=Paper=
{{Paper
|id=Vol-2234/paper4
|storemode=property
|title=Design Metamodels for Domain-Specific Modelling Methods using Conceptual Structures
|pdfUrl=https://ceur-ws.org/Vol-2234/paper4.pdf
|volume=Vol-2234
|authors=Wilfrid Utz
|dblpUrl=https://dblp.org/rec/conf/ifip8-1/Utz18
}}
==Design Metamodels for Domain-Specific Modelling Methods using Conceptual Structures==
https://ceur-ws.org/Vol-2234/paper4.pdf
Design Metamodels for Domain-Specific
Modelling Methods using Conceptual Structures
Wilfrid Utz∗
Department of Knowledge Engineering,
Faculty of Computer Science, University of Vienna,
Währingerstr. 29, 1090 Vienna, Austria
wilfrid.utz@univie.ac.at
https://informatik.univie.ac.at/ke/
Abstract. Enterprises nowadays operate in fast-changing environments
and need to adapt dynamically to new circumstances. This impacts the
way how enterprise information systems are analysed, designed and im-
plemented. Conceptual modelling methods experience a constant evolu-
tion nowadays. The methods are re-constructed continuously to reflect
the changing application/industrial domain. It is therefore required to
provide additional design support to realise domain specific modelling
methods and more precisely their underlying metamodel.
The goal of this research project is to enable the knowledge representa-
tion of a metamodel to support its design process. Building upon the as-
sumption that conceptual structures within a metamodel exist, a knowl-
edge representation framework is proposed using conceptual graphs as
the mathematical baseline. The proposal will be evaluated in a labora-
tory setting, applied on metamodel design challenges observed.
Keywords: Metamodelling · Knowledge Representation · Conceptual
Structure · Domain-specific Modelling Methods.
1 Introduction
Enterprises operate today in fast changing environments. External factors influ-
ence and disrupt their business models; technology advances at a rapid pace and
changes the way organisations offer their products and services; legal/regulatory
requirements need to be reviewed continuously and respected.
All these factors influence the way information systems are designed, anal-
ysed and used. Enterprise modelling methods are an established means for the
conceptual analysis, design and implementation of complex enterprise systems.
They models realised using these methods provide value as a documentation
∗
Supervisor: o. Univ.-Prof. Prof.h.c. Dr. Dimitris Karagiannis
Copyright 2018 for this paper by its authors.
Copying permitted for private and academic purposes.
J. Ralyté and Y. Wautelet (Eds.): PoEM 2018 Doctoral Consortium Papers, pp. 47-60, 2018.
means and to support understanding by human beings[22], formalise the knowl-
edge base and enable knowledge management and knowledge engineering[18].
In the field of conceptual modelling, we can distinguish between general pur-
pose approaches, that aim to provide a standardised conceptual modelling foun-
dation for a specific application field and domain-specific modelling methods
that target specific interpretation and operation needs of an enterprise. The
metamodel reflects these aspects at the core of any domain-specific modelling
method: model-based operations define the concepts required to syntactically
and semantically describe the organisation (or a subset of it), and enable model
processing to create value.
Within this research project, domain-specific modelling methods are in fo-
cus, specifically targeting the challenge of designing ”adequate” metamodel to
support dynamic (in the sense of quickly evolving) and complex (vertically and
horizontally integrated) environments. An initial understanding of ”adequate”
in relation with a domain-specific metamodels has been derived from [17, p. 5]
where usefulness defines scope of the abstraction performed in the design pro-
cess. Requirements derived during the design and runtime aspects influence the
design process. As a solution proposal, it is intended to extend the notion of
metamodel towards a conceptual structure. The term conceptual structure is
understood and closely linked to the work performed by Sowa in [26] as a logic-
based knowledge representation technique. Derived from linguistics, a conceptual
structure allows the representation of concepts in terms of a small number of
conceptual primitives [21] that can be expressed mathematically as a conceptual
graph. The assumption in this research project is that conceptual structures in
metamodels exist and can support the design process of domain-specific mod-
elling methods.
2 Problem Description
Problem Observed. The design process for metamodels as the core element
of a modelling method is observed as a challenging issue: the task to derive a
consistent metamodel that is adequate for a specific domain and application, en-
ables model value and provides interpretation support for stakeholders involved,
requires the metamodel engineer requires conceptual, technological and domain-
specific knowledge to base design decisions upon and weight in and compromise
on different interests.
Observing current practices in the field of metamodelling, these knowledge
challenges are introduced using the example of a development process observed
graphically shown in Fig. 1.
1. Understand requirements: in order to develop an adequate and useful meta-
model, the requirements for knowledge operations have to be captured and
understood. Knowledge on the application and industrial domain is required
to trigger the design process, e.g. simulation of manufacturing processes,
dependency analysis of community networks, verification of deadlocks in
48
Fig. 1. Example: Metamodel Development Approach
automated business processes, integration of metamodel on different formal-
isation levels into a consistent state for enterprise architecture management
in a given industrial domain.
2. Select a meta-modelling technique: based on these requirements, the appro-
priate meta-modelling technique has to be selected. The technique provides
generic constructs to develop a metamodel, construction principle and usage
patterns and consequently enables specific functionalities e.g. code gener-
ation, reasoning techniques on ontological metamodels, deduction in logic
based environments. The functionalities offered have to be mapped to the
requirement as a fundamental design decision that has to be taken. Knowl-
edge on specific techniques applicable is needed.
3. Design the metamodel: having accomplished these 2 prerequisites, the meta-
model is designed in conformance with the selected metamodelling approach.
The design techniques are typically adjustment, mapping or re-use patterns
and the resulting metamodel is strongly influenced by the expertise and cre-
ativity of the metamodel engineer in the overall approach, design decisions
taken and specific usage techniques.
4. Enable metamodel operations: knowledge operations realise the value of mod-
els, enabled by the corresponding metamodel. These operations are estab-
lished functionalities like composition (e.g. in case of sliced metamodel as
discussed in [4]), binding of model processing algorithms through reference
49
alignment, transformation and semantic lifting [13] and operate potentially
on the structure and semantic provided by the metamodel. This steps verifies
the applicability of the metamodel.
These knowledge-based challenges strongly influence the efficiency to estab-
lish an adequate design of a metamodel. The selection of a meta-modelling tech-
nique is currently driven by the metamodel engineers knowledge on a specific
approach rather than the requirements elicited; during the process adjustments
are performed to overcome this initial selection issue. It is assumed that these
adjustment have an impact on the usefulness of the resulting metamodel. As a
description framework of specific, existing metamodels is not available, a re-use
of results is limited. This results in a re-implementation of similiar structures
(using the same or a different technique).
The motivational example below showcases the observation. Different meta-
modelling techniques have been applied to represent the same metamodel re-
quirement from a structural point of view: the sequence definition of a business
process. The application of different approaches results in varying functionality
based on the design and technique used.
Motivational Example: BPMN 2.0. The observation on the construction
of meta-models and its dependency on the meta-modelling approach is visually
shown in Fig. 5. The Business Process Model and Notation (BPMN) 2.0 [24] has
been selected to exemplify the issue as this standard is extensively discussed in
research in the past years. The example has been selected as it nicely demon-
strates how different metamodelling techniques impact conceptualization results.
Driven by required capabilities of the metamodel, the results differ even though
the same concept is designed.
The representation shows the aspect of sequence flows (highlighted in red/dashed
line) of the BPMN metamodel and how it has been designed using three different
meta-modelling techniques: a) the formal specification from the BPMN 2.0 stan-
dard document using class diagrams [24, p. 144], b) an ontology-based approach
to conceptualise the specification [25, p. 140] and c) a logic-based representation
using rules mapped to petri net constructs (introduced in [15, p. 51] extended in
[14]). The selected meta-modelling does not only impact the way concepts are
represented, but is also related to the functionality required and enabled by the
approach. For the example above, the following can be observed:
a) Metamodel using UML Class Diagrams: intends a formal representation that
can be used to generate code as it can be mapped to elements of object-
oriented programming languages or other execution systems. In line with the
BPMN 2.0 specification, ”execution semantics have been fully formalised”
[24, p. 10] and allow conformance verification as well as runtime interpreta-
tion.
b) Metamodel using OWL Representation: using ontology concepts enables ver-
ification and checks of model artefacts e.g. by ”checking the compliance of a
50
Fig. 2. BPMN 2.0 Process Metamodel represented as a) UML 2.0 Class Diagram, b)
OWL Ontology, c) Logic/Rules using TELOS[23]
process against the BPMN specification” [25, p. 136], reasoning and detec-
tion mechanisms. The sequence logic is implemented as object properties in
the ontology.
c) Metamodel using TELOS : using logic and rules that are bound to the petri
net meta-structure provide simulation capabilities as the behaviour is de-
scribed already in the abstract meta-model. The sequencing possibilities are
represented as a self-referential loop on the root element.
It is assumed that the problem described using the motivational example is
not only applicable for pre-existing metatmodels (and would result in an inte-
gration challenge of different realizations) but can be observed also for domain-
specific metamodels and their characteristics that are constructed from scratch.
Gap. Resulting from the problem observed above, the design or (continuous)
adaptation/evolutions of metamodels is a tedious, inefficient and error-prone
task that requires domain expertise on one hand, meta-modelling knowledge,
an overview of existing results and creativity to perform the adjustment and
interpretation of requirements to a new/evolved metamodel. It is proposed to
close this gap by a) extending the notion of metamodels as conceptual structures
as a means to define and describe metamodels independent of the technique used
and collect these conceptual structures for re-use in the engineering process of
metamodels. The adequateness, usefulness and purpose of the metamodel might
influence the need for expressiveness of the conceptual structure for metamodels.
The toolbox of a metamodel engineer should include this collection and evolve
along industrial trends in a dynamic manner. Supporting agile techniques as
51
discussed in [17] has been identified as a challenge this research project aims
to contribute to as the means of re-use of metamodels, consistent combina-
tion/integration and evolution for ”Models of Concepts” on the other hand (see
Fig. 3 in [17, p. 8] are currently limited.
Research Objectives. Based on the identified gap, the research questions are
formulated below. As a design based research methdology is followed (introduced
in section 3. The objective of the research project is to develop a knowledge
representation formalism as a conceptual structure for metamodels, representing
the domain knowledge (syntax, notation, semantic, behaviour) encapsulated as
elements in the structure. The design artefacts realized as the contribution are
mapped to research objectives and questions depicted graphically in Fig. 3.
RQ1: Which conceptual structure can be identified
as adequate to support the formalization of metamodels?
Fig. 3. Research Question: Metamodels as a Knowledge Representation
RQ1 aims to develop a framework for knowledge representation as concep-
tual structures that is adequate to describe metamodels. It is currently assumed
that this knowledge representation can be derived from a) the definition of the
term ”metamodel” in the scientific community, b) existing metamodels from
academia and industry and their characteristics and construction principles,
c) meta-modelling techniques and patterns that are currently applied, and d)
knowledge operation as requirements on metamodels. Based on these prelimi-
nary input identification, the objective of the research questions is to identify
a formalism that allows the description of metamodels using dimensions to be
established as part of the research questions (e.g. syntactical, semantical, be-
havioural, operational, etc.).
52
RQ2: Which mechanisms are required to describe and
collect conceptual structures of metamodels?
How can reoccurring patterns in existing metamodels be identified and
described in abstract terms?
The research questions targets the mechanisms required to a) describe and
b) collect/support re-use metamodels as conceptual structure. This includes the
analysis of description dimensions that enable e.g. their registration, retrieval
and binding. Having the motivational example in mind, the second part of the
research question targets the identification of patterns based on these estab-
lished dimensions. Metamodels developed using modelling techniques of similar
or varying expressiveness and functionality are reviewed to identify techniques to
describe patterns and provide means for e.g. generalisation/abstraction of these
patterns or fragments independent.
RQ3: Do algorithms exist that are appropriate to suggest and propose
knowledge operations in a domain context using the conceptual structure?
The focus of this research question is to identify how knowledge operations
can be proposed for specific conceptual structures. This builds on the assump-
tion that specific operations require a conceptual structure to operate upon.
It is planned to investigate available algorithms that suggest functionality for
metamodels based on expressiveness of the conceptual structure and functional
requirements not limited to interoperability, semantic lifting, reference align-
ment, mapping or consistency management. The assumption underpinning the
research question is that a metamodel needs to fulfil certain requirements to be
capable to perform a concrete knowledge operation to be applicable.
3 Research Methodology
The research objective of this project is to develop artefacts to support the en-
gineering of domain-specific modelling methods, more precisely the design of
its metamodels. A novel and innovative framework that integrates the concep-
tual structures as a building block will be proposed that allows a knowledge
representation of metamodels. The design-science based approach introduced
by Hevner and Chatterjee in 2010 in [11] has been selected as the adequate
research methodology for the project. As an initial step, the Information Sys-
tems Research Framework in [11, p. 274] has been specialised to the research
project, respecting the guidelines set forth by Hevner and Chatterjee. The re-
search methodology for the project is presented in Fig. 4.
In line with the research questions, the research project aims to develop
an adequate conceptual structure to represent metamodels and corresponding
matching algorithms for knowledge operations. The design of this formalism
and algorithms is evaluated through a prototypical implementation of a domain-
specific language for metamodels. As a means to iteratively refine the design
53
Fig. 4. Research Methodology derived from [11, p. 274]
artefacts, the concept established and prototype realised are continuously eval-
uated in the context of the Open Models Initiative Laboratory (OMiLAB) [9]
using metamodels developed in the laboratory in a first phase (for a collection
of domain-specific modelling methods developed in the context of the OMiLAB
see [19]), extended to a broader scope from academia and industry thereafter.
These evaluation iterations will refine the design artefact and prototypical im-
plementation iteratively.
4 Research Approach and Preliminary Results
The research approach for the project is structured according to the research
methodology. These phases contribute individually to the research questions
and are used during the Assess-Refine iterations. The five phases are executed in
line with the methodology and will be iteratively run. This impacts individual
design-evaluation cycles of each phase and also transitions in between phases.
In general, phase 1 and 2 are contributing to the explication and requirements
elicitation objective of the project, phase 3 to a refinement, whereas phase 4
performs the development and phase 5 the evaluation of the design artefacts.
54
Phase 1: Analyse the Knowledge Base.
(contributing to RQ1 and RQ2)
During this phase, a literature review is conducted to establish an understand-
ing of the terminology and definition applicable for metamodels and conceptual
structures. The understanding of the term metamodel and its different interpre-
tation will impact the design of the conceptual structure and vice-versa. The
applicability of the research question is verified during this phase and further
refined.
Preliminary Results. A preliminary review of literature on available definitions
for the term meta-model aims to establish a common baseline. The results are
presented below having motivated in an early stage of the research project the
identification of the gap and research questions.
– Language-based Understanding of Metamodels: Strahinger investigates in [28]
the term meta-model and how it is used in scientific literature. 24 selected
definitions have been assessed, showing the ambiguity of the term and its
application in scientific literature of the domain. The author concludes by
constructing the term using a language-oriented technique, discussed in [27].
– Ontological Metamodels: The ontological representation of metamodels has
been extensively researched in the past following the objective to enrich
metamodels (type semantics) and models (inherent semantics) with more
expressiveness e.g. to cover semantic interoperability requirements (see [12]).
The ontological representation provides the required formal foundation for
a domain ([35]).
– Situative Metamodels: Brinkkemper discusses in [5] situative metamodels.
This aspect of meta-modelling aims to compose metamodels from fragments
that support a specific subset of the modelling methods. The discussion of
the language elements required to describe method fragments, including the
ontological anchoring as defined in [6] is relevant for the conceptual structure
definition in this research project. The extended view on situational methods
in [10] supports the assessment.
– Metamodelling Platforms: Karagiannis/Kühn introduce [16] a generic frame-
work for modelling methods and establish the definition of the metamodel
on meta2 level. The framework is composed of the model technique (con-
sisting of the modelling procedure and modelling language) and the model
processing functionalities as mechanisms and algorithms to be considered
during the conceptualisation.
– Metamodels in Model-Driven Development Atkinson/Kühne review in [1] the
types of metamodels used in Model-driven Development (MDD). They iden-
tify which abstraction techniques are currently used and applied by software
engineers to write higher-level code: traditional, OMG based modelling in-
frastructures, linguistic metamodelling and ontological metamodelling. The
representations resulting from the techniques are classified as views that
conform to the technique applied.
55
The review of literature is in progress and conclusions are to be established.
A review on the terminology and its application in the software engineering do-
main (e.g. model-driven development and meta-meta models in [20], graph-based
techniques in [7], multi-level metamodelling in [8]) is pending. Additionally, the
results developed by the conceptual structure community in computer science is
reviewed and assessed.
An observation at this stage shows that the research questions are in line
with the challenges observed in literature (e.g. [1] or [4]
Phase 2: Collect and Assess Metamodels.
(contributing to RQ1 and RQ2)
Concrete metamodels are identified and collected in this phase. This collection
acts as a basis for further analysis on the formalisation concepts and dimensions.
Fragmentation techniques as discussed in [4] from a specification viewpoint might
act as guidelines to assess subsets and patterns within existing metamodels. The
collection aims to develop classification dimensions in a first step and verify
those continuously. As a second aspect, the knowledge operations that can be
observed within metamodelling projects are collected and classified. The classifi-
cations scheme used is input for the dimensions used in the conceptual structure
(see [36] for an example of a similar approach on knowledge management ser-
vices).
Preliminary Results. The collection phase is in progress and builds at this stage
on the metamodel developed at the OMiLAB and the literature review performed
in [2].
Phase 3: Refinement of Requirements.
(contributing to RQ1 and RQ2)
The objective of this phase is to assess and evaluate requirements and the re-
sulting development/evaluation results in iterations. It is responsible to capture
intermediate results and feed the forward/back to the related phases.
Preliminary Results. Results from the design/evaluation iterations are not yet
available. Preliminary work has been performed in research projects that fed
into the design phases introduced above. The results achieved are published in
[34], [33], [32] and [30]. The presented results in these publications has been
done in differing industry and application domain, focusing on the development
of metamodels in these fields.
Phase 4: Establish the Conceptual Structure and Realise a Prototyp-
ical Implementation
(contributing to RQ2 and RQ3)
56
The objective of this phase is to design the concept and implement a proto-
typical realisation for conceptual structures.
Fig. 5. Metamodelling using Abstract Building Blocks
Preliminary Results. As a preliminary result a procedural framework has been
introduced using abstract metamodelling building blocks. This framework acts
as a guiding element for the research to be conducted in the review and analysis
phases. It has been proposed in [3] and prototypically been applied in [29].
1. Approach: at this stage, abstract metamodel building blocks are introduced.
Each building block specified couples abstract model constructs, related
model processing functionality and a description of the description of the
abstract metamodel building block that enables the identification and inte-
gration of it in more complex scenarios that span multiple building blocks.
The description should reflect the dimensions required for the conceptual
structure.
2. Concept: the transformation from the approach stage is done through in-
stantiation. The abstract blocks are made concrete using a selected meta-
modelling technique.
3. Implementation: represents the operationalisation level of the building blocks
as the conceptual blocks are mapped to a concrete realisation technology and
can be executed/run in a tooling infrastructure.
Phase 5: Evaluate and Refine Design Artefacts
(contributing to RQ1, RQ2 and RQ3)
The evaluation of the concept and prototype is performed using identified meta-
models in an explorative laboratory setting. Feedback from the evaluation refines
the concept developed.
57
Preliminary Results. Evaluation results in a structured form are not yet available.
An initial indications for the applicability of the concept is discussed in [31] using
reference alignment operation on two metamodels for for industrial business
process management (IBPM) to support simulation mechanisms on a graph-
based structure. The prototype developed and documented in [29].
Acknowledgment
This doctoral project is supervised by o. Univ.-Prof. Prof.h.c. Dr. Dimitris Kara-
giannis, head of the research group Knowledge Engineering and the Open Models
Initiative Laboratory (OMiLAB) at the Faculty of Computer Science, University
of Vienna.
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