=Paper=
{{Paper
|id=Vol-1963/paper565
|storemode=property
|title=OntoQAV: A Pipeline for Visualising Ontology Quality
|pdfUrl=https://ceur-ws.org/Vol-1963/paper565.pdf
|volume=Vol-1963
|authors=Silvio Mc Gurk,Jeremy Debattista,Charlie Abela
|dblpUrl=https://dblp.org/rec/conf/semweb/GurkDA17
}}
==OntoQAV: A Pipeline for Visualising Ontology Quality==
https://ceur-ws.org/Vol-1963/paper565.pdf
OntoQAV: A Pipeline for Visualising Ontology
Quality
Silvio Mc Gurk1 , Jeremy Debattista2? , and Charlie Abela1
1
University of Malta,
silvio.mcgurk.15@um.edu.mt, charlie.abela@um.edu.mt
2
ADAPT Centre, School of Computer Science and Statistics,
Trinity College Dublin, Ireland
debattij@scss.tcd.ie
Abstract. Ontological Quality has been the subject of extensive re-
search due to the importance of ensuring that a system’s underlying
ontologies are fit for purpose. Understanding quality problems may not
be straightforward, depending on the ontologies’ magnitude and com-
plexity, the nature and extent of the problems, and the metrics used
in its quality assessment. In this poster paper we present an innovative
pipeline3 linking together a quality assessment framework (Luzzu) and
an ontology visualisation framework (WebVOWL) in order to establish
an ecosystem whereby knowledge engineers can assess and interactively
understand quality problems within concepts and properties in ontolo-
gies.
Keywords: Ontology Quality, Quality Framework, Quality Assessment,
Quality Visualisation, Ontology Visualisation
1 Introduction and Context
The use of ontologies has become widespread across many domains (e.g. bio-
logical, geographical, government, etc. . .) as they provide the means for sharing
concepts and data among different organisations [5]. Together with the RDF
standard, the idea behind ontologies was to solve data interoperability prob-
lems. However, choosing a fit-for-use ontology for a system might not be the
simplest task. Research carried out by [4], amongst others, resulted in a num-
ber of metrics being proposed to help identify quality problems. Nonetheless,
without the right tools, ontology stakeholders still encounter difficulties when
choosing the right ontology for the task at hand.
Inspired by the progress achieved on Linked Data quality frameworks [1, 4] and
ontology visualisation frameworks [2], we aim to exploit these state-of-the-art
frameworks in order to address a missing niche in ontology quality, that is,
?
This research has received funding from the ADAPT Centre for Digital Con-
tent Technology, funded under the SFI Research Centres Programme (Grant
13/RC/2106) and co-funded by the European Regional Development Fund.
3
Demonstration: http://github.com/silviomcgurk/OntoQAV
assisting system engineers in finding the fit-for-use ontology. In this poster
paper we present OntoQAV, a pipeline integrating Luzzu [1], a generic Linked
Data quality assessment framework, and WebVOWL [2], a widely used tool
to represent RDF-based ontologies in a visual format. The rational behind
this pipeline is to create an ecosystem with the aim of allowing stakeholders
to assess ontologies on various quality metrics and to visualise any identified
problems. We aim to provide the possibility of assessing multiple ontologies at
once and presenting a comparative visualisation and summary of the quality
problems within the ontologies being assessed. The main contributions of this
pipeline are: (1) implementing ontology quality metrics for Luzzu identified in
our previous work [3]; and (2) implementing a plug-in for WebVOWL which
takes as input a quality problem report from Luzzu and displays the problems
in WebVOWL. Metrics for Luzzu and the WebVOWL plug-in are available in a
public repository, along with instructions and links for a demonstration of the
pipeline (available at http://github.com/silviomcgurk/OntoQAV).
2 The OntoQAV Pipeline
Figure 1 depicts OntoQAV. The pipeline follows a three-step workflow. In the
first step (Step 1), a user selects the ontology that needs to be assessed and the
relevant quality metrics that represent fitness for use. Following that, (Step 2)
the Luzzu quality assessment framework is initialised and the assessment gets
underway [1]. The assessment provides the ontology engineer with two results:
(1) the quality metadata, which is preserved on the Web of Data for further
quality-based tasks (e.g. filtering ontologies based on different quality criteria);
and (2) the quality problem report, which is preserved on the Web as Linked
Data for future use (possibly by other interested parties). The quality problem
report is then converted to JSON-LD format and fed to WebVOWL, the third
step (Step 3).
Therefore, the result is an augmented visualisation which gives a graphical rep-
resentation of the nodes and links within the ontology, with an additional visu-
alisation layer of the problems identified by the quality assessment framework.
3 Visualising Ontology Quality Problems
Luzzu [1] and WebVOWL [2] are the two tools chosen in our proposed pipeline
to demonstrate a proof-of-concept for visualising ontology quality. Furthermore,
in the future, we aim to make this pipeline generic, implementing and making
use of mechanisms and vocabularies that facilitates the data exchange between
existing and future tools. Luzzu is an extensible Linked Data quality assessment
framework. It also provides quality metadata and problem reports that can be
leveraged in semantic-driven frameworks for other tasks. WebVOWL is an on-
tology visualisation tool, representing concepts and properties of ontologies in a
Fig. 1: Proposed OntoQAV Pipeline
way that can be easily understood. Upon completion of the quality assessment
for a given ontology, Luzzu provides a Linked Data structured problem report
(cf. Listing 1.1 for a snippet) highlighting problematic concepts and properties
for each assessed metric. This problem report is then converted into a JSON-LD
serialisation and used during the modification of the WebVOWL DOM objects
to represent the problematic concepts.
ex : QualityProblem a qpro : QualityProblem ;
qpro : i s D e s c r i b e d B y ex : C y c l e s I n O n t o l o g y M e t r i c ;
qpro : p r o b l e m a t i c T h i n g [
a r d f : Statement ;
r d f : o b j e c t ex : P i z z a ;
r d f : p r e d i c a t e ex : s u b C l a s s O f ;
r d f : s u b j e c t ex : NamedPizza ] .
Listing 1.1: Snippet from Luzzu Problem Report (Turtle)
Together with the JSON-LD serialisation of the problem report, the assessed
ontology is loaded into WebVOWL for its visualisation. Following the loading of
the ontology, the user is given a choice to view the problematic concepts, upon
which the proposed rendering of quality visualisation is triggered. At this stage,
the pipeline plug-in interacts with the rendered visualisation of the ontology and
modifies it through the browser’s Document Object Model (DOM) to augment
the visualisation with quality information. Shading of the red colour has been se-
lected to represent quality issues within an ontology. For every problem identified
by Luzzu, the plug-in gives a red shade to the problematic components (nodes,
properties or relationships). Components that fail more than once (with different
metrics) will have a darker shade. As a result, the shading of red from light to
dark colour indicates the extent of possible quality issues of the respective com-
ponent. Additional information regarding the quality problems of concepts and
properties of the visualised ontology are shown to the user in the WebVOWL
sidebar. Figure 2a shows the visualisation of the pizza ontology rendered in We-
bVOWL. Once the quality visualisation plug-in is invoked, problematic concepts
are highlighted in red (cf. Figure 2b). In this example, the pizza ontology has
been assessed by the Cycles in Ontology Metric [3]. The assessment identified the
concepts American, NamedPizza, and Pizza and properties rdfs:subClassOf
violating this metric, and thus are highlighted in red.
(a) Ontology Visualisation in Web- (b) Augmented Quality Layer and Side-
VOWL bar Quality Information
Fig. 2: Visualisation
4 Final Remarks and Future Work
In this poster paper we proposed an innovative pipeline that links together a
quality assessment framework and visualisation tool in effort to establish an
ecosystem to enhance the evaluation of ontologies from a quality perspective,
providing an intuitive way of looking at various quality problems an ontology
might have. Our future work includes a plan to do a comprehensive quality
assessment on LOV4 ontologies and make available their respective quality as-
sessment, quality problem reports and augmented visualisation.
References
1. Debattista, J., Auer, S., Lange, C.: Luzzu - A Methodology and Framework for
Linked Data Quality Assessment. Journal of Data and Information Quality. (2016).
2. Lohmann, S., Negru, S., Haag, F., Ertl, T.: Visualizing ontologies with VOWL.
Semantic Web. 7, 399-419 (2016).
3. Mc Gurk S., Abela C., Debattista J.: Towards Ontology Quality Assessment. Joint
proceedings of the MEPDaW 2017 and LDQ 2017. (2017).
4. Poveda-Villaln, M., Gomez-Perez, A., Suarez-Figueroa, M.: OOPS! (OntOlogy Pit-
fall Scanner!):. Intl. Journal on Semantic Web and Information Systems. (2014).
5. Ristoski, P., Paulheim, H.: Semantic Web in data mining and knowledge discov-
ery: A comprehensive survey. Web Semantics: Science, Services and Agents on the
World Wide Web. 36, 1-22 (2016).
4
http://lov.okfn.org/dataset/lov/