One of the major challenges in developing ontologies is to eficiently merge domain knowledge and expert knowledge to enable eficient and efective work on formal modelling of the domain in focus. This paper outlines the current state of developments in the Semantically Connected Semiconductor Supply Chains (SC3) project and its application in the BMBF-funded Cognitive Economy Intelligence Platform for Economic Ecosystem Resilience (CoyPu) project. We are using the SC3 Ontology Platform in CoyPu to promote efective information sharing among the various stakeholders in the development of the ontology. Thus, the application of SC3 Ontology Platform is used to ensure that the knowledge of non-knowledge workers (domain experts) and knowledge workers come together eficiently. This paper first introduces the CoyPu project and the current ontology development; then the SC3 Ontology Platform and its main components are presented. The paper concludes with the analysis of a first usability evaluation.
we have faced in that work is the identification of appropriate workflows and tools to achieve a common understanding of the domain and its formal representation. Our advanced approach is based on refining two-fold mapping process where data are transformed into visual primitives. It increases flexibility in using visual primitives to merge domain and experts knowledge. It can be merged though the SC3 platform in a way that anyone can create environment that supports other team members in understanding domain. Diferent type of visualization helps all team members to understand their own responsibilities. In this work, we describe the use of the Semantically Connected Semiconductor Supply Chains (SC3) Ontology Platform in this challenge. The SC3 is a Support Action funded by the European Commission. This paper is structured as follows: First, we introduce the SC3 Ontology Platform, focusing on the visualization of ontologies for diferent groups of experts. We then describe a survey we created for usability analysis of the platform within CoyPu. Finally, we discuss the results of this survey.
In computer science, ontologies represent a conceptual model of the world formalized as a
logical theory [
In general, this model is the basic data structure for various kinds of visualisation in the SC3 Ontology Platform. The RRM makes use of the fact that Semantic Web representations are described using resources, relations, annotations, axioms and type assertions. Accordingly, resources define type assertions, annotations, and axioms. Relations extend resources by providing domain and range restrictions forming the connection between resources. The RRM model reorganizes structured textual representation of Semantic Web data into representation format for further processing. This model represents network-like structures of Semantic Web data with additional grouping and classification of triples. Exactly, this is used for further information processing as preparation for various kinds of visualisation techniques; each visualisation mode is constructed from the RRM model and directly interacts with it (see Fig. 1). Any change in the RRM model impacts changes to the other views. In the rest of this paper, we introduce the visualisations in more detail.
The graph-based view of the ontology is visualized in the form of a node-dot diagram with
customization options. In this graph-based visualization, the RRM is first converted into a
node-link model (NLM, see Fig. 2) and this NLM is then sent to a rendering module for display
in the form of a graph. The user can then choose between a UML (see Fig. 3) and a VOWL
notation (see Fig. 4) at this point. The NLM consists of nodes and links. Each node has unique
identifier, type and name. Links connect nodes [
Led by disadvantages and advantages of VOWL and UML notations (see Table 2 and Table
3 in [
For the intermediate level users, ontologies in the system can be also visualized in Hybrid Mode. In this mode, information from the ontology is shown in the form of resources and relations. Each individual resource and relation is represented in the form of header, description, widget-based representation and graph-based representation. Meta information is also shown in this view (see Fig. 5). Our assumption hereby is that an intermediate user has certain knowledge about ontology construction and is interested in further detailed information as like the given axioms. Ontologies can then also be displayed in the form of a textual mode. In this view, every detail of the ontology can be viewed in detail. We have chosen the TTL as a displaying ontology format.
A missing element of the SC3 Ontology Platform is currently the visualization of ontology rules,
which should be easily understood by all users involved in the ontology development process.
To extend the hybrid and graph-based visualization modes to support this feature, we plan to
extend the RRM model and dual mapping mechanisms. Other features we plan to address are
a comprehensive authentication and role management system. This system should allow to
create and manage own collections. In addition, the collaborative work on the creation and
modification of ontologies should be implemented via a tight coupling with WebProtégé [
To evaluate the applicability of the platform, we conduct a series of evaluations. The ongoing
survey for measure usability of SC3 Ontology Platform uses the System Usability Scale (SUS,
[
Seven researchers and software developers participated in this survey. Questions are divided into four diferent groups. The first group is made up of thirteen questions that are related to the user’s experience with S3C Ontology Platform. Responses in this group can be scored from strongly disagree (marked as number 1) to strongly agree (marked as number 5). Table 1 shows individual scores for each question of the SUS feedback. Each participant can answer on a question using scale from 1 to 5. This table shows more precisely individual flaws or strengths with regard to the SUS. Table 2 shows the SUS raw and final scores for each participant including average for all SUS raw score and SUS final score based on results shown in Table 1. The SUS raw score is the sum of subtracting number one from each odd numbered question value and subtracting even numbered question value from five. The SUS final score for each participant is multiplication of each SUS raw score with 2.5.
Overall, the average of all SUS final scores is equivalent to 68.21 that is marked as C grade
(65.0 – 71.0) defined in SUS proprietary scale table [
Second group of five questions is related to the general approach of the SC3 Ontology Platform. Exactly 57.1 percent of participants agreed that the Platform is a helpful tool to develop, visualise, and to modify ontology. Not all participants generally see benefits of existing the SC3 Ontology Platform over existing ontology development and visualisation tools. In addition, participants think that the SC3 Ontology Platform cannot replace existing ontology tools such as Protégé [11], but can improve visualisation services along with WebProtégé. Compared to other ontology tools, the majority of participants recognised the advantage in combining textual, graphical and widget based visualisation under the SC3 Ontology Platform rather than using only a single approach.
A set of thirty questions in the third group of the questionnaire is related to the SC3 Ontology Platform functionalities and how users are satisfied with using these functionalities. Responses in this group of questions can be scored using five grade score scale from strongly disagree to strongly agree. Table 3 shows participants’ opinion about the SC3 Ontology Platform scored as agree and strongly agree. In this table the second column summarises the percentages of User interface is easily understandable Hybrid mode for ontology modelling The dropdown buttons in Hybrid mode Easy to find the search/filter functionality valuable Easy to interact with the graph based visualisation The interaction with the graph is clear Easy to navigate between diferent views Performance of the platform is fast in terms of ontology editing The role of collapsible sidebars is clear
Percentage of participants that agree and strongly agree 85.7 71.5 71.5 85.7 71.5 71.4 71.5 71.4 100 agreed and strongly agreed scores about listed functionalities in the first column. In total, more than 71 percent of participants agreed or strongly agreed per а functionality. Exactly 71.4 percent of participants agreed with the statement that it is easy to understand SC3 user interface and only 14.3 of users strongly agreed with that statement. It is in total 85.7 percent of participants who find SC3 user interface easily understandable. The same result holds for ifnding the searching/filtering functionalities. All participants agreed that the role of collapsible sidebars is clear. Critical points in SC3 Ontology Platform functionalities, which participants observed, are interaction with graph based ontology view and how the platform is fast in terms of ontology editing, that is in total 71.4 percent of votes.
The fourth group of questions in the questionnaire is related to the amount of efort participants took to upload, visualise and modify ontology. This group has in total five questions. Table 3 shows a result of the subjective mental workload (MWL) assessment (NASA- TLX calculation) [12] based on seven responses on five questions. We did not include physical demand in the calculation. Last column in Table 4 represents individual score results across all subclasses. On the other hand last raw in Table 4 represents the group scores for every subclass separately. The overall score is given in the bottom right corner of the table. Based on the interpretation score of NASA-TLX [13], high (34.29) mental activities are required for all users to upload, modify and visualise ontology when using the SC3 Ontology Platform. The group score results show that the time pressure that participants felt during the work with the SC3 Ontology Platform is also high (54.29). The same conclusion is observed for performance subclass that is about 22 points greater than group score result for temporal subclass. All participants required a high amount of efort to achieve the requested level of performance when using the SC3 Ontology Platform. More than half participants feel high frustration when uploading, modifying and visualising ontologies in the SC3 Ontology Platform. On an individual basis, all participants, except for one, need high mental, physical, temporal activities when using the SC3 Ontology Platform. Second and fith participants are highly satisfied with the results of their work when using the SC3 Ontology Platform. The SC3 Ontology Platform usability evaluation shows that we have to take concrete measures to improve the SC3 tool. We take in account that supervised user study could help us to collect valuable feedback for further improvement of the tool.
In this contribution, we have discussed the status of the SC3 Ontology Platform development that is based on mappings from RRM to NLM model. One contribution in this work is to ofer the scientific community reach visualisation tool for developing ontologies that is built by integrating diferent tools, such as VOWL and WebProtégé. Such integration of diferent tools ensure that domain experts and knowledge engineers can work together and collaborate more eficiently. Advanced approach applied in development of the SC3 platform is a first step towards our efort to merge domain and experts knowledge when using the SC3 platform. In this publication, we have now presented the main components of the SC3 platform. This is motivated by the work in the SC3 and CoyPu projects, where ontologies are under development between domain experts and knowledge engineers. Our concern in this paper has now been to evaluate exactly how usability of the SC3 ontology platform is actually current. To accomplish this task, we combine the System Usability Scale and the NASA Task Load Index. The results show motivating values but also gaps that need to be addressed in further development. It is obvious that more feedback about the platform still needs to be gathered. We will continue gathering feedback about the SC3 platform for the future development.
This work documents results from two projects: The research has received funding from the EU KDT Joint Undertaking under grant agreement n° 101007312 (project Semantically Connected Semiconductor Supply Chains - SC3) and by the Federal Ministry for Economic Afairs and Energy of Germany in the project Cognitive Economy Intelligence Plattform für die Resilienz wirtschaftlicher Ökosysteme - CoyPu (project number 01MK21007[A-L]). [11] System usability scale and interpreting sus scores, 2022. URL: https://protege.stanford.edu/. [12] Nasa-tlx scoring worksheet, 2022. URL: https://testscience.org/wp-content/uploads/sites/ 16/formidable/15/NASA-TLX-Calculator-1.xlsx. [13] A. D. Prabaswari, C. Basumerda, B. W. Utomo, The mental workload analysis of staf in study program of private educational organization, in: IOP Conference Series: Materials Science and Engineering, volume 528, IOP Publishing, 2019, p. 012018.