An Ontology-based Approach for Enabling Linked Data Capabilities to

MOF Compliant Models

Fernando Silva Parreiras

, Gerd Gr

oner

and Tobias Walter

Faculty of Business Sciences, FUMEC University, Av Afonso Pena 3880, 30130009 Belo Horizonte, Brazil

Institute for Web Science and Technologies – WeST, University of Koblenz-Landau, 56072 Koblenz, Germany

Keywords:

OWL, Linked Data, Software Engineering, UML, MOF, Model Driven Engineering.

Abstract:

In the software development process, there are standards for general-purpose modeling languages and domain-

speciﬁc languages, capable of capturing information about multiple views of systems, e.g., static structure and

dynamic behavior. In a networked and federated development environment, modeling artifacts need to be

linked, adapted and analyzed to meet the information requirements of multiple stakeholders. In this paper,

we present an approach for linking, transforming and querying MOF-compliant modeling languages on the

web of data. We deﬁne structural mappings between MOF and OWL and propose the usage of semantic web

technologies for linking and querying software models.

1 INTRODUCTION

In a model-driven architecture, software engineers

rely on a variety of languages for designing soft-

ware systems. As different stakeholders need differ-

ent views of the information, the software develop-

ment environment needs to encompass a myriad of

general-purpose and domain-speciﬁc languages with

complementary and overlapping applications.

Since it is hard to capture all aspects of software

into one single model, contemporary model-driven ar-

chitectures include numerous notations to be used ac-

cording to the software development task. The in-

evitable usage of multiple languages leads to unman-

ageable redundancy in developing and managing the

same information across multiple artifacts and, even-

tually, information inconsistency. With the growing

demand for networked and federated environments,

the question arises about what and how existing web

standards can help existing modeling standards in ful-

ﬁlling the requirements of a web of models.

Semantic web technologies (Antoniou and van-

Harmelen, 2004) and linked open data (LOD) prin-

ciples (Bizer et al., 2008) allow any kind of data to

be represented, identiﬁed, linked and formalized on

the web. The same data can be adapted for use with

another purpose, according to the software engineer’s

perspective.

The interest on this topic has motivated the Ob-

ject Management Group (OMG) to issue a request

for proposal aiming at deﬁning a structural mapping

between Meta Object Facility (MOF) models and

Resource Description Framework (RDF) representa-

tions (OMG, 2009b). This mapping should make

possible to apply LOD principles into MOF compli-

ant models and to publish MOF compliant models as

LOD resources.

In a collaborative environment, developers cre-

ate architectures with information expressed in mul-

tiple modeling languages. These languages should be

grounded with stronger semantics than MOF can pro-

vide today.

The Web Ontology Language (OWL) (W3C OWL

Working Group, 2009) provides a powerful solu-

tion for formally describing domain concepts in net-

worked environments. OWL is part of the semantic

web stack, is compatible with RDF and with LOD

principles. OWL’s objective is to provide evolution,

interoperability, and inconsistency detection of shared

conceptualizations.

Although transformations from the MOF-like

metamodels to OWL have been proposed before (Ga-

sevic et al., 2004), addressing the aforementioned

problems requires a coherent framework comprising

techniques not only for transforming but for extend-

ing, linking and queries MOF compliant models.

In this paper, we describe such a framework for

supporting the interaction and interrelationships of

modeling languages in distributed software modeling

environments. We present our contribution as fol-

126

Silva Parreiras F., Gröner G. and Walter T..

An Ontology-based Approach for Enabling Linked Data Capabilities to MOF Compliant Models.

DOI: 10.5220/0004006201260131

In Proceedings of the 14th International Conference on Enterprise Information Systems (ICEIS-2012), pages 126-131

ISBN: 978-989-8565-11-2

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

lows: Section analyzes the requirements to be ad-

dressed. Section 3 describes the building blocks of

our approach. We analyze the related work in Section

4. Section 5 ﬁnishes the paper.

2 REQUIREMENTS

Based on demand identiﬁed in OMG’s request for

proposal (OMG, 2009b), we identify three funda-

mental requirements for realizing a linked-open data

environment in model-driven software development:

RQ1: Extend Modeling Languages. Metamodeling

languages based on EMOF do not provide ca-

pabilities for integrating conforming modeling

languages, or for deﬁning constraints for these

languages. Here, an additional (constraint-

and expression-) language that provides more

expressiveness than usual modeling languages is

required. It should provide constructs to integrate

languages (e.g. by asserting that concepts are

equivalent).

RQ2: Identiﬁcation of Equivalences. For semi-

automatically integrating modeling languages, a

mechanism is required that allows for identifying

concepts that are equal over languages and that

are then candidate for merging. The result is a

combined metamodel.

RQ3: Integration Management. To achieve an in-

tegrated representation of several modeling lan-

guages, we need more that one technique. Firstly,

languages and conforming models must be trans-

formed into the same representation. Further they

must be linked for reasoning and querying over

languages and models.

Addressing these requirements allows for achiev-

ing the following features:

Consistent View over Multiple MOF Models.

Based on an integration of multiple (MOF-based)

languages and constraints describing depen-

dencies between languages, we get a consistent

view over multiple models conforming to those

languages.

Integrated Well-formedness Constraints. Based on

the extended modeling language, we are able to

deﬁne integrated well-formedness constraints in

an embedded manner. This means that language

designers should be able to use the metamodeling

language (e.g. EMOF) of choice and still anno-

tate elements of language metamodels to deﬁne

constraints.

Dependency Checking. Once languages are inte-

grated, apart from consistency checking (check-

ing if models conform to the language meta-

model), it is possible to check dependencies

across language models.

Query Answering. Based on underlying formal se-

mantics and constraints, it is possible to query

models and inferences. It is possible to ask and

answer the following question: What is the effect

of updating the plug-in pellet? How much do I

have to test? Furthermore we are able to query

for impacts of model elements to others (Impact

Analysis) and thus can identify e.g. cyclic depen-

dencies or other unexpected consequences.

3 INTEGRATING MOF AND OWL

In this section, we describe how we exploit ontology

technologies to represent the semantics of modeling

languages with OWL. We present how we link mod-

eling languages with OWL, how we transform MOF-

based models in OWL and how we query and manage

links between modeling languages.

Our approach consists of (1) optionally extending

MOF metamodels with OWL annotations, (2) trans-

lating MOF metamodels and models into OWL con-

cepts, properties, individuals and property assertions,

(3) using ontology matching to link same classes and

individuals across metamodels and (4) write and exe-

cute queries over multiple MOF models.

3.1 OWL for Conceptual Modeling

Despite features of OWL like shared conceptualiza-

tions, support to evolution, interoperability and con-

sistency detection, OWL provides multiple means for

describing classes. It relies on class and property ex-

pressions to construct class descriptions. These de-

scriptions represent sets of individuals by specifying

conditions on the individuals’ properties.

In the following list, we present examples us-

ing OWL2 syntax of constructs used to link MOF

metamodels with OWL. In Line 1, we describe the

equivalence of a UML Activity and BpmnDiagram.

The equivalence of the set of individuals of the class

OpaqueAction and the set of individuals of the class

Activity where the property activityType is set to Task

in the BPMN metamodel is deﬁned in Line 2. Lines 3

and 4 characterize the property general of the UML

metamodel as irreﬂexive and transitive. In Line 5,

we derive a new property in the BPMN metamodel

based on a property chain, i.e., a composition of the

properties outgoingEdges and target are properties of

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sucessorActivities. For instance, if individual x out-

goingEdges y and y target z than x sucessorActivities

z. Similarly, a property chain ancestorNodes for the

UML metamodel is deﬁned in Line 6. The equiva-

lence of the deﬁned property chains is expressed in

Line 7.

Listing 1: Linking MOF metamodels with OWL.

1 Eq u i v a l e n t C l a s s e s ( uml : A c t i v i t y

bpmn : BpmnDiagram )

E q u i v a l e n t C l a s s e s ( uml : O p a q u e A c t i o n

ObjectSomeValuesFrom

( bpmn : a c t i v i t y T y p e bpmn : Task ) )

I r r e f l e x i v e O b j e c t P r o p e r t y

( uml : g e n e r a l )

T r a n s i t i v e O b j e c t P r o p e r t y ( uml : g e n e r a l )

5 SubO bjec tPro pertyOf (

ObjectP r o p e r t yChain

( bpmn : o u t g o i n g E d g e s bpmn : t a r g e t )

bpmn : s u c e s s o r A c t i v i t i e s )

Sub Obje ctPr oper tyOf (

ObjectP r o p e r t yChain ( uml : o u t g o i n g

uml : t a r g e t ) uml : s u c e s s o r N o d e s )

Sub Obje ctPr oper tyOf (

ObjectP r o p e r t yChain ( uml : i n c l u d e

uml : a d d i t i o n ) uml : i n c l u d e U s e C a s e s )

Sub Obje ctPr oper tyOf (

ObjectP r o p e r t yChain

( O b j e c t I n v e r s e O f ( uml : a d d i t i o n )

uml : i n c l u d i n g C a s e )

uml : i n c l u d i n g U s e C a s e s )

E q u i v a l e n t O b j e c t P r o p e r t i e s

( uml : s u c e s s o r N o d e s

bpmn : s u c e s s o r A c t i v i t i e s )

At the model level, developers can link mod-

els (metamodel instances) with OWL constructs.

The SameIndividual axioms allow to deﬁne the

equality of individuals in order to assert that in-

stances of different metamodels are the same.

For example, if we have a UML package called

west.twouse.backend, we can assert that this package

is the same as the Java package with the same

name – SameIndividual(uml:west.twouse.backend

java:west.twouse.backend).

Additionally, OWL2 provides other standard con-

structs that can help to enrich MOF metamodels and

to extend its expressiveness (RQ1). Object property

axioms aim at characterizing object properties like the

deﬁnition of subproperty relations and the expression

of reﬂexive, irreﬂexive, symmetric, asymmetric and

transitive properties. Moreover, equivalent and dis-

joint axioms allow explicit statements on the equiva-

lence and disjointness of classes and properties.

Another beneﬁt of using OWL for metamodel an-

notations is the monotonicity of OWL, i.e., adding

further axioms to a model does not falsify existing ex-

tailments. Thus, OWL provides a non-invasive way to

integrate same or similar concepts over modeling lan-

guages (RQ3).

3.2 Extending MOF Models

In order to extend the expressiveness of Ecore meta-

models, we use a annotation proﬁle. In this paper,

we consider Ecore as an implementation of EMOF

for the Eclipse Modeling Framework (EMF) (Budin-

sky et al., 2003). In an Ecore-based metamodel, every

element is an EObject and therefore can adopt EAnno-

tations (Budinsky et al., 2003). The process of build-

ing and extending ECore-based metamodels consists

of three steps:

1. To build the metamodel by using the Ecore

metametamodel.

2. To annotate the metamodel in order to make it

more understandable for OWL annotations, which

are created in step 3. This means that each ele-

ment in a metamodel gets an annotation, so that

it represents an ontology element. For example:

EClasses are annotated by #Class to represent an

OWL Class; EReferences are annotated by #Ob-

jectProperty to represent an OWL Object Prop-

erty, or EAttributes are annotated by #DataProp-

erty to represent an OWL Data Property. This step

can be accomplished automatically, since we have

to iterate over all elements in the metamodel.

3. To create annotations to deﬁne additional con-

structs that usually are not expressable in Ecore-

based metamodels. In general, we can create

additional OWL annotations for Class Axioms,

Object Property Axioms, Data Property Axioms,

Class Expressions, Object Property Expressions

and Data Property Expressions.

For example, we can deﬁne the concepts of two

classes as equivalent by creating a new annotation

#EquivalentWith, which refers (by references) to

those annotations of Ecore classes. Alternatively,

we can deﬁne references as transitive, creating a

#Transitive annotation which refers to the anno-

tation of the reference that should be declared as

transitive.

3.3 Mapping MOF and OWL

Ecore and OWL have a lot of similar constructs,e.g.,

classes, attributes and references. To extend the ex-

pressiveness of Ecore with OWL constructs, we need

to establish mappings between the Ecore constructs

and OWL constructs.

Based on these mapping, we develop a generic

transformation script to transform any Ecore Meta-

model/Model into OWL TBox/ABox – OWLizer.

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M1 Model

M2 Metamodel

Metamodel to TBox

Ontology

M3 Ecore

Metametamodel

conformsTo

OWL Metamodel

conformsTo

Transformation

conformsTo

Model to ABox

Language User

Language

Designer

OWLizing

Annotations

Figure 1: OWLizer.

Fig. 1 depicts the conceptual schema of transforming

Ecore into OWL.

The four lanes, Actor, Ecore, Model Transforma-

tion and OWL show three modeling levels according

to the OMGs Four layered metamodel architecture:

the metametamodel level (M3), the metamodel level

(M2) and the model level (M1). Vertical arrows de-

note instantiation whereas the horizontal arrows are

transformations from the Ecore modeling space to the

OWL modeling space. The ontology consists of the

TBox that represents the metamodel and the ABox

which expresses the model.

The language designer annotates the existing

metamodel with Ecore annotations for OWL. These

annotations work in the same way as the UML proﬁle

for OWL. Thus, the language designer can create new

attributes and references and deﬁne axioms for them

in OWL by using reference properties. It is possible

to add further modeling axioms in OWL for the model

elements, e.g., expressing two artifacts as identical.

A model transformation takes the metamodel and

the annotations as input and generates an OWL on-

tology where the concepts, enumerations, properties

and data types (TBox) correspond to classes, enu-

merations, attributes/references and data types in the

metamodel. Another transformation takes the model

created by the language user and generates individ-

uals in the ABOX of the same OWL ontology. The

whole process is transparent for language users.

The structural mapping from Ecore-based meta-

models and models to OWL makes MOF models

available as federated, accessible and query-ready

LOD resources. Models can be transformed into a

common representation in OWL ontologies accord-

ing to this structural mapping. Having models repre-

sented in OWL ontologies, one might connect these

ontologies and process these ontologies in a federated

way. Thus, the resulting OWL representations ad-

dress the requirement RQ3 (Integration Management:

transform, link and query models) deﬁned in Sect. 2.

3.4 Ontology Matching

In a model driven paradigm, resources expressed us-

ing distinct modeling languages must be reconciled

before usage. In this paper, we illustrate some of the

multiple ontology matching techniques. For a deeper

understanding of this topic, please refer to (Euzenat

and Shvaiko, 2007).

Ontology matching is the discipline responsible

for studying techniques for reconciling multiple re-

sources on the web. It comprises two steps: match and

determine alignments and the generation of a proces-

sor for merging and transforming. Matching identiﬁes

the correspondences. A correspondence for two on-

tologies A and B is a quintuple including an id, an en-

tity of ontology A, an entity of ontology B, a relation

(equivalence, more general, disjointness) and a conﬁ-

dence measure. A set of correspondences is an align-

ment. Correspondences can take place at the schema-

level (metamodel) and at the instance-level (model).

Matchings use multiple criteria: name of enti-

ties, structure (relations between entities, cardinal-

ity), background knowledge, e.g., existing ontolo-

gies or wordnet. Techniques can be string-based or

rely on linguistic resources. Furthermore, matchings

are established according to the structures that are

compared: (i) Internal structure comparison: this in-

cludes property, key, datatype, domain and multiplic-

ities comparison. (ii) Relational structure compari-

son: the taxonomic structure between the ontologies

is compared. (iii) Extensional techniques: extensional

information is used in this method, e.g., formal con-

cept analysis

The correspondences depend on the applied cri-

teria and technique. For example, if we apply string

matching, it generates a false positive correspondence

between the UML Activity and the BPMN Activity.

However, if we apply structure-based techniques and

analyze the structure of the UML class Action and the

BPMN class Activity, we see that both have similar

structures (both have one superclass with two associa-

tions with the same cardinalities). However, the UML

class Action is abstract and the BPMN class Activity

is concrete. So, we could assert that the class Activity

is a subclass of class Action.

Automatic matching techniques should be assisted

by domain experts because of existence of false pos-

itive matches. For example, the correspondence be-

tween BpmnDiagram and UML Activity is hard to

catch automatically. With the ontology matching ca-

pabilities, we address the requirements RQ2 (Identi-

ﬁcation) and RQ3 (Integration Management: trans-

form, link and query models) by identifying corre-

spondences for creating links between (meta) models.

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3.5 Querying MOF-models

SPARQL is the W3C standard query language

for RDF graphs, which is a triple-based lan-

guage (Prud’hommeaux and Seaborne, 2007). Writ-

ing SPARQL queries for OWL can be time-

consuming for those who work with OWL ontologies,

since OWL is not triple-based and requires reiﬁcation

of axioms when using a triple-based language. There-

fore, we proposed SPARQLAS, a language that al-

lows for specifying expressions that rely on inferences

over OWL class descriptions (Schneider, 2010). It is

a seamless extension of the OWL syntax for query-

ing OWL ontologies. SPARQLAS enables using vari-

ables wherever an entity (Class, Datatype, Object-

Property, DataProperty, NamedIndividual) or a literal

is allowed.

SPARQLAS queries are translated into SPARQL

queries and executed by any SPARQL engine that

supports graph pattern matching for OWL2 entail-

ment regime (Glimm and Parsia, 2010). SPARQLAS

queries operate on both the schema level (M2 meta-

modeling level) and on the instance level (M1 model-

ing level). For example, Listing 2 show a SPARQLAS

query about use cases that include other use cases. In

this example, we ask about the individuals ?x whose

type is an anonym class where the transitive property

includeUseCase has as value some use case.

Listing 2: Use Cases that includes some other use case.

1 Namespace : uml = < . . . uml2 / 3 . 0 . 0 /UML#>

S e l e c t ? x

Where :

? x t y p e ( UseCase and

i n c l u d e U s e C a s e some Use Case )

With SPARQLAS, we cover requirement RQ3

(Integration Management: transform, link and query

models) by providing distributed query facilities for

models and metamodels represented in OWL.

3.6 Proof of Concept

We have realized our approach in the TwoUse Toolkit

and it is available for download on the project web

site

. The TwoUse toolkit uses the Eclipse Modeling

Framework (Steinberg et al., 2009) and comprises (a)

a set of model transformations, (b) a set of textual and

graphical editors, and (c) a set of reasoning services,

e.g., classiﬁcation, realization, query answering and

explanation.

http://twouse.googlecode.com/.

3.7 Limitations

Depending on the strategy for matching and align-

ing ontologies there is the possibility of false positive

matches. For example, if we use a string based match-

ing technique, we match OWL classes with the same

name as equivalent, although the two concepts might

not be semantically the same. Thus, domain experts

must be involved to proof the results of matching and

alignments.

A further limitation of our approach regards the

quality of metamodels. There are metamodels that are

designed for generating code. We have seen that, in

order to ease the implementation, references in ECore

can be declared as containment (instead of imple-

menting reference resolvers). This leads to the situa-

tion where multiple objects represent the same model

elements. Here ontology matching techniques are

powerless, since models and the ontology extracted

from these models consist of elements having same

names or properties.

A further issue in evaluating our approach is the

missing implementations of technical spaces. In our

case, all implementations are under the roof of Ecore,

an implementation of EMOF. In the future we would

like to do similar tests under (C)MOF.

Moreover, linking and aligning metamodels and

models means ﬁrstly transforming (owlizing) ev-

erything into one ontological representation and

secondly processing ontology matching algorithms,

which can cause performance issues.

4 RELATED WORK

The integration of software artifacts has been the topic

of works including (Antoniol et al., 2005; Mockus

and Herbsleb, 2002). However, these approaches pre-

sented dedicated extractors for speciﬁc systems,e.g.,

bug tracking and version control but not for soft-

ware models. Moreover, neither of these approaches

presents formats for publishing data suitable to the

linked-data approach, i.e. they do not share the princi-

ples of interoperability for connecting federated soft-

ware models across the Web.

Kiefer et al. (Kiefer et al., 2007) and Iqbal et

al. (Iqbal et al., 2009) explored semantic web ap-

proaches for transforming software artifacts such as

data from version control systems, bug tracking tools

and source code into linked data. Both approaches

use artifact-speciﬁc extractors and thus work only for

a ﬁxed number of software artifacts. We propose a

generic approach for transforming and managing any

MOF metamodel in a web format.

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The OMG ontology deﬁnition metamodel (OMG,

2009a) speciﬁes mappings between OWL and UML.

In this paper, we present a general approach for map-

ping arbitrary MOF models into OWL. We provide

the means to express any MOF metamodel in its

equivalent OWL.

The OMG Request For Proposal for MOF to

RDF Structural Mapping in support of Linked Open

Data (OMG, 2009b) aims at deﬁning a structural

mapping between OMG-MOF models and RDF. This

work is a response to this request. We propose an

approach to be used as a benchmark for future pro-

posals.

5 CONCLUSIONS

In this paper, we propose an approach to enable anal-

ysis, federation, querying and rationalization of mod-

els expressed in MOF compliant languages, including

OMG standards and domain-speciﬁc languages. We

demonstrate how our approach addresses the require-

ments of an architecture ecosystem (OMG, 2009b).

The contribution in this paper shows that the usage

of the Ontology Web Language for specifying meta-

models is a viable solution to achieve interoperabil-

ity and shared conceptualizations. The role of OWL

is not to replace MOF or the Object Constraint Lan-

guage because OWL addresses distinct requirements,

specially concerning networked environments. OWL

should compose the spectrum of software modeling

languages in a uniﬁed architecture.

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