=Paper=
{{Paper
|id=Vol-1684/paper24
|storemode=property
|title=Ontology Visualization: A Systematic Literature Analysis
|pdfUrl=https://ceur-ws.org/Vol-1684/paper24.pdf
|volume=Vol-1684
|authors=Sergei Mikhailov,Mikhail Petrov,Birger Lantow
|dblpUrl=https://dblp.org/rec/conf/bir/MikhailovPL16
}}
==Ontology Visualization: A Systematic Literature Analysis==
https://ceur-ws.org/Vol-1684/paper24.pdf
Ontology Visualization: a Systematic Literature
Analysis
Sergei Mikhailov1 , Mikhail Petrov1 and Birger Lantow2
1
ITMO University, Saint Petersburg, Russia
2
University of Rostock
Abstract. The aim of this work is summarizing the current state of the
art in the field of ontology visualization. For this purpose the method
of systematic literature analysis has been applied. Publications on and
in ESWC, ISWC, KEOD, AVI, and JUCS have been systematically
searched for research results regarding ontology visualizations. Besides a
general map of activity in the area, especially the context of ontology vi-
sualization and the nature of presented artefacts have been investigated.
Furthermore, topics for future research are derived.
Key words: visualization tools, ontology, visualization, systematic lit-
erature analysis, SLA
1 Introduction
Visualization is an important task related to ontologies. Visualization is mainly
based on a mapping from information to a graphical representation in order to
facilitate data interpretation. It also provides ways to limit the amount of infor-
mation that users receive, while keeping them ”aware” of the total information
space and reducing cognitive effort. For ontology visualization, this rule is also
true. [1]
Visualization of ontologies is needed for showing their content and relations
between their elements. A successfully generated visual representation of an
ontology allows to reduce time spent working with the ontology. Visualization
tools can also provide users an opportunity to create and edit ontologies.
Visualization is a powerful tool in the exploration and analysis of ontolo-
gies. On the other hand, the growing number and size of ontologies requires
sophisticated visualization techniques that are capable to handle algorithmic,
perceptual, and visual scalability problems [2].
This article aims to provide an overview of current state of the art in the
field of ontology visualization. Thus, the central research question (RQ) is: ”How
is the area of ontology visualization covered by current scientific publications?”
For this purpose the ”Structured Literature Analysis” method (SLA) was used
(e.g. [3]). The idea of an SLA is to give a summary of activity in an scientific
area. Thus, identifying major research topics and problems and assessing the
current level of activity.
2 Ontology Visualization: SLA
The structure of this paper is organized as follows. The next section describes
the SLA method and provides a description of the method’s steps. This includes
breaking down the central research question to more specific RQs. Sections 3
and 4 present the results of the SLA. The final section is devoted to conclusions.
2 Study Design and Overview
The following section describes the process of developing a systematic literature
review. Prior to the review process, a topic of interest must be defined. This has
been done by formulating the central research question in the previous section.
Furthermore, research questions regarding this topic of interest need to be for-
mulated. They represent the basis of the study and serve as a guidance during
the data extraction. Regarding the topic of ontology visualization, the following
research questions are in focus:
– RQ1: How much activity in the field of ontology visualization has been in the
years 2007–2015?
– RQ2: What research topics (methods, tools, theories, examples, deployments,
etc.) are being investigated?
– RQ3: Who is active in area of ontology visualization?
– RQ4: What research approaches are being used?
– RQ5: How is the context of ontology visualization approaches described?
– RQ6: Which topics in the field of ontology visualization need further research
according to the authors?
The review process is divided into four different parts (see figure 1). The first
activity is to identify conference series, journals and catalogues that are likely
to represent the state of the art of research on the topic of interest. Here a base
set of papers for review is extracted by keyword search. The second step is the
exclusion/inclusion of papers based on title and abstract. Then, the remaining
papers have to be classified and data has to be extracted with regard to the
research questions. The fourth and last step is to analyse the extracted data.
This review process is based on the guidelines for systematic literature reviews
by Kitchenham [3] .The next paragraphs describe the performance of these steps
in detail.
Fig. 1. Systematic Literature Review Process [4]
Ontology Visualization: SLA 3
3 Identification and Selection of Papers
The first step is the identification of ontology visualization related papers in
a selection of appropriate literature sources. As well-known representatives of
conferences targeting at ontologies and semantic technologies, the International
Semantic Web Conference (ISWC), the European Semantic Web Conference
(ESWC), and the International Conference on Knowledge Engineering and On-
tology Development (KEOD) have been selected. For the visualization domain,
the Workshop on Advanced Visual Interfaces(AVI) has been chosen as a repre-
sentative. Additionally, the Journal of Universal Computer Science (JUCS) has
been searched for the occurrence of the topic in general computer science. For
searching relevant information in the field of ontology visualization and reducing
the number of articles the time-frame from 2007 to 2015 has been selected.
Major scientific data bases have been used for searching. ISWC and ESWC
are indexed at the Web of Science by Thomson/Reuters, KEOD, AVI, and JUCS
at SCOPUS. The following search terms have been used:
SCOPUS:
TITLE-ABS-KEY ( visual* ontolog* ) ) AND PUBYEAR > 2006
Web of Science:
TS=(Ontolog* AND Visual*)
Thus, title, abstract, and keywords have been searched for the occurrence of
words containing ‘ontolog’and words containing ‘visual’. The search terms have
been modified for each selected source in order to restrict results to that source.
For example,
AND ( CONF ( keod ))
has been used in SCOPUS for selecting papers published at KEOD. The search
form for the Web of Science separates the time frame from the search term.
Therefore, the publication years are not part of the used search term. A summary
of paper identification is shown in table 1. 52 papers have been identified in total.
Table 1. Paper Identification and Selection
Source # Identified # Selected Selected Papers
ESWC 13 6 [5, 6, 7, 8, 9, 10]
ISWC 14 5 [11, 12, 13, 14, 15]
KEOD 17 3 [16, 17, 18]
AVI 3 1 [19]
JUCS 5 2 [20, 21]
All 52 17
4 Ontology Visualization: SLA
A selection of the papers which are relevant to the area of ontology visualiza-
tion had to be made. For this purpose criteria for in\excluding were formulated:
– The paper must contain information about ontology visualization or at least
mention some techniques or theories regarding the research area;
– Every paper should give some new information about the topic. Papers about
approaches that have already been discussed in other papers, should be ex-
cluded.
For the selection, a table has been created, which contains the title, the
status (accepted\declined) of a paper and the reason for declining. E.g. ”Hon-
tology: A Multilingual Ontology for the Accommodation Sector in the Tourism
Industry” was declined with the reason that ”No information was found about
visualization”. Applying these criteria, 17 articles have been selected.
4 Data Extraction and Analysis
This section describes results of the data analysis of resulting papers after col-
lection and selection. It is based on the research questions that were given in
section 2. For the data extraction, the selected articles have been read and their
relevant data has been collected in a table for further working. An example of
the data for a paper is presented in table 2.
Fig. 2. Activity in the field of ontology visualization
4.1 RQ1: How much activity in the field of ontology visualization
has been in the years 2007–2015?
Figure 2 shows the activity in the given time period. There is a minimum of
activity from 2007 to 2009 with a local peak in 2008 (2 papers). A peak of
Ontology Visualization: SLA 5
Table 2. Example of data collection
Field Description Example
Title The title of the paper Visualizing Ontologies: A Case Study
Link Reference to the full Link
text
Publication (RQ1) Year of Publication 2011
Topics (RQ2) Keywords and main knowledge representation languages, se-
topics of the paper mantic Web, software tools
Author (RQ3) Authors of the pa- ”Howse, J.; Stapleton, G.; Chapman,
per and their insti- P (Visual Modelling Group, Univ. of
tutes and countries Brighton, Brighton, UK), Taylor, K.
(CSIRO, Australian Nat. Univ., Canberra,
NSW, Australia)”
Research Approaches Type of used research Case study
(RQ4) approach
Context (RQ5) Context may include ”Using ontologies for shared development,
the purpose, the fo- OWL don’t fetch to this task, people
cus, the roles, and used concept diagrams (extended Euler di-
the addressed prob- agrams)”
lems of the visualiza-
tion
Further work (RQ6) What authors plan to ”Implement visualization tool for concept
do in the future diagrams; Translate sketches of concept di-
agrams to symbolic form”
activity was reached in 2010 and 2011— 3 articles were published. After only 1
published article in 2012 an 2013, the number of relevant publications increased
again in the year 2014 and 2015. In average 2 articles were published per year
from 2007 to 2015. Activity in the field of ontology visualization is low (only 18
papers in 9 years were written and published in our dataset.), but stable.
4.2 RQ2: What research topics are being investigated?
The field of ontology visualization contains many topics and themes: e.g. meth-
ods, tools, theories, examples, deployment, case studies, etc. There are many
possible ways to group papers into topics. However, the focus lies on the created
artefact. Thus, 3 clusters have been identified (see fig. 3):
1. Visualization tools — this topic describes creating new visualization tools.
This usually includes creating algorithms\methods, which provide visualiza-
tion. Examples of this kind of papers are: [9, 19, 6]; There are also some arti-
cles ([7, 5]) that do not describe stand-alone ontology visualization tools but
plug-ins for existing ontology editing and visualization applications. Con-
cretely, Prótége 1 was chosen as base application, because this project is
1
http://protege.stanford.edu/
6 Ontology Visualization: SLA
widely used in the ontology community and provides users an efficient sys-
tem for implementing own plug-ins, which significantly increase Prótége’s
functionality.
2. Comparative Study — this kind of article describes methods, tools and
techniques of ontology visualization and makes detailed comparisons with
each other. Examples: [13, 20].
3. Visualization Approach — these papers offer something new in the field of
ontology visualization. E.g. using extended Euler diagrams for data visualiza-
tion ([11]) or using UML Activity diagrams for identical purposes [17, 11, 16].
Fig. 3. Research topics
4.3 RQ3: Who is active in area of ontology visualization?
This question aims to highlight scientific groups or individuals who work in
the field of ontology visualization. Most of the active institutes\universities are
located in Europe — 19 of 26 different universities are involved in ontology visu-
alization research. The most active countries in the field of ontology visualization
are:
– UK (papers: [11, 12, 17, 14, 15], total amount of authors: 15);
– USA (papers: [13, 7, 14], total amount of authors: 7);
– Germany (papers: [5, 15], total amount of authors: 4);
– Latvia (paper: [9], total amount of authors: 5);
– Belgium (paper: [19], total amount of authors: 4);
– Greece (paper: [6], total amount of authors: 4).
Ontology Visualization: SLA 7
The average number of authors, who worked on the articles is 3.65 persons
per article. Most of papers (9) were written by teams from within one country.
None of the authors of the selected papers created more than one paper. Also,
only one university (Stanford University) published papers from different au-
thors. Summarizing these facts, it seems, that working in the field of ontology
visualization is complicated and consumes a lot of time.
4.4 RQ4: What research approaches are being used?
For a complete analysis of the field of ontology visualization it is necessary to
gather the research approaches chosen by the respective authors. The classifica-
tion of research approaches is based on the work of Robson [22]. 3 main groups
have been identified in the selected papers: Case study, Prototyping, and Survey.
Additionally, one of the papers is based on Grounded Theory. The distribution of
group members is shown in fig. 4. As expected from RQ2 — most of the papers
belong to the Prototyping group, because most papers describe new visualization
tools and plug-ins.
The apers in the prototyping group have content about creating, developing,
and evaluation of visualization tools or plug-ins. Also, the papers describe main
features of visualization tools. An example of this kind of paper is: [5].
The Case study group includes papers, which offer some specific case and pro-
vide a detailed description of this case. For example, paper [11] shows how to use
concept diagrams (extended Eulers diagrams) on detailed examples. Some pa-
pers, that are included in the prototyping group, also use the case study method.
E.g. paper [18] describes how to use the visualization of ontologies for helping
a military commander to make decisions or paper [10] describes how to use
ontology visualization in ornithology.
The last group — Survey — contains papers that used data from question-
naires, interviews and observation for research. E.g. paper [13] used data from
surveys and questionnaires for deciding which way of ontology visualization is
better: indented tree or graph visualization. Also, some papers, which describe
prototypes additionally use this research approach. E.g. paper [12] used experts
for evaluating task performance of the presented visualization tool (survey), and
also used Grounded Theory in order to build categories for expert’s comments.
Overall, some of the papers used triangulation (a method mix) in order to im-
prove validity. Research on ontology visualization is mainly tool driven. Thus,
research methods that support artefact evaluation prevail (Prototyping, Case
Study,Expert Survey).
4.5 RQ5: How is the context of ontology visualization approaches
described?
This question aims to highlight the context in which the approaches and tools are
intended to be applied. The following aspects of context have been selected for
investigation: (1) User Groups (2) Application Domains (3) Data Representation
(4) Visualization Techniques and (5) Problems and Issues.
8 Ontology Visualization: SLA
Fig. 4. Research approaches distribution
1. User groups — Tools for visualization of ontologies can be designed for the
use by domain experts, who aren’t familiar with ontologies and for ontology
engineers, who work in this field. The choice of targeted end users has an
significant impact on tool’s design and features and usually it is hard to
combine both user’s way of working.
The papers [5, 6, 19] describe tools for users, who are not familiar with
ontologies, and the papers [12, 7, 8] are positioned as tools for experts in
field of ontology engineering.
2. Application Domains — Visualization of ontologies can be applied in var-
ious domains. Papers present cases of using ontology visualization in research
and education [16], shared development [11], military [18], medicine [15], bi-
ology science [10].Thus, 5 papers discussed domain specific visualizations of
ontologies [16, 9, 10, 14, 15] while the rest provided general approaches.
3. Data Representation — In most cases visualization tools of ontologies
work with RDF and OWL data. But some authors offers other data struc-
tures. E.g., paper [17] works with UML Activity diagrams which could be
converted into OWL ontologies. The authors of paper [7] work with OWL
extended by SWRL (Semantic Web Rule Language) rules. Hence, 7 papers
addressed the visualization of OWL ontologies while 6 considered RDF/S.
Among all the presented tools for ontology visualization only 8 were able to
address A-Box data. A special example is [7], it presents a tool for SWRL
rule visualization. Thus, an additional ontology part is in focus here.
4. Visualization Techniques — Data can be presented in different ways.
Choosing a method of visualization has effects on quality and understand-
ability of models. It is required to choose right view for various purposes.
The most common way of ontology visualization is using plain 2D graphs.
This form of ontology data presentation is familiar and intuitive to users.
Papers [12, 7, 6, 5, 18] use this method for data visualization. But other
authors of the selected papers used other or more specialized solutions:
Ontology Visualization: SLA 9
– 3-dimensional graph visualization layout was used by the authors of pa-
per [14] ;
– OWL classes in paper [9] were presented as UML classes, data properties
as class attributes, object properties as associations, individuals as objects,
cardinality restrictions on association domain class as UML cardinalities;
– The authors of article [8] have adopted the single-view visualizing paradigm
enabling selective detailed views which has turned out to be adequate for
visualizing concept and property hierarchies of a large amount of data in
ontologies;
– Radial tree was used in paper [19] for representing all available OWL
classes of DBpedia and various kinds of charts were chosen for showing
information extracted from DBpedia;
– Concept diagrams (extended Euler diagrams) were used for allowing dis-
parate groups of ontology developers and users to communicate effectively
by the authors of paper [11].
5. Issues and problems — The authors describe different problems, which
they faced during their research. One of the issues is making tools more
friendly for end users. Articles said, that ”ontologies are no longer devel-
oped and used exclusively by specialized researchers and practitioners” (pa-
per [12]), ”visualizations for ontologies have been developed in the last couple
of years, they either focus on specific ontology aspects or are hard to read for
non-expert use” (paper [5]). This issue has been tried to solve by adding such
capabilities as zooming and hiding parts of the ontology, history browsing,
saving and loading of customized ontologies views, graphical zooming, lay-
out customization (paper [12]); colour schema for elements, using force-direct
layout, layout animation (paper [5]).
Another issue addressed by the authors is working with large data ontolo-
gies. ”Little has yet been done to support ontology users or developers to
visually edit or explore such large volumes of interrelated individuals” (pa-
per [8]), ”ontologies can encapsulate a large amount of information (hundreds
of thousands of classes and relationships, for example). The main problems
of current tools for ontology visualization are common to any tool for graph
visualization: problems of scale versus amount of information that need to
be presented.” (paper [1]).
4.6 RQ6: What are Future Research Topics?
The papers that presented visualization tools generally named adding function-
ality, proving scalability, and tool integration into ontology engineering environ-
ments as future tasks. Those who presented visualization approaches named the
development of tools as the next step. However, most of the identified papers
stress the fact of lacking evaluation of approaches and tools. Evaluation so far is
only based on small samples, uses cases and specific domains. Thus, a general-
ization and the practical usage in research or industry scenarios are also topics
for future research.
10 Ontology Visualization: SLA
At last, two papers named virtual reality applications as a field for future
research [18, 14].
5 Conclusion
There is constant activity on the field of ontology visualization. This fact proves
that this topic is of interest today and it may evolve in the near future. Many
tools and methods for ontology visualization appear in the literature. Most of
the analyzed papers focus on concrete tools, but not on the algorithms or usage
of ontologies and ontology visualization.
The authors of the selected papers have pointed on the next topics that need
further research:
– Further development, improvement and integration into enterprise of visual-
ization tools (papers [12, 5]);
– More functionality for user-friendly interface (paper [19]);
– Scalability (papers [6, 8, 15]).
The fact that no second publications on the area have been found for the authors
of the selected papers leads to the assumption that these issues for future research
are still open and difficult to handle.
Regarding the quality of the performed SLA, publication sources of the se-
mantic web community (ESCW, ISWC, KEOD) are over-represented. Thus, the
literature search can be extended for example by looking into the visualization
community. Furthermore, the search terms could be extended. A possible addi-
tional term would be ‘Conceptual Model’. Though Ontologies and Conceptual
Models are not the same, they are quite similar.
Acknowledgement
This work has been partially supported by the Government of the Russian Fed-
eration, Grant 074-U01.
References
1. da Silva, Isabel Cristina Siqueira, Freitas, Carla Maria Dal Sasso, Santucci, G.: An
integrated approach for evaluating the visualization of intensional and extensional
levels of ontologies. In: Proceedings of the 2012 BELIV Workshop: Beyond Time
and Errors-Novel Evaluation Methods for Visualization. (2012) 2
2. Burch, M., Lohmann, S.: Visualizing the evolution of ontologies: A dynamic graph
perspective. In: Proceedings of the International Workshop on Visualizations and
User Interfaces for Ontologies and Linked Data (VOILA’15). Volume 1456. 69–76
3. Kitchenham, B.: Procedures for performing systematic reviews. Keele, UK, Keele
University 33(2004) (2004) 1–26
Ontology Visualization: SLA 11
4. Ivarsson, M., Gorschek, T.: Technology transfer decision support in requirements
engineering research: a systematic review of REj. Requirements engineering 14(3)
(2009) 155–175
5. Lohmann, S., Negru, S., Bold, D.: The ProtegeVOWL Plugin: Ontology visual-
ization for everyone. The Semantic Web: ESWC 2014 Satellite Events. Revised
Selected Papers: LNCS 8798. (2014)
6. Zampetakis, S., Tzitzikas, Y., Leonidis, A., Kotzinos, D.: StarLion: Auto-
configurable Layouts for Exploring Ontologies. The Semantic Web: Research and
Applications. 7th Extended Semantic Web Conference, ESWC 2010. Proceedings,
Part II (2010) 446–450
7. Hassanpour, S., O’Connor, M.J., Das, A.K.: A Software Tool for Visualizing, Man-
aging and Eliciting SWRL Rules. The Semantic Web: Research and Applications.
Proceedings 7th Extended Semantic Web Conference, ESWC 2010 (2010) 381–385
8. Noppens, O., Liebig, T.: Understanding large volumes of interconnected individuals
by visual exploration. System description. Semantic Web: Research and Applica-
tions. Proceedings 4th European Semantic Web Conference, ESWC 2007 (2007)
799–808
9. Cerans, K., Liepins, R., Sprogis, A., Ovcinnikova, J., Barzdins, G.: Domain-Specific
OWL Ontology Visualization with OWLGrEd. The Semantic Web: ESWC 2012
Satellite Events. Revised Selected Papers: LNCS 7540 (2015) 419–424
10. Koho, M., Hyvonen, E., Lehikoinen, A.: Ornithology based on linking bird ob-
servations with weather data. The Semantic Web: ESWC 2014 Satellite Events.
Revised Selected Papers: LNCS 8798. (2014)
11. Howse, J., Stapleton, G., Taylor, K., Chapman, P.: Visualizing Ontologies: A
Case Study. The Semantic Web. Proceedings 10th International Semantic Web
Conference. (2011)
12. Motta, E., Mulholland, P., Peroni, S., d’Aquin, M., Gomez-Perez, J.M., Mendez,
V., Zablith, F.: A Novel Approach to Visualizing and Navigating Ontologies. The
Semantic Web. Proceedings 10th International Semantic Web Conference. (2011)
13. Bo, F., Noy, N.F., Storey, M.A.: Indented Tree or Graph? A Usability Study of
Ontology Visualization Techniques in the Context of Class Mapping Evaluation.
The Semantic Web - ISWC 2013. 12th International Semantic Web Conference.
Proceedings: LNCS 8218. (2013)
14. Halpin, H., Zielinski, D.J., Brady, R., Kelly, G.: Exploring semantic social net-
works using virtual reality. The Semantic Web - ISWC 2008. Proceedings 7th
International Semantic Web Conference, ISWC 2008. (2008)
15. Hauer, T., Rogulin, D., Zillner, S., Branson, A., Shamdasani, J., Tsymbal, A.,
Huber, M., Solomonides, T., McClatchey, R.: An architecture for semantic naviga-
tion and reasoning with patient data - experiences of the Health-e-Child project.
The Semantic Web - ISWC 2008. Proceedings 7th International Semantic Web
Conference, ISWC 2008. (2008)
16. Gavrilova, T.A., Leshcheva, I.A.: Cognitive style affecting visual ontology design:
KOMET project results. KEOD 2014 - Proceedings of the International Conference
on Knowledge Engineering and Ontology Development (2014)
17. Olszewska, J.I.: UML activity diagrams for OWL ontology building. IC3K 2015
- Proceedings of the 7th International Joint Conference on Knowledge Discovery,
Knowledge Engineering and Knowledge Management 2 (2015)
18. Hodicky, J., Frantis, P.: Decision support system for a commander at the opera-
tional level. KEOD 2009 - 1st International Conference on Knowledge Engineering
and Ontology Development, Proceedings (2009)
12 Ontology Visualization: SLA
19. Dumas, B., Broché, T., Hoste, L., Signer, B.: ViDaX: An interactive semantic
data visualisation and exploration tool. Proceedings of the Workshop on Advanced
Visual Interfaces AVI (2012)
20. M. Lanzenberger, J. Sampson, M. Rester: Ontology Visualization: Tools and Tec-
niques for Visual representation of Semi-Structrured Meta-Data. In: JUCS. Vol-
ume 16. 1036–1054
21. Garcı́a, J., Garcı́a-Peñalvo, F.J., Therón, R., de Pablos, P.O.: Usability evaluation
of a visual modelling tool for OWL ontologies. Journal of Universal Computer
Science 17(9) (2011) 1299–1313
22. Robson, C.: Real World Research: A resource for social scientists and resources
(2002)