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5 Use Case 2: Mapping and Change Propagation between Engineering Models

5 Use Case 2: Mapping and Change Propagation between Engineering Models

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12 Semantic Web Solutions in the Automotive Industry


The project required that a framework should be implemented that will support

the consistency checking between the two design models, as well as, the change

propagation from one model to another.

The full details about the implementation of this project can be found in Chaps. 5

and 6 of (Tudorache 2006b).

12.5.1 Mapping Between Libraries of Components

The engineers build the functional and geometrical models using model libraries

from their respective tools. Therefore, a model is composed by instantiating template

components from the model libraries and by interrelating them. Figure 12.8 shows

this situation.

The libraries and the design models are represented in different modeling languages. The names used for corresponding components in the two model libraries

are different. The CAD library uses German names, whereas the functional library

uses English names for the components.

However, the design models built from these component libraries still have one

thing in common: They represent the same product from different perspectives. As a

consequence, there will be correspondences between the structures and parameters

of the models in the two libraries, shown in Fig. 12.8. We defined the correspondences, also known as, mappings, between components of the library. In this way it

is possible to reuse them between all instances of corresponding templates. To map

an entire system, we need to be able to compose the mappings between components.

Fig. 12.8 Mappings between component libraries in two viewpoints


T. Tudorache and L. Alani

We developed a mapping framework, described in the following section, which

supports the following operations:

The definition of mappings between library components in different viewpoints,

Reuse of template mappings for different instantiations of the design models,

Computing the mapping of systems based on the mappings of their parts,

Consistency checking between design models from different viewpoints based on

the predefined mappings.

12.5.2 The Mapping Framework

The mapping framework is used to map between different representations of the

viewpoints of a product. The mappings can be used to check the consistency of the

different viewpoints of a product, or to propagate changes from one viewpoint to the


The components of the mapping framework are (Fig. 12.9):

∙ Design models—developed by engineers in different engineering tools (e.g., Catia,

Modelica, etc.),

∙ Local ontologies—represent the semantically enriched models of the design models,

∙ Engineering ontologies—used as a common upper ontology for the local ontologies,

∙ Mapping ontology—used to represent declaratively the mappings (correspondences) between the local ontologies,

∙ Reasoner—interprets the defined mappings at run time and supports the execution

of the tasks.

The design models are the models that engineers build in the engineering tools.

For example, an excerpt of a functional model, and an XML representation of geometrical model of a planetary9 are shown in Fig. 12.10 side–by–side.

The design models that participate in the design tasks are represented typically

in different modeling languages. In the previous example, the functional model is

represented in an object–oriented language for modeling physical systems, Modelica (Modelica Specification 2005), and the geometrical model is represented in an

XML–based proprietary format defined in (Zimmermann 2005).

A local ontology is the result of the semantic enrichment of a design model. The

act of enriching, sometimes also called lifting (Maedche et al. 2002), transforms the

design model represented in some language into an ontology. This requires understanding the meta-model of the language and implies some transformations from

the language meta-model to an ontology (Karsai et al. 2003). In the example from


Planetaries are gear–wheels used in gearboxes to change the gears in a car.

12 Semantic Web Solutions in the Automotive Industry







Interpretation of

mappings and










Design Model 1



Design Model 2



Fig. 12.9 The mapping framework







Geometrical model in proprietary format

model IdealPlanetary "Ideal planetary gear box“

parameter Real ratio=100/50


Flange_a sun "sun flange"

Flange_a carrier "carrier flange"

Flange_b ring "ring flange"


(1 + ratio)*carrier.phi = sun.phi + ratio*ring.phi;


end IdealPlanetary;

Functional model in Modelica language

Fig. 12.10 Excerpts from two design models in different modeling languages side–by–side

Fig. 12.10, the geometrical representation of the planetary uses a XML element—

SimpleParameter—to represent the definition of a parameter of type float of the planetary.10 This would be represented in the local ontology using a template slot of type

float. The semantic enrichment can be done in different ways: either by annotating

the design model elements with the semantic concepts from the ontology, similar to

the approach taken by (Wache 2003), or by transforming parts of the design model

in classes and instances in the local ontology. We have chosen the second approach

in order to allow logical reasoning to be preformed on the concepts in the ontology.

The Engineering Ontologies are used as a common upper level vocabulary for

the local ontologies. As in the hybrid integration approach, described in (Wache

et al. 2001), the upper level ontologies ensure that the local ontologies share a set


“Planetensatz” (in German) means planetary.


T. Tudorache and L. Alani












Geometry Ontology



Functional Ontology

Planetensatz <-> IdealPlanetary

Planetensatz.hohlrad <-> IdealPlanetary.ring

Plantensatz.Hohlrad.ZähneZahl /Plantensatz.Sonnenrad.ZähneZahl = IdealPlanetary.ratio

Fig. 12.11 Mappings between the local geometrical and functional ontologies. The classes Planetensatz and IdealPlanetary are mapped together. Also their parts are mapped together, hohlrad of

the Planetensatz is mapped to the ring of IdealPlanetary. The mapping between the Planetensatz

and IdealPlanetary also contain a constraint between the attributes of the classes in the form of a

mathematical relationship

of concepts with the same semantics. The local ontologies specialize the upper level

concepts. This also ensures that the local ontologies will be comparable with each

other. This brings many benefits if mappings between the local ontologies need to

be discovered automatically.

The Mapping Ontology is used to represent in a declarative way the correspondences between the local ontologies. Having an explicit and well–defined representation of mappings enable reasoning about them, such as, checking whether two mappings are equivalent or mapping composition (Madhavan et al. 2002).

An explicit representation of mappings makes also possible to model different

types of mappings, such as, renaming mappings, lexical mappings, recursive mappings, and so on. Park et al. (1998) propose a classification of mapping types. However, the mapping ontology must be designed in such a way that it supports the tasks

that must be solved. For instance, mappings between ontologies of engineering systems have to take into consideration the part–whole decomposition of systems and

hence they have to support the representation of paths in the part–whole hierarchy.

An example of the mappings between the functional and geometrical ontologies

in the previous example is shown in Fig. 12.11.

The reasoner is an important part of the mapping framework. The reasoner is

used both at design–time of the mappings and at runtime to support the execution of

the design tasks. At design time, the reasoner may be used to verify the mappings

and to see their effect in a testing environment. It can also provide suggestions for

other mappings based on the already defined ones, or warn about missing mappings.

At runtime, the reasoner executes the mappings and other operations, such as consistency checking, that are relevant for a particular task.

The reasoner used in the framework is F LORA-2 , described briefly in Sect. 12.3.1.

12 Semantic Web Solutions in the Automotive Industry


The mapping approach described in this section shares similarities with the Engineering Knowledge Base approach, introduced in Chap. 4 of this book. We also refer

the reader to Chap. 6 for an in–depth discussion of mapping approaches, as well as

to the following papers (Biffl et al. 2014; Kovalenko et al. 2013; Moser et al. 2011).

12.5.3 Defining the Mappings

In order to define the mappings, the design models in the two viewpoints have to be

semantically enriched, as described in the previous section. The enrichment is done

by exporting the data from the design models to instances of the local ontologies. The

local ontologies already contain the class definitions corresponding to the template

components in the library of models in the two viewpoints.

The local ontologies corresponding to the template libraries both include the

Components ontology presented in Sect. 12.3. In this way, a common vocabulary for

the two viewpoints is defined, which already provides a starting point for finding the

correspondences between the ontologies. For example, a Component in one ontology

is typically mapped to another Component in the other ontology. The Plantensatz11

class in the geometrical viewpoint is mapped to the IdealPlanetary in the Functional


The mappings are interrelating components from the two ontologies. Some examples are shown in the previous section. The full mapping algorithm used to compose

the mappings between the components in order to map the top–level systems, such

as the Getriebe and Gearbox, is described in detail in Chap. 5 of (Tudorache 2006b).

12.5.4 Consistency Checking and Change Propagation

There are different types of mappings that can hold between different design models.

Some of them are simply structural (one component maps to another), but others

carry more information. It is common that there are mathematical constraints that

interrelate the parameters of the different models.

We modeled the mathematical relationships between the parameters as constraints

attached to the mappings between concepts in the ontologies. For example, the ratio

of the IdealPlanetary of the functional model is computed out of two parameters

of the parts of the corresponding class Planetensatz of the geometrical model. The

Planetensatz has as parts a Sonnenrad and a Hohlrad which have each defined a

number of teeth for the gears (in German, ZaehnenZahl). The constraint between the

ratio and the number of teeth of the gears is:

IdealPlanetary.ratio =

11 Planetensatz



in German means Planetary in English.



T. Tudorache and L. Alani

The constraint is defined in the context of a mapping and it is checked whenever the mapping is used. For example, this constraint is checked three times in the

mapping between the Getriebe and Gearbox systems, because they contain three

corresponding planetaries.

In order to check the consistency of two design models (e.g., geometrical and

functional), we have performed automatically the following steps:

∙ Convert the local models and their instances (containing the actual parameters of

a design model) into a Frame–logic representation,

∙ Convert the mapping ontology and its instantiation (containing the actual mappings between two design models) into a Frame–logic representation, and,

∙ Execute in F LORA-2 a Frame–logic query giving as input the two conversions

from above and the top–down mapping–composition algorithm, which will return

whether the two design models are consistent with each other.

In a similar way, we can use other predicates we have defined to do the change

propagation. In the example from above, if the IdealPlanetary.ratio is not set in the

functional model, F LORA-2 is able to compute it based on the mappings and the

attached mathematical constraints.

12.5.5 Benefits of an Ontology–Based Approach

One of the benefits we have found by using ontologies in this project was the fact

that the correspondences between the design models were described explicitly and

formally, which enabled us to automatically check the consistency between the

two models. Once the framework was implemented, the consistency checking and

change propagation could happen at the push of a button, which was previously not

possible. Also, the explicit representation of the mappings would make clear to the

engineers from both teams what are the dependencies in the other models, and how

a change in their model might affect the corresponding model. Such understanding is not to be taken for granted, as teams are often distributed, and are specialized in

certain domains, and do not have a full understanding of other aspects of the design.

The fact that we defined the mappings at the library level also allowed a better

management and building of the models: the mappings are defined once, and can

be used in all the instantiations of the library components. Even though, there was a

lot of effort involved in the initial phases, the subsequent mappings of other systems

has been much eased, as we could reuse a big part of the existing mappings.

The ontology–based approach for modeling the functional and geometrical design

models allowed to define meta-rules that constrain correct models, and which

can be checked at model building time. For example, certain types of components

cannot be connected together in a functional model. This type of constraint can be

easily formulated as an axiom in the functional ontology.

12 Semantic Web Solutions in the Automotive Industry


The ontology–based representation also improved the model management

process. For instance, it was straightforward to search for components in a model

library that have a certain type of connectors by using a simple query. The design

models can also be enriched with other types of semantic relationships that improve

the documentation of the model. Different types of meta-data can be attached to

components in the ontologies, such as, provenance, version, release status, etc., that

plan an important role in the development process.

12.6 Conclusion

We have presented five generic engineering ontologies (Components, Connections,

Systems, Requirements and Constraints), and we have shown how we have used them

in two real–world projects from the automotive industry. The first use case focused

on the stepwise refinement of requirements, and how an optimal design solution

was achieved by using ontologies and a relational constraint solver. The second use

case showed how we solved an integration problem, how we were able to check

the consistency of geometrical and functional models developed in specific tools

using ontologies and a Frame–logic reasoner. Following the description of each use

case, we have also talked about the perceived benefits of using an ontology–based


To summarize, we found several benefits of using ontologies in the engineering

domain. (1) By building generic and modular ontologies that we could reuse, we

were able to reduce the effort necessary to build new engineering models. (2) The

formal representation of the structure and constraints of these models allowed us

to automatically perform checks, both at model–authoring time, as well as post–

priori. This capability allowed us to capture modeling errors earlier in the process,

and hence reduce design costs. (3) The formal representation of the dependencies

among different types of models of the same product enabled us to automatically

check the consistency among different models, as well as to propagate changes from

one model to others.

In our approach, we also encountered several challenges. For example, representing and checking the mathematical dependencies in the current ontology representation languages is difficult, and hardly any automated reasoner s deal with this

aspect. In our approach, we were able to use the mathematical support available in

F LORA-2 , but a complete solution might need to use a hybrid approach, in which

different specialized reasoner s deal with different aspects of a model. For example,

a DL–reasoner might handle the consistency check and classification tasks, while a

constraint solver, or other mathematical algorithms deal with the numerical calculations in the model. Another challenge when modeling engineering systems that are

closed in nature is the Open World and Unique Name Assumptions in OWL, which

we discussed in Sect. 12.3.2.


T. Tudorache and L. Alani

While research is under way to address the current challenges, there are already

numerous advantages of using ontologies in the engineering domain, as demonstrated by their wider adoption in industry.


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Chapter 13

Leveraging Semantic Web Technologies

for Consistency Management

in Multi-viewpoint Systems Engineering

Simon Steyskal and Manuel Wimmer

Abstract Systems modeling is an important ingredient for engineering complex

systems in potentially heterogeneous environments. One way to deal with the increasing complexity of systems is to offer several dedicated viewpoints on the system

model for different stakeholders, thus providing means for system engineers to focus

on particular aspects of the environment. This allows them to solve engineering tasks

more efficiently, although keeping those multiple viewpoints consistent with each

other (e.g., in dynamic multiuser scenarios) is not trivial. In the present chapter, we

elaborate how Semantic Web technologies (SWT) may be utilized to deal with such

challenges when models are represented as RDF graphs. In particular, we discuss

current developments regarding a W3C Recommendation for describing structural

constraints over RDF graphs called Shapes Constraint Language (SHACL) which

we subsequently exploit for defining intermodel constraints to ensure consistency

between different viewpoints represented as RDF graphs. Based on a running example, we illustrate how SHACL is used to define correspondences (i.e., mappings)

between different RDF graphs and subsequently how those correspondences can be

validated during modeling time.

Keywords Consistency management ⋅ Multi-viewpoint systems engineering ⋅

Shapes Constraint Language ⋅ SHACL ⋅ Constraint checking ⋅ Ontology mapping ⋅

Ontology integration

S. Steyskal (✉)

Siemens AG Austria, 1210 Vienna, Austria

e-mail: simon.steyskal@wu.ac.at

S. Steyskal

Institute for Information Business, WU Vienna, 1020 Vienna, Austria

M. Wimmer

Institute of Software Technology and Interactive Systems,

TU Vienna, 1040 Vienna, Austria

e-mail: wimmer@big.tuwien.ac.at

© Springer International Publishing Switzerland 2016

S. Biffl and M. Sabou (eds.), Semantic Web Technologies for Intelligent

Engineering Applications, DOI 10.1007/978-3-319-41490-4_13


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