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4 Use Case: Integrating Heterogeneous Views on a Computer Network

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13 Leveraging Semantic Web Technologies for Consistency Management . . .


Fig. 13.7 (Adapted) Hybrid

ontology integration

approach (Wache et al. 2001)

In the following, we will describe first steps towards an approach for validating

the consistency of multiple integrated views expressed as ontologies using SWT and

most notably SHACL. Relating to the nomenclature defined in Chap. 6, we will use

the term mapping constraint for referring to SHACL shapes that are used to express

and validate mappings between concepts of an ontology.

13.4.1 Integration of Heterogeneous Viewpoints

As ontology integration architecture we have chosen the well known hybrid ontology

integration approach illustrated in Fig. 13.7. Each local ontology, which represents

a particular viewpoint of the system, is mapped to the same global ontology containing all overlapping concepts. Although we currently only focused on expressing

and validating mappings between ontologies and have not implemented any repair

mechanisms yet,9 due to potential complexity of change propagation caused by such

repairs (Eramo et al. 2012), we do not allow any direct mappings between individual


More precisely, consider the adapted version of our running example which

is illustrated in Fig. 13.8. In addition to ontologies O1−3 , a global ontology OG

was defined consisting of abstractions of overlapping concepts (i.e., oG:Device,

oG:Connection, and their respective properties oG:ID and oG:hasBandw). Furthermore, we have introduced the notion of weaving ontologies W1−3G which contain

mappings between individual views and their global abstraction.

13.4.2 Defining Mappings Between Viewpoint Definitions

using SHACL

It is difficult to keep a system as illustrated in Fig. 13.8 consisting of various local heterogeneous ontologies that are integrated with each other through a common global


cf. Sect. 13.5 for an outlook on future work.


S. Steyskal and M. Wimmer

Fig. 13.8 Adapted version of the running example, now including a global ontology (OG ) containing abstractions of overlapping concepts, and respective mappings between local ontologies

and their global abstraction (W1−3G )

representation of their overlapping concepts consistent. Since different viewpoints

may overlap, it is not sufficient enough to solely check consistency of each ontology

alone, but to verify the validity of mappings that connect overlapping concepts too.

As mentioned above, we introduced the notion of weaving ontologies which contain the mappings between individual views and their global abstraction. Each weaving ontology consists of one or more mappings of type wm:WeavingClass that relate

concepts of a view, i.e., local ontology, via wm:LHS to concepts of the global abstraction via wm:RHS. We have exemplified such mappings in Fig. 13.9, where weaving

13 Leveraging Semantic Web Technologies for Consistency Management . . .


Fig. 13.9 Weaving ontologies utilizing SHACL for defining mapping constraints between ontologies (top) and their validation on concrete data (bottom)

ontology W2G contains two concepts for defining mapping constraints between (i)

o2 :Connectable and oG :Device, and (ii) between o2 :Connection and oG :Connection

(cf. top part of Fig. 13.9).

Those exemplary mappings require that the ID of each mapped individual of type

o2 :Connectable is equal to the one of the individual of type oG :Device it is mapped

to. Furthermore, all mapped individuals of type o2 :Connection oG :Connection must

have the same values for their properties o2 :speed and oG :hasBandw, respectively. In

order to express those constraints in RDF, we define a SHACL shape for each individual mapping in the weaving ontology (cf. Listing 13.5). While wm:WeavingClass

Shape expresses the general requirement that each mapping must map exactly one

concept of the LHS to exactly one concept of the RHS,10 wm:Con2DevShape

states that each mapping of type wm:Con2Dev must relate individuals of type

o2 :Connectable to ones of type oG :Device and that the IDs of mapped individuals

must be equal (wm:Con2ConShape is defined respectively). To validate the equality of property values, we have defined a new constraint template called


As of now, we are only considering one-to-one mappings, but plan to support one-to-many and

also many-to-many mappings in the near future.


S. Steyskal and M. Wimmer

wm:EqualValuesConstraint that takes two arguments (i.e., the properties whose values should be compared against each other) as inputs and that reports a violation for

any property value of the first argument wm:property1 that is not also a value of

the second one wm:property2, and vice versa (cf. Listing 13.6 for its definition in


Listing 13.5 Definition of mapping concepts using SHACL

wm:WeavingClass a rdfs:Class .

wm:Con2Dev rdfs:subClassOf wm:WeavingClass .

wm:Con2Con rdfs:subClassOf wm:WeavingClass .

wm:Con2Dev1 a wm:Con2Dev ;

wm:LHS o2:Conable1 ;

wm:RHS oG:Dev1 .

wm:Con2Con1 a wm:Con2Con ;

wm:LHS o2:Conn1 ;

wm:RHS oG:Conn1 .


a sh:Shape ;

sh:scopeClass wm:WeavingClass;

sh:property [

sh:predicate wm:LHS ;

rdfs:label "LHS"^^xsd:string ;

sh:maxCount 1 ;

sh:minCount 1 ;


sh:property [

sh:predicate wm:RHS ;

rdfs:label "RHS"^^xsd:string ;

sh:maxCount 1 ;

sh:minCount 1 ;



a sh:Shape ;

sh:scopeClass wm:Con2Dev;

sh:property [

sh:predicate wm:LHS ;

sh:class o2:Connectable ;


sh:property [

sh:predicate wm:RHS ;

sh:class oG:Device ;


sh:constraint [

a wm:EqualValuesConstraint ;

wm:property1 o2:ID ;

wm:property2 oG:ID ;


13 Leveraging Semantic Web Technologies for Consistency Management . . .



a sh:Shape ;

sh:scopeClass wm:Con2Con;

sh:property [

sh:predicate wm:LHS ;

sh:class o2:Connection ;


sh:property [

sh:predicate wm:RHS ;

sh:class oG:Connection ;


sh:constraint [

a wm:EqualValuesConstraint ;

wm:property1 o2:speed ;

wm:property2 oG:hasBandw ;


Listing 13.6 Definition of custom constraint template wm:EqualValuesConstraint


a sh:ConstraintTemplate ;

rdfs:subClassOf sh:TemplateConstraint ;

rdfs:label "Equal values constraint" ;

rdfs:comment "Reports a violation for any value of property1 of the LHS

that is not also a value of property2 of the RHS and vice−versa, for

the same focus node." ;

sh:argument [

sh:predicate wm:property1 ;

sh:class rdf:Property ;

rdfs:label "property 1" ;

rdfs:comment "Represents a property of the concept pointed at by the

LHS of the respective mapping." ;


sh:argument [

sh:predicate wm:property2 ;

sh:class rdf:Property ;

rdfs:label "property 2" ;

rdfs:comment "Represents a property of the concept pointed at by the

RHS of the respective mapping." ;


sh:message "Value sets of wm:LHS/{?property1} and

wm:RHS/{?property2} must be equal";

sh:sparql """

SELECT $this ($this AS ?subject) ?predicate ?object



$this wm:LHS ?tmp .

?tmp $property1 ?object .


$this wm:RHS ?tmp2 .

?tmp2 $property2 ?object .


BIND ($property1 AS ?predicate) .



S. Steyskal and M. Wimmer



$this wm:RHS ?tmp .

?tmp $property2 ?object .


$this wm:LHS ?tmp .

?tmp $property1 ?object .


BIND ($property2 AS ?predicate) .


}""" .

If SHACL’s predefined constraint types are not sufficient enough to express certain types of constraints, one can always define its own constraint template which

can then be used like any other SHACL constraint. In Listing 13.6, we defined such

a custom constraint template (wm:EqualValuesConstraint). The constraint takes

two properties as arguments, i.e., values of wm:property1 and wm:property2, and

injects those properties values together with the respective focus node for which the

constraint is evaluated for into its predefined SPARQL query.11 Note that equivalence of property values represents only one possible dependency between concepts.

Due to the expressiveness of SPARQL, hence SHACL, any other kind of mapping

relationship could of course be expressed too.

If the example of Fig. 13.9 would have been validated against the shapes defined

in Listing 13.5, a SHACL validator would be able to detect the inconsistency of

mapping wm:Con2Con1 where the property value of o2:speed of o2:Conn1 is not

equal to the one of oG:hasBandw of oG:Conn1. Subsequently, validation results like

the one depicted in 13.4 would be reported by the validation engine and could guide

system engineers by resolving those inconsistencies.

Clearly, an approach based on Semantic Web technologies, such as the one we

discussed within this chapter, is capable of dealing with the previous identified challenges of Multi-Viewpoint Systems Engineering (cf. Sect. 13.1). While thoroughly

defined mappings between individual views and their global abstraction help to integrate overlapping models and as a result reduce ambiguities between concepts and

foster data exchange among models, constraints expressed as SHACL shapes provide

means to define undesired behavior, i.e., allow a SHACL engine to detect inconsistencies caused by changes to individual components of the integrated system.

13.5 Related Work

With respect to the contribution of this chapter, we will discuss three lines of work

more thoroughly: (i) approaches for handling inconsistencies in software engineering, (ii) approaches for bridging models and ontologies, and (iii) approaches for specifying SW-based management strategies for modeling artifacts.

11 To

distinguish variables that are bound by SPARQL from those that are bound by the SHACL

engine itself, the latter are prefixed by $ (e.g., $this, $property1, . . . ).

13 Leveraging Semantic Web Technologies for Consistency Management . . .


Handling Inconsistencies in Software Engineering. A large number of

approaches address the problem of integrating and synchronizing multiple viewpoints (Diskin et al. 2010). For example, works on synchronizing artifacts in software engineering which are highly influenced by approaches on multi-view consistency (Finkelstein et al. 1993; Grundy et al. 1998; Feldmann et al. 2014) using a

generic representation of modifications and relying on users to write code to handle

various types of modifications in all of the views. This idea influenced later efforts

on model synchronization frameworks (Ivkovic and Kontogiannis 2004; Johann and

Egyed 2004), in particular bidirectional model transformations (Song et al. 2011;

Xiong et al. 2007). Other approaches use so-called correspondence rules for synchronizing models in the contexts of RM-ODP and MDWE (Cicchetti and Ruscio 2008;

Eramo et al. 2008; Ruiz-Gonzalez et al. 2009). More theoretical approaches make

use of algebraic structures called lenses for defining bidirectional transformations

between models (Diskin et al. 2011a, b; Foster et al. 2008; Hofmann et al. 2011) or

based on Triple Graph Grammars (TGGs) (Schürr and Klar 2008). A more detailed

discussion on inconsistency management approaches exemplified by a mechatronic

manufacturing system design case study is provided in (Feldmann et al. 2015).

Bridging Models and Ontologies. Strategies to combine modeling approaches

stemming from MDE with ones related to the Semantic Web have been extensively

studied over the last years (Gasevic et al. 2009). There are several approaches for

transforming Ecore-based models to OWL and vice versa (Walter et al. 2010; Kappel et al. 2006a) and there exist approaches that allow defining ontologies in software modeling languages such as UML by using dedicated profiles (Milanovic et al.

2006). Moreover, some works have focused on identifying and combining benefits

of (conventional) models with those of ontologies (Parreiras et al. 2007; Parreiras

and Staab 2010). However, not only the purely structural part of UML is considered, some works also target the translation of constraints between these two technical spaces by using an intermediate format (Djuric et al. 2004). We build on these

mentioned approaches, e.g., for generating RDF graphs from models, but focus on

correspondence definitions and their respective validation.

Managing Models utilizing Semantic Web Technologies. The authors of (Parreiras et al. 2008) propose to use an ATL-inspired language for defining mappings between ontologies. Thus, unidirectional transformations are implementable

for ontologies as it is known for model transformations. In (Kappel et al. 2007),

ontology matching tools are utilized to search for correspondences between metamodels and for deriving relevant correspondences based on model transformations.

Another approach relates to the one presented in (Wagelaar 2010) which translates

parts of ATL transformations to ontologies for checking consistency of transformation rules, e.g., overlaps between rules in terms of overlapping matches. In our previous work (Bill et al. 2014) we follow this line of research, but considered bidirectional transformations specified in TGGs. Thus, in our translations to ontologies we

did not solely consider source to target transformations, but used SPARQL to encode

comparison and synchronization transformations. In this chapter, we illustrated a

similar approach for specifying correspondences, by leveraging SHACL, W3C’s

recent advancement in the area of constraint checking of RDF data. Since trans-


S. Steyskal and M. Wimmer

formation and synchronization transformation support will not be part of the main

SHACL specification, we will investigate means for extending SHACL’s functionality in this regards, e.g., by utilizing the theoretical foundation of TGGs in the future.

Finally, there are approaches shifting the task of defining and performing consistency

checks to SWTs. For instance, the authors of (Rieckhof et al. 2010) propose to use

the Semantic Web Rule Language (SWRL)12 for defining correspondences between

models, hence facilitate basic model synchronization. SPARQL and OWL have been

applied in (Feldmann et al. 2014; Kovalenko et al. 2014) to define and check consistency between different models used in engineering processes. Also more and more

companies (e.g., large infrastructure providers) adopted SWTs in the recent past to

deal with their highly complex use cases, such as knowledge management and enterprise data integration (Breslin et al. 2010; Schenner et al. 2014), especially due to

the increasing amount of tool support available (cf. Grünwald et al. 2014). To sum

up, to the best of our knowledge, we are not aware of any other approach utilizing

SHACL to define and check intermodel/viewpoint consistency constraints as of now.

13.6 Conclusion

In the present chapter, we have presented an approach for defining correspondences

between RDF graphs presenting different viewpoints on a system based on an emerging W3C standard called Shapes Constraint Language (SHACL). The correspondences are encoded as structural constraints which have to be satisfied by the referenced RDF data graphs in order to build up a coherent and complete system model.

We also discussed how SHACL may be used in hybrid integration approaches which

base on a global schema in order to reduce the overall complexity of bilateral viewpoint integrations. The declarative nature of SHACL allows to express intermodel/viewpoint correspondences without any further need of boilerplate code for validation execution and reporting.

As a next step, we plan to explore various possible means of extending SHACL

with rule support. Whereby one could e.g., define simple constraint repair patterns

that should be executed whenever their surrounding shape/constraint fails, or create mappings between ontologies based on rules that should be executed before the

actual validation process starts. In this context we also consider to apply the theory of Triple Graph Grammars for developing transformation and synchronization

support for SHACL. Finally, we plan to extend our mapping approach for supporting one-to-many and many-to-many mappings as well as to explore the capability

of SHACL to deal with structural heterogeneities between modeling languages and

viewpoints (Wimmer et al. 2010).



13 Leveraging Semantic Web Technologies for Consistency Management . . .


Acknowledgments This work has been partially funded by the Vienna Business Agency (Austria)

within the COSIMO project (grant number 967327), the Christian Doppler Forschungsgesellschaft,

and the BMWFW, Austria.


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