. A fragment of subclass of
purpose is to provide a vocabulary by which
Visualization_Techniques is shown in figure 5
users
(for example, AS2 denotes that its subclasses
communicate,
and
can be used to transform or visualize the data
matchmaking
portal
model ES2).
visualization services [21]. Several specific
and
visualization
it
systems
is
being
for
used
discovery
can
in
of
directions for future work include:
z
To populate the VO in terms of Operators,
Techniques, Algorithms and (low-level)
Transformation with respect to the Data
Model.
z
To
determine
the
limits
of
the
expressiveness of the VO.
z
To extend the above VO to include a user
model and actions.
z
To extend the above VO to include
temporal aspects/conditions.
z
To determine if the VO can be used to
characterize and describe lower levels of
details such as NAG-EXPLORER meshes
Figure 5. The content of Visualization_Techniques
using the same constructs.
z
To create a system which uses the VO to
automatically
build
ALL
permissible
visualizations given knowledge of the data.
Techniques using the Data State Reference Model.
In Proceedings of the Symposium on Information
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