=Paper=
{{Paper
|id=Vol-258/paper-37
|storemode=property
|title=Ontological Context Visualization
|pdfUrl=https://ceur-ws.org/Vol-258/paper37.pdf
|volume=Vol-258
|dblpUrl=https://dblp.org/rec/conf/owled/DmitrievaBV07
}}
==Ontological Context Visualization==
https://ceur-ws.org/Vol-258/paper37.pdf
Ontological Context Visualization
Julia Dmitrieva, Yun Bei, and Fons J. Verbeek
LIACS, Universiteit Leiden,
Niels Bohrweg 1, 2333 CA Leiden, The Netherlands
{jdmitrie,ybei,fverbeek}@liacs.nl
http://bio-imaging.liacs.nl
Abstract. Ontologies contain information about concepts and their re-
lations. Certain concepts may occur in different ontologies at the same
time and these concepts can be used as glue to integrate information
from related domains. In support of our efforts to represent information
on interesting concepts, we have developed a visualization engine ”Con-
textVis”. Meanwhile, this tool can also be used for a visual knowledge
mining and inspection of ontology integration. In this paper we present
the Ontology Context Visualization with a case study on Alzheimer dis-
ease; a typical example in which contextual information on concepts
allows to collect knowledge from different resources.
Key words: Ontology Visualization, OWL, Description Logic, Ontol-
ogy Integration
1 Introduction
Formally, ontologies existed as controlled vocabularies shared by domain experts
and focused on describing the concepts and relations. In the life sciences much
effort is put in accumulating all existing knowledge and creation of a domain
model by means of expressive language, such as OWL-DL [1]. This has resulted
in producing huge knowledge bases, that are difficult to process; operations such
as making SPARQL [2] queries and reasoning [3] requires a lot of computation
time. The motivation for the tool presented here is to provide a straightforward
visualization of context that is created from different ontologies. Such can not
be realized with existing tools since the integration [4] is only possible for those
ontologies that are very much related in their concept vocabularies as well as
in their structures. Moreover, visualization tools are dedicated to visualization
of the just one ontology. In ”ContextVis” tool we use some interesting concept
(e.g. Alzheimer disease) as glue to connect concepts from different ontologies
and to relate knowledge from different domains.
2 Tool description
The process of ontological context visualization consists of two principle parts
(see Fig. 1), i.e.:
2 Ontological Context Visualization
– Knowledge Acquisition. Here we have made use of Jena API [5] and SPARQL [2].
At this point, the ontological context is built. After that the ontological con-
text is saved in special purpose database (MySql [6]).
– Visualization. During the visualization stage the data is retrieved from the
database and represented as a 3D graph. For the implementation of graph
visualization we have used the Java3D API [7].
(a) Architecture of the ”ContextVis” (b) Creation of the Global Ontological
Context
Fig. 1: Architecture and Global Context
3 Knowledge Acquisition
Data about the interesting concept are collected from different ontologies, such as
pathway, mesh, NCI-thesaurus and go. These ontologies are obtained from
the OBO Download Matrix [8]. We are, however, not restricted to just these
knowledge bases. The only constraint is that the ontologies must be represented
in OWL format.
3.1 Global Ontological Context
The process of data acquisition starts with a search of concepts which, in their
definitions, comments or labels, have words that are equal to the query term.
To find these concepts we make use of SPARQL [2] queries in Jena [5]. First,
the NCI-thesaurus ontology is searched for relevant information. All retrieved
concepts are saved in a temporary data structure and later will be used to
trigger a similar query process in other ontologies. Subsequently, mesh ontology
is searched and the concepts that contain the specified query term plus terms
resulting from the search in NCI-thesaurus are collected. Finally, in the similar
fashion, the pathway and go knowledge bases are processed. According to this
strategy, we have obtained a set of concepts from different ontologies (see Fig. 1).
This set is referred to as the global ontological context.
3.2 Local Ontological Context
Around each concept found in different ontologies a local ontological context
is created from its native ontology; this implies that we collect the information
Ontological Context Visualization 3
about related concepts using all kind of relationships, not just subclass/superclass.
For example part of from pathway or Chemical Or Drug Affects Gene Product
from NCI-thesaurus. The context can be created with different level of depth.
Although, the inferred model is preferable in order to get relationships between
concepts, we were not, however, successful in classifying NCI-thesaurus ontol-
ogy, because it is simply too big.
3.3 Database representation
The information about the interesting concept with surrounding global and local
context is transferred to our special purpose database. In this database, initially,
only two tables are used: one for concepts and the other for relations. In order to
be able to go beyond the representation of subclass and superclass relationships
between concepts, a loosened OWL-DL representation of the relation is intro-
duced. In OWL-DL the structure that connects subject with object via some
predicate is written as follows:
In Description Logic [9] this means: P1 v ∃part of.P2 , where P1 is the term
P W 0000024 and P2 is the term P W 0000234. For our application we simplify
this structure and represent it as a graph-triple with subject P W 0000024, object
P W 0000234 and predicate part of . This allows us to see an ontology as a simple
graph. We expect that users would like to expand some of the concepts, and
explore them at a higher level of granularity. This implies that the ontological
context also has to be defined for the global concepts that are surrounding the
query term that we started from (cf. Fig. 2). This can be achieved by extending
our special purpose database with extra tables. For each concept from the global
ontological context we create a concept table and a relation table.
4 Visualization
The ”ContextVis” is based on the Java3D API [7]. We have chosen a 3D instead
of a simple 2D representation because this helps to utilize the space efficiently
as well as to experience the ontological world created from the merging process
in an intuitive way. Relevant visualization functionalities such as zoom, pan and
rotate are made available in the ”ContextVis” engine. Further interaction is
provided through the mouse; i.e., if a concept definition exists in the ontology,
it can be shown by clicking the ”right” mouse button. The nodes for which the
4 Ontological Context Visualization
concept and relation tables are created can be expanded and, at each expansion,
the local context of concept will be represented. An example of the local and
global context visualization is depicted in Fig. 2
(a) Visualization of global ontological (b) Visualization of local ontological context
context created around ”Alzheimer” created around ”Alzheimer’s disease path-
way” from NCI-thesaurus ontology
Fig. 2: Global and Local Context Visualization
5 Conclusion
In this paper we have described our efforts in developing a tool that can visual-
ize the ontological context of some interesting concept. With the ”ContextVis”
tool we can explore how one and the same concept is represented in different
ontologies. This brings about the possibility to connect the knowledge from one
domain with the knowledge from others. All considered, our processing approach
integrates the data around some interesting concept and produces a graph struc-
ture that can be visualized very well. This simple graph representation has the
advantage that we can use graph algorithms for reasoning. We are interested
especially in the searching of paths between the concepts in the graph.
References
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tology mediation, merging and alignment. Semantic Web Technologies. Chapter 6.
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6. MySql: The world’s most popular open source database: http://www.mysql.com/
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8. OBO Download Matrix: http://www.berkeleybop.org/ontologies/
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