=Paper=
{{Paper
|id=Vol-2542/MOD20-SP2
|storemode=property
|title=Conceptual Modelling and Humanities
|pdfUrl=https://ceur-ws.org/Vol-2542/MOD20-SP2.pdf
|volume=Vol-2542
|authors=Yannic Ole Kropp,Bernhard Thalheim
|dblpUrl=https://dblp.org/rec/conf/modellierung/KroppT20
}}
==Conceptual Modelling and Humanities==
https://ceur-ws.org/Vol-2542/MOD20-SP2.pdf
Joint Proceedings of Modellierung 2020 Short, Workshop and Tools & Demo Papers
Modellierung 2020: Short Papers 13
Conceptual Modelling and Humanities
Yannic Ole Kropp,1 Bernhard Thalheim2
Abstract: Humanities are becoming a hyping field of intensive research for computer researchers.
It seems that conceptual models may be the basis for development of appropriate solutions of
digitalisation problems in social sciences. At the same time, humanities and social sciences can
fertilise conceptual modelling. The notion of conceptual models becomes enriched. The approaches to
modelling in social sciences thus result in a deeper understanding of modelling. The main aim of this
paper is to learn from social sciences for conceptual modelling and to fertilise the field of conceptual
modelling.
1 The Value of Conceptual Modelling
1.1 Computer science is IT system-oriented
Computer system development is a complex process and needs abstraction, separation of
concerns, approaches for handling complexity and mature support for communication within
development teams. Models are one of the main artefacts for abstraction and complexity
reduction. Computer science uses more than 50 different kinds of modelling languages and
modelling approaches. Models have thus been a means for system construction for a long
time. Models are widely used as an universal instrument whenever humans are involved
and an understanding of computer properties is essential. They are enhanced by commonly
accepted concepts and thus become conceptual models. The main deployment scenario for
models and conceptual models is still system construction (with description, prescription,
and coding sub-scenarios) although other scenarios became popular, e.g. documentation,
communication, negotiation, conceptualisation, and learning.
1.2 Learning from Digital Hunanities
Digital humanities is becoming a hyping buzzword nowadays due to digitalisation and due
to over-applying computer technology. We have been engaged in a number of projects, e.g.
1 Department of Computer Science, Christian-Albrechts University of Kiel, 24098 Kiel, Germany, yk@is.
informatik.uni-kiel.de
2 Department of Computer Science, Christian-Albrechts University of Kiel, 24098 Kiel, Germany, thalheim@is.
informatik.uni-kiel.de
Copyright © 2020 for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
14 Yannic Ole Kropp, Bernhard Thalheim
[1, 2, 4, 6, 9]. We step back now and reconsider the challenges to conceptual modelling in
these projects and generalize the experience we have gained in these projects. Let us first
present a number of observations:
Observation (1): The concept spaces used in social sciences underpins the conceptual model.
Conceptions are systems of concepts. The concept space is typically structured complex and
is used with multiple viewpoints.
Observation (2): Conceptualisation has to be co-considered at various abstraction levels at
the same time, e.g. at the micro-, meso-, and macro-level.
Observation (3): The mould3 (and methodology) determines model handling and the
utilisation scenarios in which a model functions by playing roles. Models incorporate their
function.
Observation (4): The model consists of a surface (or normal) sub-model and of deep (implicit,
supplanted) sub-models which represent the disciplinary assumptions, the background, and
the context. The deep models are the intrinsic components of the model. Conceptualisation
might be four-dimensional: sign, social embedding, context, and meaning spaces.
Observation (5): Models benefit and suffer from the art of omission. Social and cultural
embeddings are considered to be obvious and are thus omitted.
Observation (6): Models can be materialised and thus might have a material obstinacy due
to the chosen material.
Observation (7): Conceptual models have to carry at the same time a manifold of under-
standings and a manifold of domain-situation models.
1.3 The storyline
These observations and lessons are useful for conceptual modelling in our area. They are
mostly not explicitly observed in computer science. They are however implicitly used.
Think, for instance, on conceptual database models. We often use a conceptual schema
that describes the structure of the entire database system and use additionally a number of
conceptual views that describe the viewpoints of users. Therefore, we explain now how
conceptual modelling can learn from successful approaches in social sciences. The learning
process will enhance the added value of conceptual modelling.
3 The mould is a hollow form or matrix or simply frame for giving things (such as models) a particular shape. In
production, moulds are used as a shaped cavity for forming fluid or plastic things.
Conceptual Modelling and Humanities 15
2 Learning from Humanities for Conceptual Modelling
According to [5, 10, 13] we define the model notion as follows:
“A model is a well-formed, adequate, and dependable instrument that represents
origins and that functions in utilisation scenarios.”
“Its criteria of well-formedness, adequacy, and dependability must be commonly
accepted by its community of practice (CoP) within some context and corres-
pond to the functions that a model fulfills in utilisation scenarios.”
Well-formedness is often considered as a specific modelling language requirement. The
criteria for adequacy are analogy (as a generalisation of the mapping property that forms a
tight kind of analogy), being focused (as a generalisation of truncation or abstraction), and
satisfying the purpose (as a generalisation of classical pragmatics properties).
The model has another constituents that are often taken for granted. The model is based
on a background, represents origins, is accepted by a community of practice, and follows
the accepted context. The model thus becomes dependable, i.e. it is justified or viable and
has a sufficient quality. Justification includes empirical corroboration, rational coherence,
falsifiability (in our area often treated as validation or verification), and relative stability.
The instrument is sufficient by its quality characterisation for internal quality, external
quality and quality in use. Sufficiency is typically combined with some assurance evaluation
(tolerance, modality, confidence, and restrictions).
2.1 The notion of conceptual model
A notion of conceptual model might be a slim, light, or concise one depending on the level
of detail we need in model utilisation. We will use in the sequel one notion, i.e. the concise
notion and refer for slim and light versions to [12, 14].
Concise version:
Concept(ion)s) ./ Enabler [7]:
É
Conceptual Model w (Model
A conceptual model is a model that is enhanced by concept(ion)s from a con-
cept(ion) space, is formulated in a language that allows well-structured formu-
lations, is based on mental/perception/situation models with their embedded
concept(ion)s, and is oriented on a mould and on deep models that are com-
monly accepted.
16 Yannic Ole Kropp, Bernhard Thalheim
The mould and the deep models form the matrix of a model [11]. We notice that a conceptual
model typically consists of a model suite in social sciences. Each of the models in a model
suite reflects some viewpoint or aspect.
2.2 The added value of conceptual modelling
Models do not have to be conceptual models. Conceptual models do not have to be based
on an ontology. The main purpose of conception as a system of concept or of a concept(ion)
space is the integration of interpretation pattern that ease the communication, understanding,
delivery of a model in dependence on the model functions. The concept(ion) space, the
mould of model utilisation, and the explicit knowledge of the social determination provide a
means for the correct and sufficiently precise interpretation of the model elements.
2.3 The four dimensions of conceptual modelling
The consideration of the strategic, tactical, and operational sides of modelling and of
conceptual modelling drives us to consider the four dimensions in Figure 1. These dimensions
Fig. 1: The representation, application context, foundation, and social dimension of conceptual
modelling
cover the application areas in [15] and especially those in humanities. Information systems
typically consider the representation dimension and only one of the branches of the
foundation dimension. Computer engineering especially considers the application context
Conceptual Modelling and Humanities 17
dimension.
Prescriptive conceptual models that are used as the blueprint for system realisation also
consider this dimension. The social embedding is typical for social sciences. The foundation
dimension has additional aspects in social sciences since corroboration, comprehension and
systematisation are far more complex. Conceptualisation is based on complex concept and
conception spaces.
2.4 Handling forgetful mappings to IT and DBMS technology
In is often claimed that conceptual database or data models are mainly descriptive ones.
Description is, however, only one of the functions that a conceptual data model has in
a system development scenario. Other typical scenarios are documentation, prescription,
communication, negotiation, and explanation. These scenarios are also observed for
humanities.
In system construction we transform the conceptual data model to corresponding realisation
models. This transformation also changes the semantics from rich semantics of conceptual
models to lexical semantics which is based on the lexical interpretation of the words used
in realisation models according to the meaning in the given application area. It is thus
forgetful. The reestablishment of the conceptualisation must thus be handled by a reference
to the conceptual model what also means to use a tight bundling of all models in the case of
system maintenance (e.g. evolution and migration) and integration. The social dimension
and the foundation dimension get also lost during transformation.
2.5 Sophisticated conceptual models are model suites
Based on the observations, we should consider a conceptual model as a model suite, i.e. a
coherent collection of explicitly associated models. The associations are explicitly stated,
enhanced by explicit maintenance schemata, and supported by tracers for the establishment
of coherence [8]. Each model in the model suite has its orientation and its functions in
utilisation scenarios. The association schema among the models allows to consider the
model suite as a complex but holistic model.
Model suites in most sciences and engineering incorporate some conceptual models. This
situation is not different for social sciences. For instance, the CRC 1266 [1, 2] uses as a
complex model of transformation a model suite consisting of models for socio-economic
formation (cluster B-E), for socio-environmental components of change (cluster F), and for
natural science investigation (cluster G). The interplay of these models allows to suppose
hypotheses and to draw conclusions. Most models are already conceptual ones. They use,
however, different conception spaces. The association among these models is handled by
interlinkage groups within the CRC.
18 Yannic Ole Kropp, Bernhard Thalheim
2.6 Models as mediating instruments instead of middle-range theories
Middle-range theories [2] are essentially model suites. They are used for an integrating
consideration of quantitative sources and theory conceptions. Quantitative sources are
used for derivation of quantitative concepts. The theory offer underpins these concepts.
Qualitative theory-oriented research uses theoretical concept(ion)s. These concepts are
supported by supporting sources which are often generated before and might use the current
quantitative sources. A theory integrates these concepts. We use typically several theories,
e.g. for plausibility check, for investigation, explanation, knowledge experience propagation,
and discovery scenarios. In a proxy-based research we start with proxy sources that might
be underpinned by proxy concepts. This research results in a theory request that can be
satisfied by a theory offer.
This approach often results in a gap between qualitative and quantitative research. Models
can be used to render the theory offer. At the same time, models may also render a
qualitative theory. The rendering procedures are typically different. A model suite can
now be constructed by models for theoretical concepts from one side and by models for
quantitative concepts from the other side. In this case, we use models for the quantitative
theory offers and for the qualitative theories. This approach is depicted in Figure 2.
quantitative models qualitative
research as mediators theory-oriented
(s, q)
f(s, t ) research
(s, q) (s, t ) (
quantitative f(s, m) supporting f(s, m) supporting
/ sources o
sources O O
sources
(s, q) (c, m) (c, t )
g(c, q)
G(s, m) G(s, t )
(c, q) (c, t )
quantitative f(c, m) model f(c, m) theoretical
concepts / concepts
_ o
concepts
_ _
(c, q) (c, m) (c, t )
hq hm ht
theory offer
offer− / model o theory−driven theory
driven view view
Fig. 2: Models as integrating and mediating instrument for conceptualisation, investigation, explanation,
knowledge experience propagation, and discovery
This approach has already used for the investigation in the CRC 1266 [2]. In a similar form
we can consider now conceptual models for other application cases.
Conceptual Modelling and Humanities 19
3 Concluding: Conceptual Modelling Inspired by Humanities
Conceptual modelling is a widely used practice in many science and engineering disciplines.
The current practice used for database conceptualisation can be enhanced by a number of
insights that we observed in social science research.
• The concept(ion) space is often far more complex structured than finally represented
and used for a singleton conceptual model. We should consider conceptual models
that orient on different aspects and different levels.
• The context dimension should not be neglected for conceptual models.
• The social dimension and the foundation dimension are equally important as the
representation dimension.
• Models and especially conceptual models consist of a number of models and thus
form a model suite.
We got now additionally a number of special necessities for conceptual modelling without
which conceptual models are of low quality, not justified, and also not adequate.
Deep models: Models consist of normal sub-models and deep sub-models. The first ones
are given in an extrinsic and explicit form. The later ones are often concealed.
Model mould: The second element of the matrix of modelling is the mould. We
know a number of canonic approaches that guide the modelling process, the modelling
outcome, and the capacity of the finally developed model.
Concept-biased modelling: Conceptual models are typically deeply biased by the
concepts in a given domain. Concepts such as “village”, “settlement” and “center” are
essentially representing the same understanding but are used in very different contexts. The
same applies to database models, e.g. the concepts of “Person” or “Address” depend on
geographic, law etc. assumptions.
Functions of models as the guiding principle: The utilisation scenarios determine the
functions that a model has in such scenarios. The model is an instrument in these scenarios.
Whether it is a proper and fit-to-use instrument depends on the function the model has (and
thus on the purpose and the goal).
References
[1] CRC 1266. Scales of transformation - Human-environmental interaction in prehistoric and
archaic societies. Collaborative Research Centre. http://www.sfb1266.uni-kiel.de/en/, accessed
May 13, 2018.
[2] V.P.J. Arponen, S. Grimm, L. Käppel, K. Ott, B. Thalheim, Y. Kropp, K. Kittig, J. Brinkmann,
and A. Ribeiro. Between natural and human sciences: On the role and character of theory in
socio-environmental archaeology. The Holocene, 29(10, Special Issue “Scales of Transformation:
Human-Environmental Interaction in Prehistoric and Archaic Societies”):tba, October 2019.
20 Yannic Ole Kropp, Bernhard Thalheim
[3] A. Dahanayake and B. Thalheim. Development of conceptual models and the knowledge
background provided by the rigor cycle in design science. In Models: Concepts, Theory, Logic,
Reasoning, and Semantics, Tributes, pages 3–28. College Publications, 2018.
[4] S. Deicher. The language of objects. BMBF Project KunstModell in Egyptology,
https://www.bmbf.de/files/Kurztexte_SdOIII.pdf, 2018.
[5] D. Embley and B. Thalheim, editors. The Handbook of Conceptual Modeling: Its Usage and Its
Challenges. Springer, 2011.
[6] GSHDL. Graduate school at Kiel University Human Development in Landscapes.
http://www.gshdl.uni-kiel.de/, accessed July 26, 2019.
[7] H. Jaakkola and B. Thalheim. Cultures in information systems development. In Information
Modelling and Knowledge Bases XXX, pages 61–80. IOS Press, 2019.
[8] M. Skusa and B. Thalheim. Wissenschaft und Kunst der Modellierung: Modelle, Modellieren,
Modellierung, chapter Kohärente Multi-Modell-Entwicklung, pages 431–454. De Gryuter,
Boston, 2015.
[9] The DigiCULT team. DigiCULT: a portal of the museum network at Schleswig-Holstein and
Hamburg. http://www.digicult-verbund.de/de and http://www.museen-sh.de/, accessed July 26,
2019.
[10] B. Thalheim. The conceptual model ≡ an adequate and dependable artifact enhanced by
concepts. In Information Modelling and Knowledge Bases, volume XXV of Frontiers in
Artificial Intelligence and Applications, 260, pages 241–254. IOS Press, 2014.
[11] B. Thalheim. General and specific model notions. In Proc. ADBIS’17, LNCS 10509, pages
13–27, Cham, 2017. Springer.
[12] B. Thalheim. Conceptual model notions - a matter of controversy; conceptual modelling and its
lacunas. EMISA International Journal on Conceptual Modeling, February:9–27, 2018.
[13] B. Thalheim. Conceptual modeling foundations: The notion of a model in conceptual modeling.
In Encyclopedia of Database Systems. Springer US, 2019.
[14] B. Thalheim. Conceptual models and their foundations. In Proc. MEDI2019, LNCS 11815,
pages 123–139. Springer, 2019.
[15] B. Thalheim and I. Nissen, editors. Wissenschaft und Kunst der Modellierung: Modelle,
Modellieren, Modellierung. De Gruyter, Boston, 2015.
Remark
We thank the reviewers for their remarks, suggestions, and critics. The paper is based on our previous papers
on models, e.g. on the generalisation of approaches used in design science research [3]. The compendium
[15] presents model notions and modelling used in agriculture, archaeology, arts, biology, chemistry, computer
science, economics, electrotechnics, environmental sciences, farming, geosciences, historical sciences, languages,
mathematics, medicine, ocean sciences, pedagogical science, philosophy, physics, political sciences, sociology,
and sports at Kiel university. It is based on a decade of Tuesday-evening-open-end discussions on models and
modelling in sciences. We selected only four of our collaboration projects from humanities research and discussed
some of the modelling lessons.
Conceptual Modelling and Humanities 21
Acknowledgement
This research was performed in the framework of the CRC 12664 ’Scales of Transformation -
Human-Environmental Interaction in Prehistoric and Archaic Societies’ which is funded by
the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation - Projektnummer
2901391021 - SFB 1266)5. We thank both institutions for enabling this work.
4 http://www.sfb1266.uni-kiel.de/en/
5 http://www.dfg.de/en/index.jsp