=Paper=
{{Paper
|id=Vol-2969/paper26-FOMI
|storemode=property
|title=Application of an Ontology Based Process Model Construction Tool
for Active Protective Coatings: Corrosion Inhibitor Release
|pdfUrl=https://ceur-ws.org/Vol-2969/paper26-FOMI.pdf
|volume=Vol-2969
|authors=Peter Klein,Heinz A. Preisig,Martin Thomas Horsch,Natalia Konchakova
|dblpUrl=https://dblp.org/rec/conf/jowo/0001PHK21
}}
==Application of an Ontology Based Process Model Construction Tool
for Active Protective Coatings: Corrosion Inhibitor Release==
https://ceur-ws.org/Vol-2969/paper26-FOMI.pdf
Application of an Ontology Based Process Model
Construction Tool for Active Protective Coatings:
Corrosion Inhibitor Release
Peter Klein1 , Heinz A. Preisig2 , Martin Thomas Horsch3,4 and Natalia Konchakova5
1
Fraunhofer Institute for Industrial Mathematics, Fraunhofer-Platz 1, 67663 Kaiserslautern, Germany
2
Department of Chemical Engineering, Norwegian University of Science and Technology, Trondheim, Norway
3
High Performance Computing Center Stuttgart, Nobelstr. 19, 70569 Stuttgart, Germany
4
STFC Daresbury Laboratory, UK Research and Innovation, Keckwick Ln, Daresbury WA4 4AD, United Kingdom
5
Institute of Surface Science, Helmholtz-Zentrum Hereon, Max-Planck-Straße 1, 21502 Geesthacht, Germany
Abstract
Ontology-based integrated materials modelling for an active protective coating system design is pre-
sented and applied to a practical example. For this purpose, an ontological methodology implemented
using the ProMo (Process Modelling) suite is developed to be used with an open simulation platform
(OSP), i.e., a workflow management and orchestration framework that can be integrated into digital in-
frastructures. The target infrastructures, which are under development in various Horizon 2020 projects,
include modelling marketplaces, open innovation platforms, and open translation environments among
others. Semantic interoperability for the communication between the involved digital infrastructures,
including the simulation hubs, relies on the Review of Materials Modelling (RoMM), MODA (Modelling
Data), and the Ontology for Simulation, Modelling, and Optimization (OSMO) in combination with the
Physicalistic Interpretation of Modelling and Simulation Interoperability Infrastructure (PIMS-II) mid-
level ontology, which is aligned with the Elementary Multiperspective Material Ontology (EMMO) as
a top-level ontology. The challenge of addressing semantic heterogeneity is addressed by working to-
ward crosswalks between domain-specific and mid-level ontologies for industrially relevant problems,
where knowledge graph transformation is evaluated as a candidate solution for a future implementation
strategy. The involved semantic artefacts are platform-agnostic, and their EMMO compliance allows for
a specification of executable modelling and simulation workflows on multiple EMMO-compliant OSPs.
We demonstrate the presented approach on industrial relevant example for development of active cor-
rosion protection of metallic surfaces.
Keywords
active protective coating, applied ontology, Elementary Multiperspective Material Ontology, graph
transformation, molecular modelling and simulation, ontology alignment, process data technology
1. Introduction
Europe has been dedicating substantial efforts in its Framework Programs FP7 and Horizon 2020
to the development of Open Simulation Platforms (OSPs), which consist of model orchestration
FOMI 2021: 11th International Workshop on Formal Ontologies meet Industry, held at JOWO 2021: Episode VII The
Bolzano Summer of Knowledge, September 11–18, 2021, Bolzano, Italy
" peter.klein@itwm.fraunhofer.de (P. Klein); heinz.preisig@chemeng.ntnu.no (H. A. Preisig);
martin.horsch@stfc.ac.uk (M. T. Horsch); natalia.konchakova@hereon.de (N. Konchakova)
© 2021 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)
tools for the construction of materials modelling workflows and materials modelling and simu-
lation tool repositories. Their harmonization is connected to ongoing work on the Elementary
Multiperspective Material Ontology1 (EMMO) [1, 2] which, as a top-level ontology, supports
semantic interoperability, and the MODA (Modeling Data) standard [3] for documenting such
modelling workflows. These OSPs implement simulation backbones in various approaches,
who follow the idea to make materials modelling widely available, in particular to industrial
users, in Horizon 2020 efforts by adding additional services on top of their OSP cores. Among
them are business decision support systems, materials modelling marketplaces, open translation
environments, open innovation platforms and open innovation environments. All these efforts,
at various levels of maturity and rigour, offer aspects of translation [4, 5], which in its core deals
with the construction of materials modelling workflows fitting to industrial challenges.
Beside translating industrial problems to modelling workflows, translators typically need to
implement EMMO-interoperable semantic technology related to the required industrial and
simulation workflows. In this paper, we argue that humans providing these translation services
are unnecessarily expected to be ontology experts. In our opinion, this is an overburdening
of the Translator role that needs to be (semi-)automatized and in this paper, we present such
an approach. In previous work, the authors constructed a set of ontology-based tools with the
idea to provide a usable abstraction on top of EMMO and MODA supporting interoperability
between OSP cores, cf. Preisig [6]; in these and similar scenarios, a human in the loop, referred
to as the Scientific Data Officer [7], can greatly improve the outcome from the ontological
tools developed. Here, these tools are applied in a somewhat different arena: the construction
of modelling workflows for industrially relevant applications and the potential to implement
automation of modelling workflow construction based on the developed ontological tools.
It turns out that this challenge is feasible, albeit a human inspection and modification of the
outcome is still advantageous for constructing optimal modelling workflows.
As a practical example demonstrating the development of ontology based modelling by
EMMO extension and MODA application for industrial relevant multi-physics simulations, the
release of a corrosion inhibitor in an active protective coating is considered in the current paper.
2. Use Case and Simulation Workflow
2.1. Inhibitor Leaching in Active Protective Coating Processes
It is well known that aggressive environmental exposure and corrosion-induced damage often
limit engineering structures’ service life. Functional coatings are the most effective and efficient
way to protect bridges, off-shore equipment, cars, trains, buildings, ships, aircraft, and daily
consumables. The main role of the coating in degradation and damage protection is to provide
a dense barrier against corrosive species. However, defects appear in the protective coatings
during exploitation of the coated structures opening direct access for aggressive agents to the
metallic surface. Including anti-corrosive inhibitor agglomerates into coating polymer matrix
provides “smart” corrosion protection when the coating (barrier) is damaged (cracks) by its
ability to release a corrosion inhibitor (leaching) which accumulates in the cracks [8]. The
1
Previously known as the European Materials and Modelling Ontology, with the same abbreviation.
inhibitor, released from the nano-containers, forms an active layer on the substrate surface and
thus prevents the corrosion development on the metallic surface [9].
Figure 1: Pigments dissolution: Interpretation of inhibitor leaching processes including model simpli-
fication.
Corrosion inhibitor release (leaching), as sketched in Fig. 1, is the main active protective
mechanism of corrosion inhibiting primers used in the field of protective coatings [10]. These
primers are loaded with sparingly soluble salts to provide a reservoir of corrosion inhibiting
ions. In case of a defect, the coating will be exposed to the environment and absorb moisture,
which triggers the dissolution of the corrosion inhibitor, the leaching into the damage region
(crack), and their agglomeration on top of the metal substrate where inhibitor prevents the
surface from damage. The molecular structure of the corrosion inhibitor, the inhibitor leaching
rate, and the critical concentration in the damage regeion (crack) are the key factors influencing
the immediate and long-term corrosion prevention by this active protective coating process. The
development of new coating formulations is a complex process, usually performed by enormous
time and resources consuming experimental efforts. An effective way to accelerate novel
coatings development is by replacing experimental efforty by materials modelling methods.
The approach summarized above is one of the core scientific ideas of the ongoing H2020
EU project VIPCOAT [11] which is funded under call DT-NMBP-11-2020, “Open Innovation
Platform for Materials Modelling.” The main scientific objectives of the project are to establish
an Open Innovation Platform for the development of inhibiting active protective coatings and
corresponding accelerator tests for assessing their in-service durability, as well as to promote the
development of a green technology for active protective coatings based on materials modelling
and optimization.
One of the most effective classes of inhibitor agglomerates are Layered Double Hydrox-
ides (LDH) nanocontainers [12]. If LDH nanopigments are present in the corrosive media,
a significant reduction of the corrosion rate is observed. LDHs are anion-exchange systems
consisting of stacks of positively charged, mixed-metal hydroxide layers, intercalated by layers
of anionic species and solvent molecules. These environmentally friendly nano-structures al-
ready demonstrated their ability to control the release of active inhibition species under certain
environmental conditions. Their functioning is twofold: not only to release the species that
impart active protection, but also to trap corrosive inonic agents (for example, Cl− ) [13].
2.2. MODA Description of an Inhibitor Performance Simulation Workflow
A representation of the present simulation workflow using MODA [3] is depicted in Fig. 2.
Figure 2: Sketch of the MODA overview section for a simulation workflow of inhibitor selection, in-
hibitor leaching and inhibitor performance at a system level.
The individual models in the MODA workflow are identified along the following lines of
reasoning:
• The coating applied to a metal substrate consists of a polymer matrix enriched with
different solid particles, mostly pigments and active inhibitor agglomerates. The release
of inhibiting molecules in the polymer matrix is modelled using data based machine
learning (ML) as molecular descriptor roles. The data are retrieved from experiments or
atomistic modelling using molecular dynamics.
• The leaching within the microstructure is modelled using mesoscopic Computational
Fluid Dynamics (CFD). The dissolution of pigments uses the molecular desriptor roles
from the previous step as a source term. The microstructure of the coating is fully resolved.
• If a defect occurs and is exposed to humidity, water condenses in the defect area. The
leaching model is then coupled to a second CFD model which is responsible for simulating
the transport of inhibitor molecules to the substrate surface where they adsorb to form a
protective layer. (Fig. 1).
A full MODA/OSMO description of a use case, for each model in the MODA overview section,
calls for many more details. In particular, the mathematical equations, their boundary and initial
conditions need to be specified, together with a numerical solver used in the MODA workflow.
However, the modelling workflow description formalism from MODA [3] is affected by serious
shortcomings and ambiguities [14], particularly in view of an executable workflow deployment.
In order to overcome these shortcomings, an alternative approach has been developed, which
we outline and apply in the following sections.
2.3. Physical Topology
The physical topology is a graphical method to capture the essentials of a model. It shows
the process as a network of extensive-quantity-exchanging capacities. The capacities may be
distributed, meaning the intensive properties are a function of the position or lumped if that
is not the case. They are also associated with a relative time scale, namely event, dynamic or
constant/static. The surroundings of a process are modelled by a set of infinite capacities, which
– in analogy to thermodynamics – we call “reservoirs.” Only the intensive properties are known
for reservoirs, and the total extensive quantities, i.e., mass and energy, are not balanced. It is
the intensive properties of the reservoir that make the embedded process change its state. This
distinction between extensive and intensive variables and their roles in materials modelling is
completely absent from MODA.
In the demonstration case, cf. Section 2.1, we use reservoirs (infinitely large with constant
intensive properties) to model the electrolyte resources and distributed systems for the water
layer on top, the defect, the combination of coating matrix and electrolyte-containing pores
and the inhibitor layer. The adsorption of the inhibitor to the substrate is modelled as a point
capacity where the adsorption takes place and distributed system for the inhibitor layer (Fig. 3).
More details on the graphical modelling language and its relation to MODA can be found
in Preisig [6]. The main criticism of MODA here is that there are no control structures in the
MODA workflow description leading to an umbiguous mapping to executable workflows.
The inhibitor is stored in a sandwich-type of structure, which captures the inhibitor between
layers of support. The support is not exchanging material with its environment, as shown in
Fig. 4. Its sole purpose is to contain the inhibitor through a static electrical field, which we do
not show in this exhibition. One may view these particles like cookies with a filling of inhibitor.
The release of the inhibitor is an ion-exchange process in which the inhibitor ion is replaced by
either water or electrolyte components that cause corrosion of the substrate metal with chlorine
being the main one. The production of the inhibitor yields agglomerates of inhibitor-loaded
cookies, which yield a cumulative interface of the inhibitor particles with the water phase in
the pseudo-matrix-water phase (Fig. 3).
2.4. The Workflow as a Cognitive Process
The modelling workflow evolves directly from the topology. As an example, we divided the dis-
tributed systems in Fig. 3 into two CFD simulations modelling the behaviour of the pseudophase
and the leaching process. Additionally, the leaching process is considered by a molecular
simulation of a single LDH cookie.
The physical topology, representing one possible model for the coating problem, can be
mapped to a workflow; the mid-level ontology representation of that workflow, using the
Physicalistic Interpretation of Modelling and Simulation Interoperability Infrastructure (PIMS-
II), is depicted in Fig. 5. Following design choices from the EMMO, the PIMS-II mid-level
Figure 3: A possible physical topology for the coating process. The “weather” is here simulated by
mixing two streams of electrolyte, allowing for changing concentration as well as switching on/off
the supply. The coating matrix and the pores are combined to form a “pseudo phase,” which implies
that properties are normalised by the overall volume. The inhibitor leaches into the pores filled with
the watery electrolyte in the matrix, thus the pseudo-phase, and forms an inhibiting layer. The large
grey boxes suggest a possible simulation arrangement. Thus, one would simulate the leaching and the
diffusion in the crack and the pores together. The building of the inhibitor layers would be solved in a
second task.
Figure 4: Left: Expansion of the “inhibitor agglomerates” from Fig. 3, showing three representative
“cookies” as an example. Right: Single cookie, with the support material and the in-between sand-
wiched ion layer of inhibitor (LDH), active components like chlorine anions, and water.
ontology is fundamentally based on mereotopology [17] and Peircean semiotics [18], which are
combined to mereosemiotics as a coherent ontological paradigm [19] that is formalized both in
OWL2 description logic as well as by a series of axioms in modal first-order logic [15]. In PIMS-II,
following Peirce’s approach, an elementary cognitive step is a process which is conceptualized
as a triad and starts from the previously established representation relation between a sign (i.e.,
Figure 5: Modelling and simulation workflow represented as a cognitive process, based on Peircean
semiotics, using the graphical notation associated with PIMS-II [15, 16]; arrows labelled Č denote
the PIMS-II relation isSemioticallyConstitutiveOf, arrows labelled R denote isRepresentamenFor, unla-
belled solid arrows denote directlyGrounds, and unlabelled dashed arrows denote directlyPrecedesStep.
a representamen) and an object (i.e., the referent of the representamen); the cognitive step adds
a third element to the sign and the object, by which a new representation relation is created. In
Fig. 5, cognitive steps are visualized as triangles, where the three triadic elements are situated
in the corners; representation relations are denoted by blue arrows from the representamen to
the referent, and dependency relations between cognitive steps are denoted by green arrows.
A minimal representation of the workflow is a Petri-like net, which imposes causality: An
input/output mapping can only be executed if all inputs are available as indicated in Fig. 6.
Therefore, each computation step is equipped with an input gate. The gate functions like a
Petri transition: the computation is only started if all inputs are present. In addition, input data
must be persistent. Thus, the gate-compute mechanism guarantees causality. Workflows would
commonly step in time, which implies that the computational sequences are repeated. Thus
computation loops are formed, which must be initialised at the beginning. For this purpose, an
additional element, namely an input-selection switch, needs to be introduced, which will change
from initial conditions to taking the initial conditions for the next step as the final condition of
the previous time step. One may also implement a split, where a signal is passed to more than
one gate. An example is the starting point, which sends the starting signal to all three activities.
Figure 6: A slightly simplified Petri-like net representation of the workflow leaving out the “weather”
simulation section. Each computational step has an import gate. A computation can only be started
once all the inputs are available. Data at the input are persistent. A computational step may have
several outputs allowing to trigger other computational steps. The workflow is for one time-step only
and assumes that the leaching model is computed only once during each step. Adding time-stepping
requires a loop, which needs to be initiated by introducing a input switch as an additional control
element. The post-processing step includes activities like graphical representation of the result or the
computing of performance criteria.
3. MODA to EMMO Crosswalk
3.1. Knowledge Graph Transformation Systems (KGTS)
By a crosswalk, we refer to any algorithm, tool, or specification by which instances of one
semantic artefact are systematically mapped to instances of another [20]. This includes con-
ventional ontology alignments, obtained as solutions of the ontology matching problem [21],
but can go beyond that, as it is indeed necessary for workflow and provenance metadata in
computational engineering; n.b., this is not due to any specific complexity of the underlying
disciplinary matters. Instead, it is a consequence of requirements by the European Commission,
which simultaneously endorses both MODA [3] and the EMMO [1, 2] as metadata standards
to be applied to the same domain of knowledge, requiring a crosswalk between them. In the
crosswalk from MODA to the EMMO, it is an intermediate step that turns out to be crucial
to the quality of the outcome. On the one hand, MODA has an immediate correspondence
with OSMO, the domain-ontology version of MODA [14, 22]; on the other hand, the mid-level
ontology PIMS-II is designed to be close to the top-level ontology EMMO [15]. However, it is
the alignment of OSMO with PIMS-II that it is comparably challenging to implement practically.
This reflects a substantial discrepancy between the way in which information is arranged in
MODA and the system of relations provided by the EMMO; due to major deviations between the
structure of knowledge graphs that correspond to each other, applying conventional ontology
alignments would lead to a major loss of knowledge. Instead, the present work explores a
route based on knowledge graph transformation systems (KGTS), i.e., graph transformation
systems [23, 24] applied to knowledge graphs based on RDF triples.
Graph transformation is among the formalisms that are occasionally (though not very fre-
quently) used in semantic-web architectures [26, 27, 28], including work by Mahfoudh et al.
[27] on ontology merging. To assess the viability of KGTS for crosswalks between workflow
representations in computational molecular engineering, a candidate fragment of a KGTS was
Figure 7: Two rewriting rules selected from a knowledge graph transformation system (KGTS) that
aligns OSMO [14, 22, 25] with PIMS-II ... [15, 16]. Arrows labelled Ėint denote the PIMS-II relation is-
SignInInterpretation, arrows labelled E int denote isInterpretantInInterpretation, and the arrow labelled
O denotes isReferentIn. The left-hand side specifies the pattern upon which the applicability of the
rule is conditional. The right-hand side specifies the graph which with it is to be replaced; grey colour:
Vertices and labels unaffected by the transformation step. Top: Where the pattern on the left-hand
side is detected, the edge labelled contains_section is deleted and an edge labelled O is created. Bot-...
tom: Where a vertex (individual) instantiating the concept solver has any edges labelled Ėint and E int ,
representing input and output from a simulation step, it is replaced with a new vertex, i.e., an individual
with a new IRI is introduced, satisfying all properties and occurring in the same assertions as the old
individual; then for the new vertex, the instantiation of solver (from OSMO) is deleted, and an assertion
is created by which it instantiates Simulation (from PIMS-II). This replacement is necessary because
the solver and the simulation cannot be the same individual, but it is desirable for the new element to
retain the role of the old element in several respects; other rewriting rules can be applied subsequently
to correct any undesired properties that have been carried over from the source individual.
developed for OSMO as the source ontology and PIMS-II as the target ontology; two selected
rewriting rules from this fragment are shown in Fig. 7. In all rules, newly created vertices and
edges exclusively instantiate concepts and relations from the target ontology, and each rule
deletes at least one instance of a concept or relation from the source ontology. This substantially
restricts the expressive capacity in comparison with graph grammars in general, which are
Turing-complete; n.b., however, that the expressive capacity of conventional ontology align-
ments is still strictly included, while termination after a linear number of transformation steps is
guaranteed. This reduces the problem to 𝒪(𝑚𝑛) instances of the graph isomorphism problem,
where 𝑛 is the size of the source graph and 𝑚 is the number of rules (usually, a constant). While
that problem is not known to be solvable in polynomial time, it has been shown to be quasipoly-
nomial [29]. It is also the use case for which engines of semantic technology software (e.g.,
SPARQL end points) are best optimized. Since race conditions can occur between critical pairs
of rewriting rules [30], the outcome is not in general uniquely defined; if required, uniqueness
of the mapping can be enforced by imposing an order of precedence between applicable rules.
The rules from Fig. 7 illustrate how, particularly by specifying multi-node shape constraints
(implementable straightforwardly in SPARQL or SHACL) as a source pattern, the KGTS can
retain information that would be lost in a conventional alignment based on immediate con-
ceptual or relational subsumptions: The connection between a simulation (i.e., a semiosis,
following Peircean semiotics on which the EMMO is based) and the simulated object is one
of the foundational elements for the EMMO, and hence for PIMS-II, which it is crucial to pre-
serve. However, in MODA, and hence in OSMO, it is not the simulation but the solver that is
immediately represented as a section (corresponding to a page in MODA), and information on
the simulated object is not directly associated with the solver at all, but with a different section,
namely, the use case. Graph-based patterns can take such indirect connections into account.
3.2. Application to the Use-Case Scenario
This is illustrated here for the VIPCOAT use case from Section 2.1, applying the candidate
KGTS to the part of the workflow consisting of the CFD simulation of leaching and diffusion
and the preceding data-combination processing step. The source graph, shown in Fig. 8 (top),
corresponds to an annotation by metadata following OSMO, e.g., as it would be obtained by digi-
talizing the MODA input provided by a user of a simulation hub or a research data infrastructure.
The target graph, obtained by applying a sequence of rewriting rules, is shown at the bottom.
The KGTS crosswalk succeeds at retaining the most relevant features from the source graph.
This includes information on the dependency (linking) between the two steps of the workflow,
the data items communicated from step to step, and the relation between the representing
elements and their referent, the simulated leaching and diffusion process. The individuals shared
by both knowledge graphs, i.e., nodes that do not undergo deletion or replacement (𝑚, 𝑜, 𝑠, 𝑠′ ,
𝑠𝑚 , 𝑠𝑤 , and 𝑢) during this crosswalk, are highlighted with double-line borders. The capability to
replace source individuals with new individuals (as in the second rule from Fig. 7) substantially
increases the viability of the crosswalk; e.g., where MODA/OSMO indicates the presence of a
solver in a workflow, this almost always means that in the corresponding PIMS-II workflow
there should be a Simulation (at EMMO level, a Semiosis). However, an ontology alignment
that would subsume one under the other, osmo:solver ⊑ emmo:Semiosis, would be incorrect;
the solver and the semiosis are not the same individual, it is only the presence of one element
in the source graph that indicates the presence of the other element in the target graph. Such
correspondences go beyond what can usually be realized by conventional ontology matching.
3.3. Metadata Curation
While metadata tools can assist in digitalizing and annotating data, it is advisable to include
human support in the process. This includes data stewardship [31] and data curation [32],
functions that overlap but differ in nuance. Analysing requirements from high-performance
computing, Schembera and Durán [7] argue that technical and organizational (or “ethical”)
tasks interrelate, which is best addressed by advancing the role of a Scientific Data Officer (SDO)
Figure 8: Source and target knowledge graphs from a potential application of a KGTS implementation
of the OSMO to PIMS-II crosswalk. Top: Source knowledge graph. Abbreviations for relations from
OSMO: “hap” for has_access_point, “hcv” for has_carried_variable, “hli” for has_logical_input, “hlo” for
has_logical_output, “hres” for has_resource, and “hsv” for has_stored_variable; the unlabelled dashed
arrow denotes is_linked_to. Bottom: Target annotation in PIMS-II as it would be constructed auto-
matically by an automated crosswalk implementation. Arrows labelled Č denote the PIMS-II relation ...
isSemioticallyConstitutiveOf, arrows labelled Ėint denote isSignInInterpretation, arrows labelled E int
denote isInterpretantInInterpretation, arrows labelled O denote isReferentIn, arrows labelled R denote
isRepresentamenFor, and the unlabelled solid arrow denotes directlyGrounds.
Figure 9: Improved target graph as it might be obtained from an autogenerated target graph as in Fig. 8
(bottom) by the stewardship and curation work of a Scientific Data Officer, cf. Schembera and Durán
[7]; arrows labelled Č denote the PIMS-II relation isSemioticallyConstitutiveOf, the arrow labelled Ėacc
denotes ...
isAccumulationInputIn, the arrow labelled Ëacc denotes isObjectInAccumulation, the arrow
labelled E acc denotes isAccumulationOutputIn, the arrow labelled... Ėsim denotes isSimulationInputIn,
the arrow labelled Ësim denotes isSimulatedIn, the arrow labelled E sim denotes isSimulationOutputIn,
→
−
arrows labelled R denote isRepresentamenFor, the arrow labelled 𝑅 denotes isIncludedInAccumulation,
the arrow labelled ◁a denotes articulates, and the unlabelled solid arrow denotes directlyGrounds.
as a career. The job description for an SDO encompasses data stewardship and curation, legal
and procedural control of good practices, and user management. In the present case, an SDO
might reannotate the outcome from the KGTS (Fig. 8, bottom) as illustrated in Fig. 9. Thereby,
three kinds of improvements are made: First, instantiations of concepts and relations are made
more specific (e.g., from InformationProcessing to Accumulation). Second, the graph structure
is simplified by eliminating unnecessary nodes. Third, helpful additional edges are created;
here, the model employed in simulation 𝜄3 occurs in two ways: As the data item 𝑠, and as the
proposition 𝑚. This connection is made explicit by stating that 𝑠 articulates 𝑚 (relation ◁a ).
4. Conclusion
In this work, we apply an ontology-based toolset to support the translation process as specified
and recommended by the European Materials Modelling Council (EMMC). Through a minimal-
istic graphical modelling language with only few construction elements, highly complex model
topologies of industrially relevant processes can be modelled; the present work demonstrates
this for active protective coating development, applicable to many different surfaces in their
respective operational environment. The connection between a MODA-based and an EMMO-
based annotation of workflows constitutes a challenge, which the present work resolves by
constructing a context-sensitive crosswalk between the two semantic artefacts.
The present KGTS candidate fragment produces acceptable results even without human
intervention. Despite illustrating the potential for automatisation of a translation process
and the viability of the suggested approach, it is nonetheless not advised here that complex
crosswalks should be deployed in an unsupervised way. Requirements for human oversight
apply particularly strongly to the challenge of mapping information content from one metadata
schema to another. The difficulty is precisely due to semantic heterogeneity: Conceptual
schemes appear to be incommensurable unless a crosswalk has already been accepted as valid
by the community of its users (which is an organizational task, requiring an agreement) or
mappings are approved on a case-by-case basis, requiring an explicit control and affirmation
each time. It is therefore strictly impossible to substantiate the validity of crosswalks purely
by formal verification. If high standards of correctness are to be met (which is certainly not
always the case, since approximate annotation is often good enough), human supervision by a
translator or an SDO will be unavoidable.
In this work, we demonstrated that it is possible to devise ontology based model construction
tools for the specific example of active protective coatings. In the future, we plan to explore more
industrially relevant application areas in a similar spirit, most probably using a generalization
of the approach used in this work, in order to attain much broader translation capabilities.
Acknowledgments
The co-authors P.K., H.A.P., and N.K. acknowledge funding from the Horizon 2020 research and inno-
vation programme of the EU by grant agreement no. 952903, VIPCOAT, and H.A.P. also acknowledges
funding from Horizon 2020 by grant agreement no. 760173, MarketPlace. The co-author M.T.H. acknowl-
edges funding by DFG project no. 441926934, NFDI4Cat, within the NFDI programme of the German
Joint Science Conference (GWK). This work was facilitated by activities of Inprodat e.V., Kaiserslautern.
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