=Paper=
{{Paper
|id=Vol-2262/ekaw-demo-16
|storemode=property
|title=BioOntoVis: An Ontology Visualization Tool
|pdfUrl=https://ceur-ws.org/Vol-2262/ekaw-demo-16.pdf
|volume=Vol-2262
|authors=Nassira Achich,Alsayed Algergawy,Bassem Bouaziz,Birgitta König-Ries
|dblpUrl=https://dblp.org/rec/conf/ekaw/AchichABK18
}}
==BioOntoVis: An Ontology Visualization Tool==
https://ceur-ws.org/Vol-2262/ekaw-demo-16.pdf
BioOntoVis: An Ontology Visualization Tool
Nassira Achich2 , Alsayed Algergawy1 , Bassem Bouaziz2 and Birgitta
König-Ries1
1
Institute for Computer Science, Friedrich Schiller University, Jena, Germany
2
Higher Institute of Computer Science and Multimedia, University of Sfax, Tunisia
firstname.lastname@uni-jena.de
Abstract. As the main way for knowledge representation for the pur-
pose of machine understanding, ontologies are widely used in different
application domains. This requires more and more domain specific infor-
mation to be inserted into ontologies, making them harder to be easily
understood by a human and there is a growing need to develop ontology
visualization tools. However, most of existing tools focus on either a spe-
cific user requirement or ontology-specific features. To this end, in this
demo, we introduce a new generic and user-friendly ontology visualiza-
tion tool, called BioOntoVis, for visualizing and editing ontologies. We
present the general architecture of the tool focusing on the web-based
user interface and different ontology visualization schemes. Through the
demonstration of BioOntoVis, we introduce the tool’s capabilities and
highlight its effectiveness and usability.
Keywords: Semantic Web; Ontology visualization; Ontology editing
1 Introduction
Ontologies are the basic components of the Semantic Web, where underlying
data are well structured for the purpose of full machine understanding. An
ontology consists of a set of concepts (classes), a set of attributes (data type
properties), relationships (object properties), and constraints to abstractly rep-
resent a specific domain. An important aspect is how to facilitate the process
of design, management, and exploration of complex structures. This results in a
growing need for ontology visualization tools that simplify the user involvement
in different ontology-related management processes [3].
Furthermore, and as a main way of knowledge representation, ontologies have
been becoming more and more widely used in different application domains.
Therefore, there exist a large number of ontologies, some of them containing hun-
dreds of thousands of concepts or more. For example, BioPortal 3 contains 737
ontologies with 9,604,539 classes (with an average 13,157 classes per ontology).
The arising number of developed ontologies as well as the number of concepts
each ontology has make ontology visualization a difficult task. Even though, a
3
http://bioportal.bioontology.org/ (access on October 25, 2018)
number of ontology visualization approaches have been developed [2,3,4,5], most
of the existing ontology visualization tools either cannot cope with visualizing
big ontologies or with providing multiple views depending on ontology features
and based on user requirements.
To this end, in this paper, we introduce a new ontology visualization tool,
called BioOntoVis 4 , which is based on a combination of visualization techniques
to reflect the mentioned challenges. The proposed tool follows three main steps,
including: (i) reading and parsing the input ontology, (ii) a set of processing
aspects to make the ontology suitable for visualization, and (iii) finally, the
suitable visual representation is selected to visualize the ontology. The proposed
BioOntoVis tool supports node-link and tree, zoomable, and 3D information
landscape [3]. Furthermore, the tool also provides the possibility to edit the
visualized ontology by adding/removing one or more ontology entities checking
the consistency of the modified ontology.
2 BioOntoVis: The tool overview
To deal with visualizing ontologies meeting different application domains as
well as variant user requirements, we present a new ontology visualization tool,
BioOntoVis, introducing a high-level architecture for a generic pipeline for on-
tology visualization. The pipeline has three main steps: parsing, processing, and
visual representation.
Parsing. An ontology O can be represented as a set of entities, such that an
entity is either a concept (class), a relation, or an instance (individual). The set
of concepts and the set of relations are disjoint, where there is a concept hier-
archy between the set of concepts, and there is also a relation hierarchy among
relations. Before visualizing an ontology, a parsing step is needed to extract the
ontology entities and transform them into a JSON format to be graphically rep-
resented. We select working with the OWL API 5 to deal with ontologies with
different formats. The appropriate parser is automatically selected at runtime
when an ontology is loaded.
Processing. This step focuses on the cognitive features included in the visu-
alization tool aiming to strengthen the user’s support. It includes both basic and
advanced functionalities. The basic functionalities include: i) colors: we define a
color scheme for a better distinction between several ontology entities. For exam-
ple in the fisheye view, we choose a dark-blue scheme for super-classes, light-blue
for sub-classes, orange for the properties and a pink scheme for individuals. This
color scheme is clearly very helpful in identifying the ontology elements varia-
tions, ii) shapes: BioOntoVis provides various views with various node shapes.
This option gives several choices to the user to visualize ontologies. In the net-
work view as an example, we used a rectangle shape, however in the fisheye
view we used a circle shape, iii) zooming: we use two different zooming types:
4
This work was originally motivated by the need to visualize ontologies from the bio
domain, hence the name.
5
http://owlcs.github.io/owlapi/
2
geometric and semantics. The geometric zoom allows the user to explore certain
ontology entities in detail when she zooms-in analyzing the global structure of
the ontology once she zooms-out. The semantics zoom offers the possibility to see
details of a specific entity by zooming, and iv) details; an ontology contains a
large amount of information and description (metadata), including entity name,
label, types, and relations’ types, ect. To this end, BioOntoVis supports illus-
trating these information. It contains a circular attractive menu, shows general
details about the ontology, such as the metadata, the ontology format and the
number of concepts, properties and individuals.
(a) Tree view (b) Fisheye view
Fig. 1: Different visualization views
Advanced functionalities the proposed tool supports include: i) search: find-
ing an ontology entity by visually scanning the ontology graph is a very difficult
process, especially in the context of big ontologies. Therefore, the current imple-
mentation of the tool contains a search window to quickly find particular entities
or other elements by their names/labels. In the tree interface, once a user tapes
in the window, a list containing the set of relevant entities appears to facilitate
the search process. We then visualize the searched entity with a different color.
ii) editing operators: ontologies posses a very complex structure and editing
its entities is a hard task. This is due to the fact that each edit process has
to check the correctness and consistency of the modified ontology. Therefore,
most of existing ontology visualization tools lack the advanced functionalities
that are important for the user to interact more with the displayed ontology. For
example, ontology modularization partitions an ontology into a set of partitions
(modules) [1]. To validate the modularization result, an ontology visualization
tool with editing capabilities is required. It should be noted that any edit op-
eration can violate the semantic and structure definitions of ontology’entities,
thus changing the status of the ontology from consistence into inconsistence.
Therefore, the tool asks the user to check before these changes will be applied.
Visual representation. To cover a wide range of ontologies, BioOntoVis
provides three views of visual representations, which are: tree, network, and fish-
eye view. As shown in Fig. 1a, the tree view representation is a hierarchical
node-link view, in which the ontology is represented as a set of interconnected
nodes in different levels. In this view, the user can extend or compress the tree,
by displaying or hiding children nodes allowing the user to control the depth
3
of the tree. In the network view, the nodes have rectangle shapes and are con-
nected to each other through edges. In this view, colors have their meanings to
facilitate distinguishing between ontology entities. Moreover, a detailed box ap-
pears once a node is selected to give detailed information about the entity, and
its relationships. In large ontology visualization, links between concepts would
be drawn crossing and overlapping each others, which cause a very messy and
potentially unusable view of the ontology graph. Therefore, we implemented the
fisheye view in the tool to deal with this problem. This view demonstrates the
selected node surrounded by the ones that are related to it, hiding all the others,
as shown in Fig. 1b.
3 Demonstration Scenarios
In this demonstration, we will start by presenting the different features of BioOn-
toVis such as various capabilities of the tool to visualize different ontologies using
different views, such as tree, network, or fisheye based on the characteristics of
the ontology, as shown in Figs. 1a and 1b. The demonstration will consist of two
main parts. First, we would like the user to appreciate the importance of the vi-
sualization phase. Second, we present different operations that can be employed
on the displayed ontology. To this end, the BioOntoVis tool is provided with
a nice web interface6 , where the user can upload her ontology and then select
which visualization view to show the ontology. Through this web interface, the
user can get some metadata about the ontology, such as format, the number of
concepts, the number of properties, etc. Later, the user can adjust the view to
fit her needs.
4 Acknowledgments
A part of this research was supported by DAAD funding through the BioDialog
project. A. Algergawy’ work is partly funded by DFG in the scope of CRC 1067
AquaDiva.
References
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analysis and partitioning. In EDBT, pages 644–647, 2016.
2. Z. Dragisic, V. Ivanova, P. Lambrix, D. Faria, E. Jiménez-Ruiz, and C. Pesquita.
User validation in ontology alignment. In 15th ISWC 2016, pages 200–217, 2016.
3. A. Katifori, C. Halatsis, G. Lepouras, C. Vassilakis, and E. G. Giannopoulou. On-
tology visualization methods - a survey. ACM Comput. Surv., 39(4):10, 2007.
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5. S. Negru and S. Lohmann. A visual notation for the integrated representation of
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6
A beta version of the tool: http://bioontovis.uni-jena.de:8080/BioOntoVis-V1.
0-1
4