=Paper=
{{Paper
|id=Vol-2941/paper9
|storemode=property
|title=Towards Reusable Ontology Alignment for Manufacturing Maintenance
|pdfUrl=https://ceur-ws.org/Vol-2941/paper9.pdf
|volume=Vol-2941
|authors=Marco Kainzner,Christoph Klösch,Dominik Filipiak,Tek Raj Chhetri,Anna Fensel,Jorge Martinez-Gil
|dblpUrl=https://dblp.org/rec/conf/i-semantics/KainznerKFCFG21
}}
==Towards Reusable Ontology Alignment for Manufacturing Maintenance==
https://ceur-ws.org/Vol-2941/paper9.pdf
Towards Reusable Ontology Alignment for
Manufacturing Maintenance
Marco Kainzner1 , Christoph Klösch1 , Dominik Filipiak1[0000−0002−4927−9992] ,
Tek Raj Chhetri1[0000−0002−3905−7878] , Anna Fensel1,2[0000−0002−1391−7104] ,
Jorge Martinez-Gil3[0000−0002−5730−7965]
1
Semantic Technology Institute, Department of Computer Science, University of
Innsbruck, Austria
2
Wageningen University & Research, The Netherlands
3
Software Competence Center Hagenberg GmbH, Austria
{marco.kainzner,christoph.kloesch}@student.uibk.ac.at,
{dominik.filipiak,tekraj.chhetri,anna.fensel}@sti2.at,
jorge.martinez-gil@scch.at
Abstract. With advancements in technology and big data availability,
industries are struggling with data interoperability and knowledge rep-
resentation. Ontologies have a great potential to solve such problems.
However, the lack of standardisation prevents the widespread adoption
of ontologies in different manufacturing domains. We investigate the pos-
sibility of preparing ontology alignment for manufacturing maintenance.
This paper provides an overview of the available ontologies in this do-
main. We also provide an openly available alignment between IMAMO
(maintenance ontology) and CDM-Core (process ontology): https://
github.com/DominikFilipiak/IMAMO-to-CDM-Core.
Keywords: Manufacturing maintenance · Ontology alignment
1 Introduction
Ontologies provide the ability to model and represent knowledge in a reusable
manner. For example, Chang et al. [5] use an ontology to define a knowledge
model providing definitions of common concepts and domain knowledge for a
service robot. Further, ontologies enable interoperability [11] and therefore have
a high potential to improve processes and save costs in various industries [9,4,14].
However, most of the ontologies are developed independently, which makes them
incompatible, non-shareable, and severely limits their potential applications [8].
These issues can be addressed either directly by developing a shared ontology
(or standardised development process) or through an ontology alignment. The
ontology alignment process results in combined knowledge originally represented
in multiple ontologies. In contrast, shared ontology development serves the same
Copyright © 2021 for this paper by its authors. Use permitted under Creative Com-
mons License Attribution 4.0 International (CC BY 4.0).
2 M. Kainzner et al.
purpose but is more time consuming and tedious [6]. Therefore, we benefit from
the existing body of knowledge to focus on the former.
Our work on the alignment focuses on ontologies in the manufacturing do-
main. The primary motivation for such a focus stems from the fact that mainte-
nance in manufacturing industries accounts for 15 to 60% of total manufacturing
operating costs [20]. Furthermore, to the best of our knowledge, no maintenance
ontology alignment has been performed. The alignment of manufacturing main-
tenance ontology helps to combine scattered knowledge, which can then be used
to improve maintenance, such as by performing predictive maintenance tasks like
Cao et al. [3]. Combining different ontologies improves, for example, data and
process integration for manufacturing maintenance and provides a solution to the
industry’s data heterogeneity challenge [17]. Paired with other systems, such an
alignment can broaden semantic contexts in the production process, for example
enhancing quality evaluation or failure analysis. While an ontology alignment
can also be performed automatically, we focus on the manual approach due to
the user acceptance and accuracy limitations of available tools [13]. We concen-
trate our efforts on answering the following research questions: What ontologies
for the manufacturing domain are available (RQ1)? To what extent can selected
ontologies in the manufacturing maintenance domain be combined (RQ2)?
The remainder of this paper is organised as follows. Section 2 provides an
overview of the existing ontologies and answers RQ1. Section 3 explains the
details of the ontology alignment process, which is related to RQ2. In Section 4,
we discuss possible use cases for our work. The paper is also concluded there.
2 Ontologies for manufacturing
To provide the alignment, one has to identify ontologies suitable for this pro-
cess. This section gives an overview of ontologies relevant to the manufacturing
domain. There are several ontologies dedicated to general manufacturing. For
example, Process Specification Language (PSL) [7] has been built to “facilitate
correct and complete exchange of process information among manufacturing sys-
tem” [7]. It is a standard that is openly available online. PSL has been formalised
in OWL. It consists of the core component and a set of its extensions. Grüninger
and Menzel argue that since different terminology is used by separate depart-
ments (such as logistics and resource managers), the business can benefit from
establishing semantic relationships between used concepts. The CDM-Core on-
tology [15] was developed as a common base ontology for the manufacturing
domain. It is used for process models, services and sensor data. Its authors
describe CDM-Core as “the first publicly available applied manufacturing on-
tology” [15]. The authors demonstrate the usage of their ontology with cases of
automotive exhaust production and metallic press maintenance. CDM-Core is
also formalised in OWL and it is generally available.
More recently, Additive Manufacturing Ontology (AMU) was developed to
address the lack of ontologies that are suited for modern manufacturing processes
such as additive manufacturing [18]. It is developed as part of the Industrial
Towards Reusable Ontology Alignment for Manufacturing Maintenance 3
Ontologies Foundry (IOF) initiative, and is formalised using OWL and DOLCE.
AMU focuses on modelling machines, products, features, types, and processes
occurring in additive manufacturing. A use case of ontology-based validation of
additive manufacturing data is presented in the paper. The authors also provide
a short survey of ontologies for additive manufacturing. Other manufacturing-
related ontologies are MASON [12], Machine Tool Model (MTM) [10], Machine
of a process ontology (MOP) [19], Manufacturing Service Description Language
(MSDL) [1], and Part-Focused Manufacturing Process Ontology (PMPO) [16].
Most of these are upper ontologies or are designed for general manufacturing.
There are, more specialised ontologies, for instance, explicitly developed for
manufacturing maintenance. IMAMO (Industrial Maintenance Management On-
tology) [9] is designed to cover all aspects related to manufacturing maintenance.
This ontology includes various concepts related to the structure of equipment to
be maintained – spare parts, failure detection, events, material resources, main-
tenance actors, technical documents, equipment states, and equipment life cycle.
Another manufacturing maintenance ontology, ROMAIN [8] is similar in scope
to IMAMO. It is built basing on the common Basic Formal Ontology (BFO) [2].
The authors present ROMAIN in a maintenance strategy effectiveness scenario.
3 Alignment
In this section, we explain our choice of ontologies and detail the process of
aligning them. We have chosen IMAMO (maintenance ontology) and CDM-
Core (process ontology), as these two ontologies were the best candidates for
alignment. Both of these ontologies cover the subject of general manufacturing.
Most of the other ontologies we examined are either upper ontologies or focused
on more narrow disciplines within manufacturing. The scopes of IMAMO and
CDM-Core are not equivalent, though – they are rather complementing each
other. IMAMO concentrates on the maintenance process and defines some basic
concepts for sensor data, whereas CDM-Core allows user to annotate process
models, services and sensor data. Moreover, both of the ontologies are openly
available online in OWL (many other that we identified were not).
The IMAMO ontology has 434 classes and 36 individuals, whereas the CDM-
Core contains 240 classes representing 18 individuals. To perform the mapping,
we analysed each concept of CDM-Core and searched for corresponding seman-
tic concepts in IMAMO. A manual alignment was performed, in which only the
superclasses of CDM-Core were considered. The alignment was conducted by the
authors of this paper. Since the overall number of classes was relatively small,
no specialised tool for the alignment was needed. If we found an equivalent class
(mostly based on label and ontology structure), we created a equivalentClass
element in our alignment. We were able to align 77% of the superclasses of
CDM-Core. Some classes could not be matched. IMAMO defines more granular
monitoring systems (in computational resource), such as Computerized Main-
tenance Management Software, Data Acquisition System, Diagnostics System,
or Document Management System. In contrast, CDM-Core focuses more on the
4 M. Kainzner et al.
process modelling part. Additionally, IMAMO defines “external resource”, which
is used for representing subcontractors (no such concept in CDM-Core). The
alignment is available publicly – the link is provided in the abstract.
4 Possible Use Cases and Conclusion
There are several possible use cases for the presented alignment, for example,
they may consider sensor data. The IMAMO ontology and the CDM-Core on-
tology have both sensor data defined (IMAMO#Sensor and CDM-Core#Sensor re-
spectively). The IMAMO sensor is defined as a device that detects and responds
to some input from the environment. In a manufacturing case, this would be the
physical environment where the sensor is attached. With this definition, one can
now use the CDM sensors (e.g. Electric power sensor, Pressure sensor) in
the IMAMO ontology without redefining it.
Another possible use case considers event-oriented systems. A key concept in
maintenance is a triggering system that starts particular actions. IMAMO con-
tains different classes facilitating this task: Alarm, Event Observed by User,
Improvement Request, or Notification (RUL, Warning). CDM-Core defines
the Component Fault class, which defines multiple faults (Gas Leakage, Cooler
Efficiency Degradation). In IMAMO, one would model these cases with a
Triggering Event – Alarm. With the provided alignment, one can react on
CDM-Faults and trigger a Maintenance with IMAMO.
This paper has given an overview of ontologies in the manufacturing domain.
We analysed them in terms of the possibility of alignment. In conclusion, we
decided to align the two publicly available ontologies CDM-Core and IMAMO
first. We were able to match 77% root classes of CDM-Core with IMAMO. It
can act as a starting point for other researchers to publish more alignments,
thereby facilitating knowledge sharing in manufacturing. A certain limitation of
this study is the lack of evaluation and validation. Therefore, future work might
encapsulate comparing our alignment to automatically generated ones, as well
as validating it with industrial domain experts. Future work could build on our
mapping and expand it by both adding more ontologies and more sub-concepts.
Acknowledgements This research was co-funded by Interreg Österreich-Bayern
2014-2020 programme project KI-Net: Bausteine für KI-basierte Optimierungen in der
industriellen Fertigung (grant agreement: AB 292), and EU Horizon 2020 project On-
toCommons: Ontology-driven data documentation for Industry Commons (grant agree-
ment no. 958371).
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