Towards Hybrid Semantics of Enterprise Modeling Languages
Richard Braun and Werner Esswein
TU Dresden, Chair for Wirtschaftsinformatik, esp. System Development, 01062 Dresden, Germany
Enterprise Modeling, Meta Modeling, Formal Semantics, Material Semantics, Behavioral Semantics.
Enterprise Modeling Languages (EMLs) are generally perceived as conceptual modeling languages having
a formal syntax and informal semantics. The non-formality of semantics is mainly caused by the material-
ity of the addressed domain (enterprises and its related aspects) and the resulting personal interpretation of
syntactical constructs. However, EMLs may also explicitly define invariant interpretations in the sense of pos-
sible model executions or the definition of domain-specific restrictions. It is therefore promising to address
a possible amalgamation of material semantics and formal semantics in order to provide an integrated and
comprehensive semantic specification of EMLs. This position paper introduces and motivates the topic by
systematizing and consolidating approaches from both fields and introduces a framework for so-called hybrid
semantics on the meta model layer. Further, the general relevance of semantics and semantic specifications in
EMLs is emphasized and prospective research challenges are proposed.
1.1 Semantics in Enterprise Modeling
Enterprises are multifarious, heterogeneous socio-
technical organizations, whose components are in-
terrelated within a complex system on different ab-
straction levels (Vernadat, 2003). Enterprise Model-
ing (EM) aims to conceptualize, abstract and repre-
sent parts and aspects of enterprises by creating con-
ceptual models in order to foster communication be-
tween involved stakeholders and enable an integra-
tion of static, procedural and functional dimensions
(Lankhorst, 2009; Frank, 2013; Frank, 2014; Maes
and Poels, 2007).
Enterprise Modeling Languages (EMLs) serve
as languages for the construction of respectively
needed enterprise models. The range of EMLs can
be divided into integrated, domain-independent ap-
proaches like ArchiMate (Lankhorst et al., 2009),
ARIS (Scheer and N
uttgens, 2000) or MEMO (Frank,
2014), purpose-specific languages like BPMN (pro-
cesses (OMG, 2011)) or KAOS (risk management
(Heaven and Finkelstein, 2004)) and dedicated,
domain-specific approaches like CPmod (Burwitz
et al., 2013).
Most EMLs constitute as semi-formal modeling
languages (Wand and Weber, 2002), generally fea-
tured by a formal syntax and informal semantics
(Karagiannis and K
uhn, 2002). Informality of se-
mantics primarily results from the fact that EMLs ad-
dress the modeling of enterprises and related aspects,
which are usually not entirely formalizable (Pfeif-
fer and Gehlert, 2005), as they mainly refer to real-
world concepts (Lindland et al., 1994, p. 44). This
can be also referred as ontological semantics (Harza-
llah et al., 2012, p. 489). EMLs cover a practi-
cally unlimited set of concepts (Anaya et al., 2010,
p. 101). Semantics represents the meaning of a par-
ticular sign system (syntax) in regard of a semantic
domain (Overhage et al., 2012, p. 22).
Consequently, stakeholders have different concep-
tualizations and understandings of particularly ad-
dressed real-world things (van der Linden, 2015).
Also specific contexts (Bjekovic et al., 2014) or lex-
ical ambiguities (Delfmann et al., 2009) may deter-
mine different interpretations of one and the same
meta model construct. Besides this immanent fea-
ture of subject-dependent reference between enter-
prise models and semantic constructs (Rosemann
et al., 2004; Guizzardi, 2007; Opdahl and Henderson-
Sellers, 2002), invariant interpretation is explicitly de-
sired in some enterprise models for different reasons
(Bork and Fill, 2014, p. 3400). Besides, it could be
explicitly intended to limit the variance of interpret-
ing enterprise models in order to evolve and ensure
a common, coherent understanding within a particu-
Braun, R. and Esswein, W.
Towards Hybrid Semantics of Enterprise Modeling Languages.
DOI: 10.5220/0005812504120420
In Proceedings of the 4th International Conference on Model-Driven Engineering and Software Development (MODELSWARD 2016), pages 412-420
ISBN: 978-989-758-168-7
2016 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
lar domain (Frank, 2013, pp. 13-14) - for instance, in
regulated and rather standardized domains like health-
care (Burwitz et al., 2013).
1.2 Research Objective and Research
It is therefore advisable to focus the current state of
the art in regard of material semantics (Gehlert and
Esswein, 2006) and formal semantics (Bork and Fill,
2014) in order to investigate reasonable integrations
of these types. We therefore aim to provide a frame-
work for differing types of semantics based on their
characteristics. This is necessary, as the term seman-
tics itself (Thalheim, 2012, p. 7) has several seman-
tics within different research disciplines (Harel and
Rumpe, 2004, pp. 67-69). For instance, semantics
in formal modeling languages is understood as the
invariant transformation of one valid model state to
an other (Engels et al., 2000), while in semi-formal
languages a mapping to semantic concepts is meant
(Rosemann et al., 2004). This issue seems to be
closely related to the immanent multi-disciplinarity
in EM. The framework should further facilitate the
identification of particular integration points and sup-
port the subsequent alignment and integration of the
doubtless existing description techniques for seman-
tics from different fields of research (van der Linden,
2015; Opdahl et al., 2012; H
offerer, 2007; Engels
et al., 2000).
The motivation of the stated research goal comes
from the rather poor semantic specification of mod-
eling languages (Harel and Rumpe, 2004, pp. 67-
69) and EMLs (Santos et al., 2013, pp. 690, 706),
which hampers language comprehensibility (Guiz-
zardi, 2007; Wyssusek, 2006) and provokes mis-
understandings and semantic mismatches (van der
Linden, 2015). Further, syntax and semantics
ager, 2011b) or informal and formal aspects
are often amalgamated (Gehlert et al., 2005). Es-
pecially in OMG specifications, syntactical state-
ments and semantic explanations are often mixed and
distributed over hundreds of specification document
pages (Hausmann et al., 2005, p. 19), causing misun-
derstandings and contradictions (Natschl
ager, 2011b).
This issue is even exacerbated due to the pure infor-
mality of semantic statements.
It is therefore necessary to elaborate and define a
generic and integrated specification format for EML
semantics in order to provide possibly precise seman-
tics for meta model concepts, both in terms of variant
as well as potentially invariant semantics. This in turn
could facilitate semantic agreements in collaborative
EM (Rospocher et al., 2008; Renger et al., 2008).
While the formal syntax of EMLs is fundamen-
tal, we agree to Bjekovic et al. (2014) stating that se-
mantics and pragmatics are the actual drivers of EML
usage. Investigating material and formal semantics
in the EM domain is therefore also highly relevant in
terms of EML dissemination and revision, as seman-
tic comparisons might better justify the necessity for
EML dialects and language adaptations (Braun, 2015;
Bjekovic et al., 2013). Generally, general-purpose or
purpose-specific languages like BPMN or ArchiMate
provide generic semantics, which are often speci-
fied or refined for particular contexts (Hamann and
Gogolla, 2013, p. 501).
The remainder of this position paper is therefore
as follows. Section 2 introduces formal semantics in
detail, as the role of formality in EM is only little in-
vestigated so far. Section 3 then presents material se-
mantics. Section 4 outlines the aimed framework and
proposes several topics for following research. The
paper ends with a brief conclusion in Section 5.
2.1 Relevance from the Perspective of
Enterprise Modeling
Formal semantics cover the unambiguous and
subject-independent usage of linguistic expressions,
which causes interpretive invariance (Guizzardi et al.,
2002). Formal semantics is relevant within enter-
prise modeling for basically two reasons: Automation
and analysis (Braun, 2015). Automation-oriented for-
mal semantics indirectly refers to the real-world do-
main in the sense of automatable process execution
in order to avoid failures and improve efficiency in
a proactive manner. Functional or process-oriented
languages are conducted for that purpose (Bork and
Fill, 2014). BPMN aims to support process execution
by textually describing their automatic interpretation
(OMG, 2011, p. 435).
While automation owns real-world references
(e.g., references to particular resources or machines),
in analysis-oriented formal semantics the transforma-
tion of models into other models or formalizations
is primal. For instance, deontic logic is applied to
BPMN (Natschl
ager, 2011b) and other authors intro-
duced specific operations on models for BPMN model
version management (Pietsch et al., 2014). However,
formal semantics is rarely discussed from the perspec-
tive of EM and there is a lack of specifying required
formal aspects in the context of EMLs (Bork and Fill,
2014). Below, generic approaches for both specifying
Towards Hybrid Semantics of Enterprise Modeling Languages
formal semantic domains as well as the formal seman-
tic mapping are hence introduced in order to prepare
a potential application for invariant parts of enterprise
2.2 Formal Semantic Domain
The semantic domain of formal semantics does not
correspond to the real world, but rather to a con-
structed artificial world, which can be seen as solution
layer that aims to solve problems that are relevant for
real world problems but require further interpretation
(Gehlert, 2007; Pfeiffer and Gehlert, 2005). In de-
tail, a formal semantic domain often acts as kind of
a projection surface for solving consistency issues of
modeling languages, while the explicit real world ref-
erence is omitted (Engels et al., 2001, pp. 187-188).
Consequently, formal semantic domains are usu-
ally described by formal expressions (e.g., algebra
or graph-based languages), whose interpretations are
generally perceived as invariant (Tarski, 1936; Haus-
mann, 2003; Schobbens et al., 2006). Formal seman-
tics are mainly discussed in the context of the UML
(Engels et al., 2000), implicating a naturally strong
focus on design-oriented approaches within Software
Engineering (Hausmann et al., 2005, p. 5). Although
an explicit and integrated specification of semantics
is deeply required (Maoz et al., 2010, p. 2), a lack of
formal specifications can be observed, e.g. for UML
(Soltenborn and Engels, 2009, p. 7).
Formal semantics can be divided into behavioral
formal semantics and static formal semantics (Engels
et al., 2000).
Behavioral formal semantics describe executable
things in the sense of transformations between states
in order to model token flows or state transitions
(Hausmann et al., 2005, p. 25), aiming to enable auto-
matic interpretation of each model instance (Hamann
and Gogolla, 2013, p. 489). Consequently, abstract
state machines, Petri nets, automatons or finitie ac-
tions traces are leveraged as semantic domains for
behavioral semantics (Engels et al., 2000; Hausmann
et al., 2005; Maoz et al., 2010; Kossak et al., 2014).
There are further some works regarding execution
semantics of BPMN focusing workflow derivation
(Lam, 2012). By default, BPMN only provides rather
imprecise textual statements on this topic (OMG,
2011, p. 435).
Static formal semantics aims to represent valid
and consistent static states. Static semantics is rarely
specified in modeling languages and refers to deno-
tational approaches. It is hence more interesting to
limit a particular interpretation scope by introducing
additional constraints (e.g., regarding union subsets
in UML (Hamann and Gogolla, 2013)) or specifying
the technical domain that should be represented. For
instance, system models (Cengarle et al., 2014) are
used as formal domains of syntactical domains speci-
fied with the MontiCore language (Maoz et al., 2010).
2.3 Formal Semantic Mapping
The actual formality of formal semantics results from
the invariant relation between syntactical constructs
and the semantics domain, which enables an a pri-
ori definition of the semantics (Messer, 1999, p. 99).
Formal semantics can hence be understood as replace-
ment of expressions of one language by expressions
of an other formal language causing syntactical map-
pings (Fill, 2015, p. 44). This actually leads to a con-
tinual shift of unambiguous interpretation (Gehlert,
2007, p. 37), constituting as rule-based chain of trans-
formations (Holten, 2003, p. 50), which typically
leads to a final formulation of mathematic or algebraic
statements (Hausmann et al., 2005, p. 10).
Hence, formal semantic mapping represents the
specification of two formal syntaxes; generally con-
stituting as operational or even axiomatic semantic
annotations (K
uhn, 2004, pp. 34-35). Surprisingly,
most mappings are only informally specified (Haus-
mann et al., 2005, pp. 25-27). Formal approaches
cover process algebra, linear equations, task graphs
or functions. In contrast to these textual approaches,
some researchers propose model-based approaches
for specifying relations between MOF-based meta
models (Hausmann, 2003).
Finally, the Dynamic Meta Modeling (DMM) ap-
proach has to be taken into account. DMM is a
generic approach for the integrated specification of
formal semantics and mappings of behavior describ-
ing modeling languages, e.g., state charts or activity
diagrams (Engels et al., 2000). DMM only requires a
well-defined meta model and can be implemented in
three major steps: First, the meta model of a language
is slightly extended. Then the behavior is formally
specified and a transformation into a typed graph has
to be conducted in order to realize the behavioral se-
mantics (Engels et al., 2000). Therefore, syntactical
expressions are coupled with pre conditions (events),
rules for transformation (action) and post conditions
(Soltenborn and Engels, 2009). If the pre conditions
for a syntactical construct are fulfilled then particular
rules are applied until the post conditions are satisfied,
which represent a new valid model state.
MODELSWARD 2016 - 4th International Conference on Model-Driven Engineering and Software Development
Material semantics generally covers enterprise-
related domains (Harel and Rumpe, 2004; Lindland
et al., 1994), which cannot be described with formal
syntax per se (Pfeiffer and Gehlert, 2005; Gehlert,
2007). This is of primal relevance in EM, as the entire
formalization of enterprise-related domains is practi-
cally not possible (cf. (Searle, 1984, p. 6), (Gehlert,
2007, p. 29) and (Pfeiffer and Gehlert, 2005, pp. 111-
112)). The reason for that lies in the lack of distinc-
tion between particular terms; especially regarding
synonyms, which cannot be differentiated confidently
and cause a not Turing-computable problem (Pfeif-
fer and Gehlert, 2005). Besides, different EML users
usually have different understandings of EML meta
concepts (van der Linden, 2015).
We divide semantics on meta model layers into the
semantic domain and semantic mappings (Karagian-
nis and K
uhn, 2002)
. This understanding is shared
by numerous researches in the field of ontological
analysis (Wand and Weber, 1993; Rosemann et al.,
3.1 Material Semantic Domain
The semantic domain covers all kinds of things the
language should be able to express (Harel and Rumpe,
2004, p. 68) and is conceptualized by domain con-
structs representing those things (Lindland et al.,
1994, p. 44). Consequently, syntactic constructs are
already pre-conceptualized and implicitly limit possi-
ble semantic references (Bjekovic et al., 2013; Wys-
susek, 2004). Basically, material semantics covers
what should be represented, i.e. those referred do-
main constructs that should be expressed (Esswein
and Weller, 2007, p. 2004). A further distinction into
static and behavioral material semantics as applied
in the field of formal semantics (Hausmann et al.,
2005) seems to be superfluous, as the actual impact
of a syntactical construct becomes only relevant in
its individual application (cf. pragmatic semantics
(Bjekovic et al., 2014)). The underlying conceptu-
alization (Guizzardi, 2007) is only the carrier or inter-
mediate layer for that. In contrast, in formal seman-
tics the semantics itself represents the application, in-
dependent of any intermediate layer.
Domain constructs can represent physical things
as well as artificially constructed things of a domain
(Malt, 1990; Bloom, 1998). Thereby, it is extremely
We omit the consideration of semantics on the model
layer, which is referred as inherent semantics (Bork and Fill,
2014, p. 3403), since this paper explicitly aims to investi-
gate semantics within the meta model layer.
important to note that the perception, cognition and
interpretation of those things are subject-dependent
and determined by multiple factors (Bjekovic et al.,
2014; Esswein and Lehrmann, 2013; van der Linden,
We refer to this as structural ambiguity, as it fi-
nally refers to the addressed domain constructs. Some
EM researches investigate dimensions of basal meta
concepts from the enterprise domain and demonstrate
their immanent variance (van der Linden and Hoppen-
brouwers, 2012; van der Linden and van Zee, 2014;
van der Linden, 2015). However, most EMLs implic-
itly base on positivistic assumptions (Bjekovic et al.,
2014, p. 438) indicating a precise and invariant refer-
ence to domain concepts, which is insufficient in this
regard. Rather the positions of constructivism (mod-
els as constructions of reality-related conceptualiza-
tions) or at least critical rationalism (models as re-
constructions) seem to be more appropriate (Gehlert
et al., 2005; Esswein and Lehrmann, 2013) in order
to correspond to the immanent socio-pragmatic pur-
pose of EMLs (Bjekovic et al., 2014, p. 433).
3.2 Ambiguous Interpretation
Current representation approaches address the actual
description of material domains with the help of de-
notational approaches (K
uhn, 2004, p. 34); namely
pre-defined generic ontologies (Wand and Weber,
1993) or EM-related ontologies (Gehlert et al., 2005;
offerer, 2007; Anaya et al., 2010; Fill, 2015). All
these approaches contain natural language statements
implying the lack of invariant interpretation (Thal-
heim, 2012). We refer to this as lexical ambiguity.
It covers same understandings with differing labeling
(e.g., synonyms) as well as different understandings
with same labeling (e.g., homonyms).
The stated ambiguities implicate subject-specific
understandings of material semantics, which is also
referred as personal semantics (van der Linden,
2015). We refer to this as actual semantics cov-
ering the actual understanding of an EML (perhaps
influenced by situational particularities (Henderson-
Sellers, 2005; Braun and Esswein, 2014). In contrast,
stipulated semantics constitutes as the actual seman-
tics of the language designers, which hence act as ini-
tial base for language understanding in the sense of an
original reference point for comparison (Rosemann
et al., 2004).
Reducing the mentioned difference between stip-
ulated and actual semantics of different stakehold-
ers causes the issue of semantic consensus-finding
(Harzallah et al., 2012, p. 501) and ontological com-
mitment (Guizzardi, 2007, p. 25) within EMLs. Frank
Towards Hybrid Semantics of Enterprise Modeling Languages
(2013) claims that the semantics of domain-specific
EMLs need to be invariant within a particular do-
main over a certain time period (Frank, 2013, pp. 13-
14). Obviously, such common understandings seem
to be realizable within a small modeler community.
It becomes more difficult when addressing general-
purpose languages (e.g., UML) of purpose-specific
languages (e.g., BPMN), which are underspecified
and intentionally generic for a certain degree by defi-
nition (cf. the Pool meta class in BPMN, for instance).
Consequently, respective syntactical constructs own
several semantic mappings. It becomes obvious that
respective consensus finding approaches - on top of
current representation formats - need to be elaborated
and investigated in further research.
3.3 Material Semantic Mapping
Semantic mapping is the mapping of syntactical con-
structs to domain constructs (Karagiannis and K
2002). This mapping usually constitute as interpretive
step (Wand and Weber, 1993) due to the immanent
role of subject-dependent interpretation and missing.
In EM relevant languages, the mapping is mostly in-
formally specified (Bork and Fill, 2014, p. 3407). The
field of ontological analysis of EMLs focuses on those
mappings in order to evaluate completeness of a lan-
guage in regard of a given ontology. For instance, the
BWW ontology provides a projection for the mapping
of syntactical concepts of EMLs by adapting the on-
tology of Bunge (Wand and Weber, 1993).
4.1 Pragmatics and Motivation
Pragmatics is understood as the actual application of
an EML in a specific context in order to solve or sup-
port a particular task (Lindland et al., 1994; Thalheim,
2012). Pragmatics in the field of EM is rarely investi-
gated so far (Bjekovic et al., 2014). We hence propose
to distinguish pragmatics in accordance to the recip-
ient of the model that represents the major point of
utility creation, since enterprises constitute as socio-
technical information systems. Usually, the recipient
is a human actor, who is expected to perform some-
thing by using the model. This could be done for rea-
sons of pure documentation, which focuses the status
quo, or in regard of design and engineering, which
rather focuses desired states (e.g. designing a particu-
lar application system). If the recipient is a machine,
then this machine is expected to solve a particular
task that was technically specified before (e.g. model-
based analysis). Respective value is hence basically
created either by humans (variant, not determinable)
or machines (invariant, determinable). Potential hy-
brids between both types have to be decomposed un-
til a clear differentiation is possible. The creation of
value requires the understanding of models and re-
spective meta models. In case of expected human ac-
tions, material semantics are appropriate, while for-
mal semantics are relevant for machine actions. For-
mal semantics can be also relevant for human actors,
if interpretation invariance can be created based on
some kind of consensus finding.
There may be cases in EM, where both human ac-
tors and machines are recipient of a specific model.
For instance, in the field of clinical process manage-
ment, clinical pathway models represent the process-
oriented, multi-perspective and integrated view on
treating patients and respectively required service
processes (Braun et al., 2014). While the treatment
process is naturally not formal, some service pro-
cesses can be formally defined in order to execute au-
tomatable tasks. For instance, a particular resource
has to be automatically reordered by an ERP system
if the execution of a specific activity is completed. In
contrast, this activity is part of a treatment process
that is manually executed and the stated resource can
have different (material) semantics for human actors:
A physician perceives some resource as instrument
for treatment and a controlling expert refers to cer-
tain financial aspects. Despite the different semantic
references of each actor (ERP system, physician, con-
trolling expert), the thing resource is mutual and pro-
vides a point for integration.
Therefore, it seems to be promising to discuss the
specification of respectively required semantics in or-
der to enable an integrated definition of semantics (cf.
Section 1.2) and support the identification of such se-
mantic integration points.
4.2 Framework
It becomes obvious that single semantic types are use-
ful for different purposes in the context of EM. These
types of semantics are therefore summarized in the
light of EM by focusing their semantics and ontolog-
ical positions concerning the real-world in Table 1.
They are further presented in detail below. Thereby,
also potential adaptations of formal semantics to orig-
inally non-formal issues from EM are briefly consid-
Behavioral formal semantics can be seen as ap-
propriate means for the specification of executable
MODELSWARD 2016 - 4th International Conference on Model-Driven Engineering and Software Development
Table 1: Types of Semantics in the Context of EMLs.
Behavioral Formal Seman. Static Formal Semantics Material Semantics
Principle Transitions between two
valid states by mapping to a
formal system and specifying
the transformation rules.
Definition of validation
rules for a model state
by mapping to a formal
Mapping syntactical con-
structs to subject-dependent
domain constructs.
for EMLs
Execution of parts of the
EML, i.e. some of its
meta classes become exe-
cutable. For instance, in-
variant machine operations
(Fill, 2015) or the semantics
of executable BPMN classes
(OMG, 2011, p. 435).
Restrictions on single meta
classes or constructs of
classes; similar to syntactic
semantics (Bjekovic et al.,
2014, p. 439). Alternatively
useful for on top model
analysis, i.e. mapping to a
formal target model.
Shaping a particular dis-
course and its basic concep-
tions (Proper et al., 2005);
pragmatic semantics in the
sense of relating syntax
to individual conceptualiza-
tions and the context of
a particular user (Bjekovic
et al., 2014).
Invariance Needs to be invariant and
mapped to truth-apt, exe-
cutable syntax.
Invariant for syntacti-
cal mapping, variant for
ontological mapping.
Variant to differing degrees
(cf. Section 3.2).
meta classes of an EML by specifying legal transfor-
mations between respective model states. It is tra-
ditionally useful for the specification of any kind of
transformative and behavioral aspect in parts. This
might be the case for accompanying aspects of pro-
cess models, like resource-related aspects (Braun and
Esswein, 2014, p. 49). More precisely, the flow
through a particular process model might cause hu-
man decisions and is hence not invariantly inter-
pretable. But the affected resource transformations
could be invariantly interpretable if particular re-
source allocations or transformation processes are
triggered and executed (see above). Although this
could be also done by an integration of a separate for-
mal language, it is more comprehensive to keep it in
one EML definition and one EML semantics defini-
tion. Respective classes are therefore not solely de-
scriptive, but contain executable semantics.
Behavioral formal semantics require invariant se-
mantic mappings to a formal target model (syntax of
the explaining modeling language, cf. Section 2.3).
This indicates that a respective syntactical construct
from the meta model should become subject of an on-
tological discussion in order to find respective con-
sensus. After reaching an invariant semantic mapping
to a particular domain construct, the domain construct
should be mapped invariantly to a syntactical con-
struct in order to specify respective transformations.
Briefly speaking, the syntactical construct is mapped
to the syntactical construct of an other language (Fill,
2015) after an explicit agreement on the respective
reference domain is reached (Frank, 2013, pp. 13-
14). Finally, a particularly instantiated model rep-
resents not only real-world things descriptively, but
rather can manipulate parts of it (e.g., by coupling a
Enterprise Modeling Language
Hybrid Semantics
Material Domain
Material Semantics
Formal Syntax
Formal Semantics
Static Semantics
Material Semantic Mapping
(variant interpretation)
Formal Syntax’
Formal Syntax’
Formal Sem. Mapping
real-world interpretation
Figure 1: Outlining Hybrid Semantics in the Context of
EML Specifications.
particular machine control unit with respective model
Static formal semantics might be applicable for
the transformation of parts of the meta model into
other formal models aiming to define particular anal-
ysis on top of a model (external view) on the one side
ager, 2011a; Pietsch et al., 2014). On the
other side, it can be used for specifying valid model
states (internal view), which represent inherent rules
and restrictions of a particular domain.
In contrast to behavioral formal semantics, trans-
formation rules are not defined. Due to the required
invariance between the syntactical constructs, respec-
tive consensus on the those relations is necessary,
Towards Hybrid Semantics of Enterprise Modeling Languages
i.e. consensus on the syntactical meaning of meta
model elements. This implies that a particular meta
model element should only be invariant in regard of
this relation, but can have several ontological map-
pings besides. Static formal semantics is hence view-
Material semantics are naturally useful for the ex-
plication of individual conceptualizations about par-
ticular discourse objects (Guizzardi, 2007; Bjekovic
et al., 2014), indicating a particular agreement on re-
lated domain constructs (Lindland et al., 1994; Es-
swein and Lehrmann, 2013; van der Linden, 2015),
which aims to enable a common and efficient com-
munication about a domain. Material semantics are
therefore primarily useful for supporting externaliza-
tion, documentation and communication between in-
Figure 1 outlines the concept of hybrid semantics
by splitting the EML semantics definition into parts
for material and formal semantics, which are inte-
grated by corresponding syntactical elements of the
meta model. The framework also depicts the final
relation of the type of formal semantics to the real-
This position paper outlines the prospective definition
of hybrid semantics for EMLs in order to explicitly
integrate formal and material semantics in a compre-
hensive manner. The paper should be understood as
starting point and contributes to the research com-
munity by structuring and framing different seman-
tic approaches and understandings, which are rele-
vant for EM. This could be seen as a first step for
multi-disciplinary integration, tearing down still ex-
isting academic stonewalls between Information Sys-
tems Research (especially material semantics) and
Software Engineering (especially formal semantics)
in order to elaborate respective synergies for EML se-
mantics. This explicitly covers the aspect of using
and applying enterprise models and addresses finally
pragmatics, which is still a poorly investigated topic
that is largely omitted (Bjekovic et al., 2014).
Naturally, several topics need to be investigated in
further research. We explicitly propose the following
three topics.
Firstly, it is extremely important to specify the
considered integration of material and formal seman-
tics on the meta model layer. We therefore conduct
two case studies in the field of manufacturing and
healthcare in order to derive more insights and tech-
nical consequences on that issue.
Secondly, it is also important to determine a spec-
ification format for hybrid semantics. It is there-
fore planned to conduct the DMM approach (Engels
et al., 2000) for formal aspects and ontology-based
approaches for material aspects. A structural en-
hancement of these ontologies should be examined
(van der Linden, 2015).
Thirdly, it is necessary to address the actual pro-
cess of consensus finding as stated in Section 3.2,
since a certain level of invariance is immanent for
any kind of execution and automation. This includes
the so far less discussed issue of explicitly specify-
ing semantics of generic EMLs for specific contexts,
domains or situations. This could lead to language
dialects keeping the syntax stable and changing the
semantic mappings, for instance (Braun and Esswein,
2014, pp. 47, 53).
This research paper presents partial research results
from the research project SFB Transregio 96, which is
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