=Paper=
{{Paper
|id=Vol-3355/visuonto
|storemode=property
|title=Towards a Visualisation Ontology for Data Analysis in Industrial Applications
|pdfUrl=https://ceur-ws.org/Vol-3355/visuonto.pdf
|volume=Vol-3355
|authors=Zhuoxun Zheng,Baifan Zhou,Ahmet Soylu,Evgeny Kharlamov
|dblpUrl=https://dblp.org/rec/conf/semiim/ZhengZSK22
}}
==Towards a Visualisation Ontology for Data Analysis in Industrial Applications==
https://ceur-ws.org/Vol-3355/visuonto.pdf
Towards a Visualisation Ontology for Data Analysis in
Industrial Applications
Zhuoxun Zheng1,2,* , Baifan Zhou3 , Ahmet Soylu2,3,4 and Evgeny Kharlamov1,3
1
Bosch Center for Artificial Intelligence, Renningen, Germany
2
Department of Computer Science, Oslo Metropolitan University, Oslo, Norway
3
SIRIUS Centre, University of Oslo, Oslo, Norway
4
Department of Computer Science, Norwegian University of Science and Technology
Abstract
Machine learning (ML) approaches have proven their great potential in dealing with heterogeneous and
voluminous data and thus are widespread in industry. To facilitate the presentation of the ML results
and the subsequent discussion on that, visualisation is essential, as it effectively conveys the information
behind the data. However, a standardisation of the knowledge and practice about visualisation is still
lacking in the industry, which sometimes leads to misunderstandings in conveying information and
thus making the discussions on the ML results error-prone. A visualisation ontology which models the
nature and pipeline of visualization tasks are well suited to provide such standardisation. Currently there
are a few studies that discuss partially the modelling of visualisation, however they are less adequate
in depicting the practical procedure of visualisation tasks, which is highly demanded in the industrial
applications. To this end, we present our ongoing work of development of the visualisation ontology in
industrial applications at Bosch. We also discuss applications and evaluation of our ontology.
Keywords
Ontology Engineering, Knowledge Graph, KG Generation, Data Science, Manufacturing
1. Introduction
Data driven methods especially machine learning aim to extract knowledge and insights from
noisy, structured and unstructured data [1, 2, 3], and have been widely applied in industrial
applications to reduce down-times, improve quality monitoring [4, 5, 6, 7], and robot posi-
tioning [8, 9]. Machine learning approaches have proven their great potential in dealing with
heterogeneous and voluminous data, which is common in the industry, and thus greatly con-
tributes to the overall value-chain [10]. After the machine learning approaches, the visualisation
of the results is also of great importance, as the graphical presentation of the results helps to
reach a common understanding and facilitates subsequent discussions among the stakeholders.
However, a formal description of the general knowledge and practical methods about visuali-
sation is still lacking in the industry. This renders the clarity of the representation unguaranteed
SemIIM’22: 1st International Workshop on Semantic Industrial Information Modelling, 30th May 2022, Hersonissos,
Greece, co-located with 19th Extended Semantic Web Conference (ESWC 2022)
*
Corresponding author.
$ zhuoxun.zheng@de.bosch.com (Z. Zheng); baifanz@ifi.uio.no (B. Zhou)
0000-0002-4223-6746 (Z. Zheng); 0000-0003-3698-0541 (B. Zhou); 0000-0001-6034-4137 (A. Soylu);
0000-0003-3247-4166 (E. Kharlamov)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
Figure 1: A visualisation example with its procedure, 1: Canvas determination; 2,3,4: subplot drawing
and makes the subsequent discussions on the machine learning results lacks a common basis. In
this regard, a visualisation ontology is a good method, which, as formal explicit specifications of
shared conceptualisations [11], identifies the general nature and the workflow of visualisation
tasks by defining the concepts in the domain and relationships between those concepts. Besides,
one can easily extend such visualisation ontology by adding individual information on it and
thus have the potential to generate knowledge graphs, which can be able to represent concrete
visualisation tasks. Currently there are a few studies that discuss partially the modelling of visu-
alisation. For instance, the computer science ontology [12, 13] contains the general knowledge
about visualisation, but the concepts of specific visualisation process is not involved. Statistics
ontology [14, 15] enumerates the various visualisation methods, but they insufficiently study
the procedures of the visualisation approaches. In conclusion, the existing relevant ontologies
are less adequate in depicting the practical procedure of visualisation tasks, which is highly
demanded in the Bosch.
To this end, we develop a visualisation ontology and we present our on-going work on this
topic. Our visualisation ontology is continuously evaluated and evolved through the common
use cases at Bosch, a world leader in automotive industry and Internet of Things. Our studies
represent a broad range of visualisation activities. Besides, we align our effort with literature
and common programming libraries (e.g. matplotlib in Python). In addition, we also discuss
applications and evaluation of our ontology.
2. Our Approach
Visualisation Tasks Rather than being able to represent all the visualisation tasks, VisuOnto
we present in this short paper aims to cover most of the visualisation practices in data analysis
projects at Bosch. Therefore the covered visualisation tasks are limited to charts, that are
intended to represent the properties such as the distribution, change, statistical information etc
of numerical data. Specific representation methods can be divided into scatter plot, curve plot,
histogram, heat map, pie chart, etc. The functions of these chart types can overlap, for example
both pie charts and histogram can represent the distribution of a set of data. These types of
representation methods we considered can meet the visualisation needs of most data analysis
projects at Bosch.
In addition to the distinction in representation methods, the charts can be divided into simple
charts and complex charts, A simple chart uses one method to represent one set of data, while a
Figure 2: The workflow of generation of visualisation ontology
complex chart uses multiple methods to represent multiple sets of data.
To complete such a visualisation task, i.e., to produce a figure that meets the task requirements,
the procedure can be formalised and be divided into such steps: (1) create the canvas of the
figure, with the configuration of its name and layout (subplots); (2) in each subplot, represent
the desired data according to the customization, which includes the representation method (line
plot, scatter plot, etc.) and some details (colour, size, etc.). An example of visualisation with its
procedure can be seen in Fig 1.
Three Aspects of Requirements. We now discuss the following aspects of requirements for
VisuOnto.
R1. Coverage: The ontology should be able to cover the aforementioned visualisation tasks.
For the covered types, VisuOnto should be able to formalise all the common features. From
another point of view, all common features of any diagram of the covered types can be identified
by the properties in VisuOnto.
R2. Procedure: Ontologies typically contain taxonomies of classes and sub-classes. We also
emphasise on the inclusion of procedures of visualisation tasks in the modelling. Specifically, our
ontology should also reflect the procedure of build a diagram that meets the task requirements.
After extending the visualisation ontology into a knowledge graph by adding specific individual
information from a concrete visualisation task, one can easily give out the pipeline for that task,
which is one of the applications of VisuOnto and will in introduced later.
R3. Application: The VisuOnto should be as comprehensible as possible, and is thus easy to be
used in industry.
Ontology Development Process.
We broadly follow the routine of the Human-Centered Collaborative Ontology Engineering
Methodology (HCOME) [16, 17], which is a kind of collaborative ontology engineering method-
ology. We use Protege as an ontology editor with OWL 2 as the underlying representation
language. The whole process can be divided into 4 steps, which are dipicted in Fig 2. Step
1: Domain Analysis: We discussed common visualisation tasks at Bosch, read literature, and
studied common Python libraries (e.g. matplotlib) in order to comprehend the knowledge of
visualisation domain. We enumerate common and important terms of visualisation tasks and
classified visualisation tasks into categories to built taxonomies of tasks. In addition, we also
studied frameworks of implementing visualisation tasks with popular programming languages
(essentially Python). Step 2: Concepts Formalisation: Based on the terms collected from the
last steps, basic concepts are formalised as classes and relationships between them. Step 3:
Mechanism Investigation. We study the mechanism of how VisuOnto can serve as the basis for
our visualisation knowledge graph generation. Visualisation knowledge graph is the knowledge
graph representing a concrete visualisation task and also the procedure to solve it by drawing
the specific diagram. We will discuss this more in Section 3. Step 4: System Deployment. After the
validation, the ontology will be deployed in manufacturing, where user feedbacks are collected
constantly and lead to further domain analysis and iterative processing.
Visualisation Ontology. The visualisation ontology represents the concept of building a
concrete diagram to present specific data. Intuitively, to build a diagram with some data to
present, one need first to determine the overall properties of the canvas, such as its name and
layout. Next, each set of data are presented in the diagram with desired properties. An example
of such process in building a diagram can be seen in Fig. 1. According to the requirement
of practicability, this ontology , as partially depicted in the right of Fig. 2, emphasis on the
workflow of such building process.
Specifically, under the concept of visualisation, there are three classes, VisualisationTask,
VisualisationMethod and VisualisationProcedure with their names representing their nature. Visu-
alisationTask can be divided into two sub-classes, the AtomicVisualisationTask and PracticalVisu-
alisationTask. The AtomicVisualisationTask models the most basic visualisation components and
is thus named as “atomic". There are two kinds (sub-classes) of atomic visualisation tasks, first
is CanvasCreationTask, which determines the canvas, and the second is DataRepresentationTask,
which refers the task of presenting one set of data in the canvas accordingly. These two classes
of tasks correspond two sub-classes of VisualisationMethod, namely FigureConstructionMethod
and PlotConstructionMethod respectively. Another visualisation task, PracticalVisualisationTask
models the practical visualisation tasks, they can be regarded as the serialization of atomic
visualisation tasks. It connects VisualisationProcedure with the object property hasPipeline.
And the class VisualisationProcedure consists of a series of VisualisationStep, which adpot the
VisualisationMethod and is completeIned by AtomicVisualisationTask. The reasoning of this
visualisation ontology includes such constraints: every PracticalVisualisationTask split into
exactly one CanvasCreationTask and at least one DataRepresentationTask.
3. Evaluation and Application
In this section we will introduce the evaluations of VisuOnto and one of its application.
Workshop Evaluation. To evaluate the developed ontology, several workshops are held in
Bosch, In the workshops, practical visualisation tasks in Bosch are collected. Several data
scientists and knowledge engineers at Bosch are asked to represent these tasks based on our
ontology afterwards. According to the generated representations, data scientists will try to give
out the procedural (or python scripts) to complete the tasks. In this process, the questions in
three dimensions are studied.
D1: How well can VisuOnto represent the collected visualisation tasks.
D2: How well can VisuOnto represent the procedure that can be used to complete the collected
visualisation tasks.
D3: The hardness to understand and use VisuOnto.
These three dimensions of questions correspond to the three aforementioned requirement of
our ontology respectively.
Competence Questions. The collected visualisation tasks in the previous step are selected
randomly, and knowledge engineers at Bosch encode them into ontologies in instance level (as
known as knowledge graphs). Then the competency questions are discussed. The designed
competency questions reflect the coverage of the domain knowledge from two aspects in the
visualisation, i.e., visualisation tasks inspection (e.g., What are the data desired to represent in
one visual-task? ), visualisation procedure summary (e.g., What is the last step in drawing a
chart for one task?).
Automatic Knowledge Graph Generation. Through our ontology, an knowledge graph
representing concrete visualisation tasks can be generated automatically. Specifically, a GUI
can be used to ask users give out specific information to describe a visualisation task, those
information in individual level can be encoded as the assertional knowledge into the ontol-
ogy, forming knowledge graphs automatically. Since VisuOnto is procedure-orientated, such
knowledge graphs not only represent specific visualization tasks, but can also be used to decode
specific pipelines to solve the corresponding tasks.
4. Conclusion and Outlook
In this paper we present our ongoing work of visualisation ontology. The generated ontology
is easy to understand and covers most of visualisation cases in industry. Additionally, it’s
practice-orientated, which means this ontology also emphasis on the general knowledge of
visualisation pipelines. This ontology is still under evolution in Bosch: it will be continuously
evaluated, exploited and utilized in use cases throughout its life cycle, which is part of the future
work.
Acknowledgements. The work was partially supported by the H2020 projects Dome 4.0
(Grant Agreement No. 953163), OntoCommons (Grant Agreement No. 958371), and DataCloud
(Grant Agreement No. 101016835) and the SIRIUS Centre, Norwegian Research Council project
number 237898. We gratefully acknowledge the economic support from The Research Council of
Norway and Equinor ASA through Research Council project “308817 - Digital wells for optimal
production and drainage” (DigiWell).
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