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 efectively 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.
Data driven methods especially machine learning aim to extract knowledge and insights from
noisy, structured and unstructured data [
However, a formal description of the general knowledge and practical methods about
visualisation is still lacking in the industry. This renders the clarity of the representation unguaranteed
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 [
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 efort with literature and common programming libraries (e.g. matplotlib in Python). In addition, we also discuss applications and evaluation of our ontology.
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 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 ifgure, 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) [
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. VisualisationTask can be divided into two sub-classes, the AtomicVisualisationTask and PracticalVisualisationTask. 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.
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 ontology, 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.
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).