=Paper=
{{Paper
|id=Vol-395/paper-6
|storemode=property
|title=Bringing Ontology Awareness into Model Driven Engineering Platforms
|pdfUrl=https://ceur-ws.org/Vol-395/paper04.pdf
|volume=Vol-395
|dblpUrl=https://dblp.org/rec/conf/models/ZivkovicMK08
}}
==Bringing Ontology Awareness into Model Driven Engineering Platforms==
https://ceur-ws.org/Vol-395/paper04.pdf
Bringing Ontology Awareness into Model Driven
Engineering Platforms1
Srdjan Živković, Marion Murzek, Harald Kühn
BOC Information Systems GmbH, Wipplingerstrasse 1,
1010 Vienna, Austria
{srdjan.zivkovic, marion.murzek, harald.kuehn}@boc-eu.com
Abstract. In state-of-the-art of MDE platforms semantic technologies such as
ontologies are rarely used. Our aim is to understand the role of ontologies in
supporting model-driven engineering, in particular MDE platforms. MDE
platforms may benefit from semantic technologies in formal model semantics
and automated reasoning on different levels of the metamodelling architecture.
We present an ontology-aware MDE platform architecture and outline some
application scenarios where ontologies and automatic reasoning may bring
benefit to such platforms. Additionally, an example of using ontologies for
verification checks of mapping models in the course of metamodel composition
is illustrated.
Keywords: metamodelling, model-driven engineering (MDE), ontology-aware
architecture, ontology application scenarios.
1 Introduction
In state-of-the-art MDE platforms semantic technologies such as ontologies are rarely
used. Currently, such platforms focus on aspects such as metamodelling, metamodel
composition, model integration, and model transformation [1]. We are convinced that
MDE platforms may benefit from semantic technologies particularly in formalizing
semantics of models on different metamodelling levels, which in turn allows for
application of automated reasoning.
Based on this hypothesis, in this research-in-progress paper, we discuss first how a
MDE platform architecture may be extended to be considered as ontology-aware
(section 2). Referring to the introduced architecture, we describe some possible
application scenarios where ontologies and automatic reasoning may bring benefit to
such platforms (section 3). Out of these scenarios, we highlight in more detail an
example from the metamodel composition domain, to illustrate how an ontology-
based approach may enhance quality of process by supporting verification checks of
1
Acknowledgement: This research has been co-funded by the European Commission
and by the Swiss Federal Office for Education and Science within the 7th Framework
Programme project MOST N° 216691, http://most-project.eu.
metamodel mappings (section 4). Finally, we summarize the discussion and give
outlook for future work.
2 Architecture of Ontology-aware MDE Platforms
Metamodelling platforms are software environments providing means for the
management of models and metamodels. Usually, such model-aware platforms allow
for: (1) definition, storage and maintenance of models and modelling languages, (2)
execution of mechanisms working on models, metamodels and the meta-metamodel
and (3) guidance on how to apply a metamodelling language and/or modelling
languages together with corresponding mechanisms to produce metamodels and/or
models [2]. Besides these capabilities, metamodelling platforms need to meet other
functional and non-functional requirements such as multi-productability, web-
enablement, multi-client ability, adaptability, extensibility, scalability and
interoperability [3].
The architecture for MDE platforms may be seen as an incarnation of the generic
metamodelling platform architecture [2]. Furthermore, adding the ontology aspect, we
envision an enriched MDE platform architecture including semantic technologies as
depicted in fig. 1.
Ontology-aware MDE Workbench
Model, Ontology and Mechanism Editors
(Textual, Graphical etc.)
Access Service
Model Repository
Repository
Ontology
Model-aware and
Ontology-aware
Metamodel Library Mechanisms
Meta-metamodel
Persistency Service
Fig. 1. Logical Architecture for Ontology-aware Model Driven Engineering Platforms
The root core architectural element is the meta-metamodel which defines the concepts
available for the definition of modelling languages. Based on it, the metamodel
library contains metamodels of defined modelling languages. The metamodel library
conforms to the meta-metamodel and, in turn, forms the foundation of the model
repository, in which all models are stored.
As an extension to models on different levels, the ontology repository serves as
storage of the semantics of models, metamodels and the meta-metamodel. Semantics
can be formally described by using the notion of ontology [4]. Reasoning on
ontologies is part of the ontology-aware mechanisms.
Model and ontology editors are used for the definition and maintenance of models,
metamodels, and ontologies.
All mechanisms used for evaluating and using models are stored in the mechanism
base. A fundamental mechanism within MDE environments is model transformation.
Further important mechanisms are model/metamodel integration, comparison and
mapping mechanisms. The ability to manage different versions of models and
metamodels or its parts is another key characteristic of model-aware systems, which
should be enabled by means of model/metamodel versioning mechanisms. To support
syntactically and semantically correct modelling, validation mechanisms on models
and metamodels are used. Furthermore, querying mechanisms of ontology-enriched
models and metamodels are needed features to allow various model analyses.
Traceability of transformations, which should support guiding the software
development tasks, is another needed mechanism within MDE platforms. Mechanism
editors are used for definition, configuration and maintenance of mechanisms.
Guidance describes the application of modelling and metamodelling languages and
mechanisms. Particularly in context of MDE, this can include guidance on what to
consider when defining certain models or guidance on the decision what the next step
in a particular model-driven software development process would be. Guidance
information can be stored in process models or ontologies, and/or extracted out of
existing modelling artefacts and/or traceability links.
Persistency services support the durable storage of models, metamodels, and
ontologies. These services abstract from concrete storage techniques and permit
storing of modelling information in heterogeneous data sources such as files,
databases or web services.
Access services serve two main tasks. On the one hand they enable the open, bi-
directional exchange of all metamodel, model and ontology information. On the other
hand they cover all aspects concerning security such as access rights, authorization,
and en-/decryption.
An ontology-aware MDE workbench serves as a common environment for integrating
different editors.
3. Some Ontology Application Scenarios in MDE Platforms
The “ontology” concept, as the literature describes it, seems to enjoy as many
definitions as there are attempts to define it. In the course of this paper, we will
favour the one we believe to best match the context under consideration. Hence, we
understand an ontology as an explicit conceptual model extended by formal logic
based semantics [5]. Formal semantics expressiveness of ontological models is a de
facto advantage compared to conceptual models in software engineering i.e. MDE.
Model checking, model enrichment and dynamic classification are some of the
identified usage scenarios when thinking about marrying ontological and
metamodelling technical spaces [6].
In the following, we describe some scenarios, where ontologies may find its usage
within MDE platforms. We concentrate particularly on the following core elements of
the MDE platform (see section 2): the model, metamodel and meta-metamodel
element (section 3.1), the mechanism element (section 3.2) and the guidance element
(section 3.3). Each section starts by introducing a problem domain, followed by an
ontology application scenario description and finalized with a list of possible benefits
gained by using semantic technology.
3.1 Ontologies and Models on M1, M2 and M3 Level
Problem Domain: The 3-layer MDE architecture enables definition of modelling
languages, and based upon it, creation of models. The M3 model, i.e. the meta-
metamodel, defines the syntax of the metamodelling language which is used for
modelling metamodels on the M2 layer. Similarly, the M2 model, the metamodel,
constructs the syntax of the modelling language for the application domain used on
level M1. Even though the abstract syntax is structurally well defined, metamodelling
language (M3) and modelling languages (M2) lack well-defined semantics. Currently,
language semantics on M3 and M2 level may be expressed algorithmically in the core
implementation of the M3 model or in the M2 models. Another approach is to use a
declarative language, such as the Object Constraint Language (OCL [7]) to
additionally define the semantics of M3 and M2 models.
Furthermore, modelling task on M1 level requires adequate knowledge about the
subject under consideration. Such knowledge may currently be captured and reused
via reference models or patterns. However, more sophisticated mechanisms to
facilitate modelling task semantically are missing, which would prevent heterogeneity
problems, model ambiguity etc.
Ontology Application Scenario: The syntax-rich languages (M2 and M3 languages) in
MDE platforms may be extended by semantically more expressive ontology
languages, in order to take advantage of automated reasoning. There are different
approaches, which tackle the problem of converging languages from different
technical spaces, such as UML+OWL integration [8]. Having integrated languages,
their synergetic effect may be exploited. On the one side, automated reasoning may be
used for model consistency checks which may outperform existing solutions in terms
of semantic soundness. On the other side, ontology-based approaches may be used
both for the definition of modelling languages (M2) and their models (M1). Relying
on common domain ontologies in form of machine-readable and reusable domain
knowledge, quality of modelled solutions may be raised.
Gained Benefit: Semantically enriched modelling languages and models; machine-
readable formal semantics of models; enhanced quality of modelling solutions.
3.2 Ontologies and Mechanisms
Problem Domain: Mechanisms are applied on models residing on different
abstraction levels (M1, M2 M3 models). According to the abstraction level,
mechanisms may be generic, metamodel specific or hybrid. Generic mechanisms are
defined on the M3 level, thus being independent of languages defined on the M2
level; e.g. generic import/export interface for model exchange. Metamodel specific
mechanisms require explicit knowledge about metamodels, in order to work on their
underlying models on M1 level; e.g. business process model simulation mechanism.
Hybrid mechanisms are a combination of the previous two. They are generic, but they
are adaptable to specific metamodels; e.g., model transformation is a hybrid
mechanism which uses M3 level constructs to define transformation rules between
M2 models, which are, in turn, executed on models on M1 level. Other MDE
mechanisms such as model integration, metamodel composition or model comparison
fall into this category as well. The challenge of applying metamodel specific or hybrid
mechanisms lies in their configuration. For example, to enable a simulation on a
specific process language, mappings to generic process concepts need to be defined.
Similarly, the specification of metamodel mappings for hybrid mechanisms such as
transformation, metamodel composition or model integration is, in most cases,
performed manually (see section 4 for a detailed example).
Ontology Application Scenario: Ontologies may be applied to fill the formal semantic
gap towards support of automated configuration of metamodel-specific and hybrid
mechanisms. For instance, in the case of model transformations, this would mean e.g.
an ontology based model transformation approach. On the other side, metamodel
specific mechanisms such as simulation would benefit from ontologies, by relying on
e.g. generic, machine-readable process ontology. The prerequisite is that different
simulation-enabled languages, i.e. process modelling languages have to conform to
particular generic process ontology. Consequently, the process of configuring a
simulation mechanism for different process languages may be automated by inferring
mappings out of ontology.
Gained benefit: Reduced costs through automated configuration of mechanisms;
increased potential for reuse; low efforts for substitution and extensions of
mechanisms.
3.3 Ontologies and Guidance
Problem Domain: Guidance in the MDE context may include information on what to
consider when defining certain models, or information of the next step in the model-
driven software development process. Often, execution of a step within the software
process is influenced by many factors, such as pre and post-conditions, defined rules
etc.
Ontology Application Scenario: Ontologies and automated reasoning may leverage
execution of process models, by formalizing its semantics in the form of process
guidance ontologies that formalize rules, conditions and actions a software engineer
has to conduct in specific situations. This way, reasoning technology would infer the
next step within the process based on the process guidance ontology and by deriving
implicit knowledge from corresponding modelling artefacts.
Gained Benefit: Flexibility of process definitions; enhanced quality of guidance.
4 An Example: “Verification of Mapping Models”
Mappings define correspondences between elements of different models. Particularly,
metamodel mappings have an important role in the MDE approach by building
semantic bridges between different metamodels. They add knowledge about
integrative usage of different modelling languages, still leaving the integrating
languages independent. Bridging of metamodelling and ontology languages is done
via mappings [10]. Rules for MDA based model transformations may be built based
on existing mappings [11][12]. Furthermore, mappings are used as input for
metamodel composition rules by stating about structural-semantic relationships of
metamodel elements from different metamodels [13]. However, two problems arise
when the mappings are applied: First, discovery of mappings by means of metamodel
matching is a complex task, being a tedious work when executed manually and a
challenging task for semi-automatic identification [14]. Second, mappings are
managed as models and are built based on a mapping language. Thus, mappings need
to be verified against their syntax and defined semantics. This may imply not only
checking the mapping model, but also crossing the model boarder and diving into the
semantics of metamodels being integrated, in order to verify certain mapping
statements against e.g. cyclic generalization relationships, multiple inheritances,
redundancy etc.
DO1: Process Ontology DO2: Organizational Ontology
Verification check of MM3
Result: Inconsistent state
due to multiple inheritance
«class»
«class»
Participant
Participant
MM3: MM1 + MM2 + MapMod
MM1: BPMN MapMod MM2: OrgMod
parent unit «class» «class»
«abstract «class» «class»
«abstract class»
class» Org
Graphical Org Unit/Entity
Unit/Entity Role
Role
Graphical Object
Object «class»
«class» head of
11 Org
Org Unit
Unit
==
belongs to
«class»
«class» «class»
«class» 22 «attribute»
«attribute»
Participant
Participant Task
Task «class»
«class» Performers
Performers
Actor
Actor
has role
«class» «class» «attribute» «class» DO: Domain ontology
«class» «class» «attribute» 33 «class»
Role Entity Performers == Role MM: Metamodel
Role Entity Performers Role
== - Equivalence mapping
- Generalization mapping
Figure 2 Ontology-aware Modelling and Verification of Mappings for Metamodel Composition
Figure 2 illustrates the simplified case of a metamodel composition using mappings.
Let us assume that the MDE platform supports two modelling languages, e.g. BPMN
[15] and the ADONIS Language for Organizational Modelling [16], separately. The
envisioned integrated metamodel (MM3) should exhibit characteristics of a business
process modelling language extended by the concepts for modelling organizational
structures (in Fig. 2 as MM1 and/or MM2). A proposed approach is to assemble
existing metamodels by utilizing metamodel mappings. The manual process of
metamodel matching results in three identified mappings (Fig. 2, 1, 2 and 3), which
are candidates for integration points. At first glance, everything seems correct, as the
defined mappings conform to the mapping language which uniquely state correlations
between elements. However, after generating the integrated metamodel (MM3) (see
[13] for detailed integration rule definitions) a verification check founds an
unconformity against the meta-metamodel, which disallows multiple inheritance of
elements. The drawback of the solution is, that the verification of a mapping model
may only be done after having generated the integrated metamodel, since such cross-
model correlations may not be examined in the mapping design time. Here, ontologies
may be used guiding both metamodelling and modelling of metamodel mappings. If
metamodels are designed with the support of corresponding ontologies, as depicted in
the Figure 2, not only the metamodel matching process may be enhanced, but also an
early verification of a mapping model in design time against integrated semantics
stemming from both problem domains may be possible. In addition, large scale
metamodel integration scenarios may especially benefit from the ontology based
composition approach reducing ambiguities and improving quality of modelling
solutions.
5 Summary and Outlook
In this research-in-progress paper we presented a possible extension of the generic
architecture for MDE platforms [3] towards an ontology-aware MDE platform. Based
on this architecture and its core elements, possible ontology application scenarios
have been discussed. Some of their expected benefits are:
• Semantic-enriched models (on M1, M2, and M3 level).
• Machine-readable formal semantics of models.
• Semi-automated configuration of mechanisms.
• Increased potential for reuse of mechanisms.
• Flexibility of process definitions for guidance.
We are currently working on the refinement of the presented ontology-aware MDE
platform architecture to support first prototype implementations. This includes the
application of ontologies for enhanced software process guidance.
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