The development of software functionalities, or applications in general, that monitor and analyze manufacturing related data in order to improve, support or automate processes, is becoming increasingly important in industry. These applications require several information from different data sources in their context. An application that is planning a maintenance workers daily schedule for instance, requires several information about machine statuses, production plans and inventory, which resides in different systems likes Programmable Logical Controllers (PLC) or Structured Query Language (SQL) databases. Furthermore, manufacturing companies usually run machines and software systems from different vendors or of different ages. The schemata used in such systems do therefore not follow a certain standard, i.e. they are very heterogeneous in their semantics. When building such applications, accessing, searching and understanding the data sources is becoming a very time intensive, manual and error prone procedure that is repeated for every newly build application and for every newly introduced data source. To allow for an eased access, searching and understanding of these heterogeneous data sources, an ontology can be used to integrate all heterogeneous data sources in one schemata.
This repository contains an ontology of ISO 22400-2 which is a standard to describe KPIs. We maintain a whole list of standard-based ontologies, check out these links:
The ISO 22400-2 presents KPIs for Manufacturing Operations Management (MOM) by terminologically standardizing various KPI-related terms, but also providing formulas for their calculation. In addition, the ISO 22400-2 references the asset hierarchy defined in DIN EN 62264-2. By referring to the asset hierarchy of DIN EN 62264-2, all competency questions that are aiming at structural correlations should be able to be answered using ISO 22400-2 as well.
In this way, ISO 22400-2 also acts as a standardized matcher of KPI descriptions with structure descriptions. The Lightweight Ontology of the ISO 22400-2 can be found in the subsequent Figure 1. There are mainly three categories of KPIs, which are Production, Maintenance, Inventory and Quality. Each category holds a set of KPIs. Each KPI has a set of DataTypeProperties (Attributes) that can describe the KPI. Furthermore, there are a couple of annotations specific to every KPI and parameter. Every KPI may contain complex arguments in the sense that a KPI is calculated based on another KPI. Some KPIs may only have simple arguments in the sense of parameters like "Actual Production Time". Parameters also have annotaions specific to them and a set of attributes.
Figure 1: Key Performance Indicators according to ISO 22400-2
Exemplary competency questions that may be anwered by using this Ontology-Design Pattern can be found in Table 1. SPARQL patterns that create KPIs or their parameters can be found in the sub-folders of this directory.
Table 1: Exemplary competency questions and answers specific to ISO 22400-2
If you want to this ontology design pattern, the easiest way is to directly import it into your ontology via owl:imports statements. Make sure to reference a fixed release version so that you can't get surprised by future changes. To do so, click on the branch selection right below the number of commits and select a tag from the dropdown, e.g. v1.4.2. Then navigate to the .owl-file and open the raw file. For this example it would be https://raw.githubusercontent.com/hsu-aut/IndustrialStandard-ODP-ISO22400-2/v1.4.2/ISO22400-2.owl. You can use this URL in an owl:imports statement of your ontology. If you're having trouble using this URL in a tool like Protégé, try opening your ontology with a text editor and simply inserting your imports manually.
An example of an imports section looks like this:
<owl:Ontology rdf:about="http://www.hsu-ifa.de/ontologies/capability-model#">
<owl:versionIRI rdf:resource="http://www.hsu-ifa.de/ontologies/capability-model/1.0.0#"/>
<owl:imports rdf:resource="https://raw.githubusercontent.com/hsu-aut/IndustrialStandard-ODP-ISO22400-2/v1.4.2/ISO22400-2.owl"/>
</owl:Ontology>Of course you can also clone or download this repository and import an ODP from a local copy. The advantage of the first approach is that tools like Protégé or TopBraid Composer will directly use the ontologies from the internet and you can simply increase the version number in case you want to use a newer version of our ODPs.
In case you want to make creating individuals from these TBoxes a lot easier, check out our 'Lightweight Industrial Ontology Design Support Tool' (LiOnS). It is designed to create RDF models using the Ontology Design Patterns of this repository. This enables users to:
- Semi-automatically design RDF models (only variable parts of the graph have to be defined)
- Consistent modelling, without being an ontology expert
- Downloading Turtle serialized models or SPARQL INSERTs
For more information, see https://github.com/hsu-aut/lion.
- C. Hildebrandt, A. Köcher, C. Kustner, C.-M. Lopez-Enriquez, A.W. Muller, B. Caesar, C.S. Gundlach, A. Fay: Ontology Building for Cyber-Physical Systems: Application in the Manufacturing Domain. IEEE Transactions on Automation Science and Engineering, 2020, S. 1–17.
- C. Hildebrandt, S. Törsleff, T. Bandyszak, B. Caesar, A. Ludewig, A. Fay: Ontology Engineering for Collaborative Embedded Systems – Requirements and Initial Approach. In: Schäfer, Karagiannis (Hrsg.): Fachtagung Modellierung, 2018.
- C. Hildebrandt, S. Törsleff, B. Caesar, A. Fay: Ontology Building for Cyber-Physical Systems: A domain expert centric approach. In: 2018 14th IEEE Conference on Automation Science and Engineering (CASE 2018), 2018.
- C. Hildebrandt, A. Scholz, A. Fay, T. Schröder, T. Hadlich, C. Diedrich, M. Dubovy, C. Eck, R. Wiegand: Semantic Modeling for Collaboration and Cooperation of Systems in the production domain. In: 22nd IEEE Emerging Technology and Factory Automation (ETFA), 2017.