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The Science of Conceptual Modelling
CAU@Kiel, Vorlesung SS 2012
WInf-BAppE: Selected Topics in Business Application Engineering (WInf-BAppE) (080001)
Part II
SS 2012
Bernhard ThalheimDr. rer.nat.habil.
Prof. @ Christian Albrechts University at Kiel, GermanyDepartment of Computer Science
Information Systems Engineering Group(∗) Kolmogorov Professor h.c. @ Lomonossov University Moscov, Russia
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Semiotics
Model suitePatternKnowledgeConceptsPhilosophyParadigmsFoundationsFinally
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Foundations of ModellingTowards a Science of Modelling
• Semiotics of Modelling
• Semantics
• Model suite
• Model pattern
• Knowledge in the model
• Concepts
• Philosophie of Science
• Knowledge and paradigms
• Summarising
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Semiotics of ModelsSyntax
well built
easy to express
Semanticswell defined
easy to understand
Usagewell applicable
easy to apply
Applicationwell supported
of high value
It is written: “In the beginning was the Word!”
Even now I balk. Can no one help?
I truly cannot rate the word so high.
I must translate it otherwise.
I believe the Spirit has inspired me
And I must write: “In the beginning there was Mind.”
Think thoroughly on this first line,
Hold back your pen from undue haste!
Is it mind that stirs and makes all things?
The text should state: “In the beginning there was Power!”
Yet while I am about to write this down,
Something warns me I will not adhere to this.
The Spirit’s on my side! The answer is at hand:
I write, assured, “In the beginning was the Deed.”
Goethe, Faust I, Faust’s Study
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Separation of Concerns Based on theSemiotic Triangle of Content, Concepts
and Topics
Semantics Pragmatics
Syntax
Content
Computation
Concept
Validation
Topic
Presentation
Presentation theory
Computation theory
Model theory
Infon
Semanticalunit
Asset
interpretation
foundation
-presentation
explanation
K
contentdelivery
U
annotation
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The Mappings of the Syntax, Semantics,and Pragmatics Dimensions
Enrich,integratetopics
I
deliverassets
explain by
infons
Derivationof concepts
-
interpretthrough units
represent
by infons
Computingand ETL ofcontent
R
foundby units
annotatethrough assets
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Components of a (Meta-Semiotic) Logics• Syntactic constituent:
which symbols are going to be used in the language, which combinations
(wordS) of these symbols are allowed, which constructors can be (partially)
used if there is a chance that expressions can be constructed inductively
• Semantic constituent:
what is the purpose of the language, which structures are of interest, what is
going to be expressed
• Constituent that relates syntax and semantics, e.g. defintion of truth (or ap-
propriatedness):
which structures or expressions are true or meaningful or potentially meaning-
ful?
• Pragmatic constituent:
which meaning can be canonically assigned to words; which restrictions must
be considered, which closure operators are applicable
typically finiteness assumption
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The “Playground” of Logics see too: Semantics in DB & KB
Signature of a language and structures: for description
assumption: signature embedding of structures
Construction of the language based on constructors for words
assumption: inductive construction
special assumptions for inherentlycyclic language constructs
Conceptionalisation of structures with quality properties, e.g.
“true”
assumption: observability of properties
assumption: canonical truth values
Association of language and structures by interpretation of
language constructs with structural properties
assumption: canonical association
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Syntax, Semantik, Pragmatik
Morphologie: (Wortbildunglehre) Zusammensetzung von Wortern
aus Stammen, Vor- und Nachsilben, sowie anderen Wortern
Syntax: (Lehre vom Satzbau) strukturelle Beschreibung des Satzbaus
Subjekt, Pradikat, Objekt, Schema der Fragebildung
Was ist denn das Grune hier in der Suppe?
Semantik: (Bedeutungslehre) formale Beschreibung des propositio-
nalen Gehalts
((grun(x), Suppe(y), in(x,y)) , ?)
Pragmatik: (umstandbezogene Bedeutungsinterpretation) Verwen-
dung in der kommunikativen Situation je nach Kontext
intendierte Funktion: Information, Lob, Kritik, ...
mogliche Antwort: Wenn es dir hier nicht schmeckt,kannst du ja woanders essen gehen!
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Besonderheiten der naturlichen Sprache
• Semantisch fixierte Grundelemente (Wortschatz), generelle Aus-
drucksmittel (Grammatik, Referenz)
• Vielfalt von Ausdrucksformen und Sprechhandlungen
• Primar ausgelegt auf die aktuelle Herstellung von Sachbezugen,
situationsabhangige Charakterisierung
• Objektivierte Darstellung durch Abstraktion und Generalisation
• Metasprache fur sich selbst
• Hohe Fehlertoleranz
• Keine Unterscheidung des Abstraktionsniveaus (wahrContent,
wahrKonzept (Metasprache), wahrSymbol (intensional))
Epimenides: cSubstitution basierend auf Abkurzung ist wahrKonzept gdw.
cmetasprachliches Korrelat zumobjektsprachlichen Wort ‘c′ nicht wahrContent ist
(c.= ‘c ist nicht ein wahrer Satz’)
• Unterschiedliche Folgerungseigenschaften (Junktoren, Quantoren)‘Julia findet ein Einhorn’ = true ⇒ ∃ Einhorn
‘Julia sucht eine Einhorn’ = true ⇒ ∃ Einhorn
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Eigentumlichkeiten der naturlichen Sprache kein Deffekt, okonomisch. flexibel
Ambiguitaten lexikalische (Bank), strukturelle (Wort-, Satzstruk-
tur), referentielle (Wohnungen der Vermieter und ihre Adressen),
pragmatische (Konnen Sie ...? Ja!), semantische (IC, die Hohe al-
ler Berge in SH)
Vagheit als Konzept zur schrittweisen Fokussierung (viele, manche,
(fast) alle, in, mit haben, von)
Elliptische Verkurzungen: intrasententielle Ellipsen (Vorwarts-,
Ruckwartsellipse (Preiswerteste und teuerste Wohnung), kompo-
sitionale Ellipsen (Vorerwahnung, 1. Wohnungen mit Dusche; 2.
mit Bad (Ersetzung); 3. am Westufer (Zusatz))
Tempus, Modalitat, Metaphorik mit Einbettung in den Kon-
text, Kodierungen (Der Flur sieht aus wie ein Schlauch)
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The Place of Semiotics
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Semiotics of Models: Syntax: Morphology
• science of word form structure.
• classified according to their categories and roles within a model and according
to their specific expression within a model
• ruled by inflection, deviation, and composition
lemmatisation (reduction of words to their base form) and characterisation by the
(morpho-syntactic) role within a model
• morphological features:
• full or partial specification,
• layering within a model,
• integrity constraints,
• cyclic or acyclic structuring,
• complete set of schemata for cognitive semantics, open or closed context,
and kind of data types.
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Basic Categories
• Person
• Thing
• Event
• Action
• State
• Time
• Place
• Direction
• Attribute
• Manner
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Morphologisches Lexikon Woher weiß der Parser, welches Feature ein gegebenes Wort hat?
Morphologisches Lexikon
versus Vollformenlexikon
lieben,liebt, liebte, geliebt,
ungeliebt
Im Lexikon stehen fur jedes Wort der Sprache
• sein Sprachpartikel (syntaktische Kategorie)
• ggfs. nodefault values fur die Merkmale (features)
liebte
• Information uber seine Bedeutung (kommt spater)
• ggfs. Verweis auf seine Stammform (root) etc.
boys → boy + (n-number = 3p)dishes → dish + (n-number = 3p)playing → play + (tense = progressive)
+ (v-number = 1s 2s 3s 1p 2p 3p)taken → take + (tense = pastp)
+ (v-number = 1s 2s 3s 1p 2p 3p)loves → love + (tense = present) + (v-number = 3s)played → play + (tense = past)
+ (v-number = 1s 2s 3s 1p 2p 3p)
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Lexikoneintrage fur den Parser
• Aufbau eines Lexikons z.B.
mit einer Funktion “dictiona-
ry”
• Jeder Lexikon-Eintrag hat
eine der folgenden Formen:
(word part-of-speech–feature-assignments– )
(word root-form part-of-
speech–feature-assignments– )
↑optional
(dictionary
(a det)
(be auxverb (tense = tenseless))
(is be auxverb (tense = present)
(v-number = 3s))
(block noun)
(block verb)
(can modal
(v-number =1s 2s 3s 1p 2p 3p))
(do modal)
(did do modal (tense = past)
(v-number = 1s 2s 3s 1p 2p 3p))
(fish noun (n-number = 3s 3p))
(frog noun)
(jack proper-noun)
... )
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Semiotics of Models: Syntax: Namespaceand Lexigraphy
• coding and structuring lexical elements based on the lexicon
‘name’, ‘description’, ‘identifier’
• general and an application-dependent namespace
• model as product of a community of practice with its needs, its common-speak,
its specific functions of words, its specific phrases and abbreviations, and its
specific vocabulary.
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Semiotics of Models: Semantics:Lexicology
• ontologies
• linguistic relations such as homonym, antonym, paronym, synonym, polysemy,
hyponym, etc.
• meanings in the namespace:
• referential meaning establishes an interdependence between elements and
the origin (‘what’);
• functional meaning is based on the function of an element in the model
(‘how’)
intext (within the model), the general, the part-of-model, and the differential
(homonym-separating) meaning
• change of meaning for legacy models
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Ontology
Ontology is a classification with a set of constraints representing subtyping,partition, disjointness, covering and incoherence, more specifically
• set of terms (objects) of interest in a particular domain (O = o1, ...., on) and
relationships (R = R1, ...., Rm) among them (ontological commitment)
relating concepts with kinds, valuation (value, modality, existency) and actors (worlds)
• oi = (k, idi, vi) for k ∈ Kind, vi value form DOM(O) , idi ∈ ID,
• Ri = rj = (trj , oj,1, ..., oj,k, oj) | trj ∈ TR, oj,l parameters of rj
• Kind = predicator thing, action, actor, rule (predicator/structural view; actor+action/dynamic view; rules/deontic view)
• TR = execute, actand, use, extend, ... (dynamic view, e.g., actand )
Shared ontology of two communities G1 and G2 with A1 and A2 defined by:
Common generic extensible ontology A that can be mapped by infomorphisms
(f1, g1) to A1 and by f2, g2 to A2
greatest consistent classification that is finer (or equal) than A1 and A2
Core classification A∗ of the communities is defined by the fusion of the clas-
sification lattice theories of A1 and A2 modulo synonyms of A1 and A2,
respectively
with a local classification theory (coincide on common classification, local
on non-shared classifications)
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General Semantics Goal: Clarifying semantics beyond CS
• Semantics as one constituent of semiotics
• Semantics with a variety of notions and approaches
• Resulting misunderstanding among communities
• Be aware of hidden semantics of languages, e.g., well-formedness
constraints of the ER model
• Models and schemes are restricted by their languages and developed
with some intentional incompleteness
• Variety of notions of implication and consequence
• Axiomatisation as one issue, complexity of derivations as another
one Take home: integrating logics, linguistics, philosophy, CS
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General Notions of SemanticsSemantics is the study of meaning, i.e. how meaning is constructed,
interpreted, clarified, obscured, illustrated, simplified, negotiated,
contradicted and paraphrased.
Scientific community: ‘always valid’ semantics based on the ma-
thematical logic
Database modellers: ‘strong’ semantics for constraints
Database analysers and miners: ‘may be valid’ semantics
Users: ‘in most cases valid’ semantics
based on prototypes or exemplars remembered
Groups of users: ‘epistemic’ semantics depending on the group
Intuition-driven: ‘hidden’ experience-based semanticsK.-D. Schewe, B. Thalheim: Semantics in Data and
Knowledge Bases. SDKB’08, LNCS 4925
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Six (or more) Kinds of Semantics
Lexical semantics: words in a language
word meaning and of association of words
“Semantic” Web: rudimentary form of word semantics for meta-
characterisation
Grammatical semantics: categories ‘noun’ , ‘verb’ ‘adjectives’
combinatorial semantics - specific form of grammatical semantics
Statistical semantics: meaning on co-occurrence of words, on pat-
tern of words in phrases, and on frequency and order of recurrence
Logical semantics: relation between the formal language of logics
and structures or worlds
Prototype semantics: meaning through users evolving experience
Program and dynamic semantics: semantic memory, i.e. the
memory of meanings, understandings, and other concept-based
knowledge unrelated to specific experiences of agents
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Formal Semanticsis typically given
• by an interpreter that maps syntactic types to semantic types,
• by a context abstraction that is based on an aggregation of values
which remain fixed in certain temporal and spatial intervals,
• by states that provide a means of representing changes over time
and space,
• by an configuration that is based on an association of contexts and
states,
• by an interpretation function that yields state results based on cer-
tain computation,
• by an evaluation function that yield some value results for the syn-
tactic types, and
• by an elaboration function that yield both state and some other
value results.
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Variations of Semanticsmappings logics closed
world
logics
logics on finite
worlds
logics on natural
worlds
matching of syntactic
language L and seman-
tic structure worlds Won signature τ
exact or
coincidence
embedding
exact exact or coin-
cidence embed-
ding
partial depen-
ding on interest
and meaning in
use
considering context no no no depending on
use and user
considering states any any only finite
structures
states in scope
restricting states and
context
no no no depending on
interest and
demand
interpretation for alpha-
bets
exact for al-
phabet
exact potentially
restricted
multiple inter-
pretations
evaluation of variables full full full partial evaluati-
on
elaboration full negation
as failure
derivable
structures
extrapolation
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Semantics in Computer ScienceOperational semantics interprets syntactic types by computational types of a ma-
chine
Denotational semantics associates mathematical functions to syntactic types
Axiomatic semantics uses a calculus with axioms and proof rules
Transformational semantics uses mappings to other syntactic types
categorical or functorial semantics: translation to category theory
Algebraic semantics uses a set of abstract basic syntactic systems with their
semantics and a set of rules for construction of more complex systems based
on these systems
Macro-expansion semantics is based on static or dynamic inductive rewriting of
syntactic types and allows to introduce abstractions such as the types of the
λ calculus
Grammar semantics uses a state consisting of semantic category variables and
on instantiations for atomic non-terminal syntactic types
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Reduction (/Database) Semantics(Indoeuropean Variant) Deductive normal form with generation based on knowledge and context
c⃝P. Broman: Survey on R. Hausser database semantics
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Overuse and Misuse of Semantics The word ‘semantics’ has still a positive co-notation.
semantic web, semantics as a metamodel, semantics as context, se-
mantics as behaviour, semantics as being executable, semantics as the
meaning of individual constructs, semantics as mathematical notation,
semantics as mappings, ....
Semantic web: micro-semantics of wordings, vocabulary of name
spaces or of ontologies
semantification of semantic web
Separation of semantics and behaviour: confusion of semanti-
cs and behaviour,
Semantics illustration through diagrams: UML 100+x types
of diagrams, spatial restriction, consistency and coherence of dia-
grams
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Semantische Interpretation
Prinzip der kompositionellen
Semantik. (Theorie uber Ver-
stehen der Bedeutung von Spra-
che)
(1) Syntaktische Analyse
(2) Ableitung der Bedeutung
des Ganzen aus der Bedeu-
tung der Teile.
Es gibt andere Formen der se-
mantischen Interpretation.
• Zuordnung von Bedeutungen zu den einzelnen
Wortern (zu finden in einem Konzept-Lexikon):
red --> (color ?x red)
block --> (inst ?x block)
the --> retrieve-val
• Kombination der einzelnen Wortbedeutungen, um
Bedeutung der Wortgruppen zu erhalten:
red block --> (and(inst ?x block)(color ?x
red))
the red block --> (retrieve-val ’?x
’(and (inst ?x block) (color ?x
red)))
• Anbindung dieser Prozesse an den syntaktischen
Parser
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Ambiguitatsprobleme Lexikalische, strukturelle, semantische
• Mullers sahen die Alpen, wahrend sie nach Italien flogen.
(Wer oder was fliegt hier?)
• Ich nahm das Essen mit Messer und Gabel ein.
• Ich nahm das Essen mit Erwin und Gabi ein.
• Can companies litter the environment
(?)
• Ich sah den Mann im Park mit dem Fernrohr
...nur undeutlich, da meine Linse schmutzig war.
...und es hatte den Anschein, daß er den Mond betrachtete.
(noch schlimmer:) I saw the man in the park with the telescope.
• I saw her duck. - ihre Ente oder Kopf einziehen
• Flying planes made her duck.
Planes: Flugzeuge, Ebene flying: aktiv oder ich bin dabei zu fliegen
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Zusammenhange, Ellipsen undAmbiguitaten Naturliche Sprache ...ist schwer, nicht nur fur Computer
Nimmer diesen Monitor legen, wo der Schnur von Personen darauf spazieren
gehen grausam behandelt wird.
Aus der deutschen Betriebsanleitung eines japanischen Fernsehmonitors (Quelle:
Spiegel 10/ 92)
Bundesministerin Claudia Nolte (26.10.1995): “Frauen werden in wirtschaftlichschwierigen Zeiten eher entlassen und spater als Manner wieder eingestellt.”
Scharping (10.95): “... und damit der Proporz gewahrt bleibt ...”
Scharping (10.95): “... Diese Runde war eine Niederlage, aber es werden weiterefolgen. Meine Damen und Herren ... Runden, Runden!!”
Lebende Karpfen - auch geteilt !
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Montague’s intensionale Semantik PTQ - proper treatment of quantification
Erweiterte Signatur mit Kategorien Ω′ ⊆ Ω fur Sorten und Verknupfungen
ω1/ω2, ω1//ω2 (Verkurzung)
(in)transitive Verben, Terme, modifizierte Adverbien, Appelative, ...
Typenkalkul fur Ausdrucke mit Zuordnung der Kategorien bzw. Sorten
Grundfunktionen F0(β).= every β, F1(β)
.= the β,
F2(β).= a(n) β (mit Vokalregel), F3,n(α, β)
.= α such that β
Funktionale Anwendung F4, F5, F6, F7
Konjunktion und Disjunktion F8, F9
Quantifizierung F10
Tempus- und Vorzeichenregeln F11, F12, F13, F14, F15 (z.B. negative 3.
Person Singular Prasens Perfekt)
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Montague’s intensionale Semantik Beispiel der Anwendung der Funktionen
man
Every man
he0
love
woman
he1
loves he0
C
she1 loves him0
woman such that she loves him0
a woman such that she loves him0
loves a woman such that she loves him0
he0 loves a woman such that she loves him0
Every man loves a woman such that she loves him
F10,0(F0(man), F4(he0, F5(love, F2(F3,1(woman, F4(he1, F5(love, he0)))))))
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Intensionale LogikUnterscheidung zwischen
• Wert (durch Interpretation, Referenz auf Extension (semantischer Wert))
• Bedeutung (durch Darstellung des Sinns, Referenz auf Intension)
Induktive Einfuhrung einer Typenlogik
• Entity und Truth sind Typen
• Elementartypen (Typen und Situationen (Welt, Zeitpunkt, Kontext))
• (a, b), (s, a) sind Typen fur Typen a, b, Situation s
mit Junktoren und Quantoren
Interpretation durch Mod(α,M,W, t, g) mit Struktur M in Welt W zum Zeit-
punkt t und Variablenbelegung g (Content(α) Extension(α))
Darstellung durch Sym(α,M, g) mit Struktur M und Variablenbelegung g
(Symbol(α) Intension(α))
Harmonisierung fur GetCont(Sym(α,M, g), w, t).= Mod(α,M,W, t, g) mit
Extension(Symbol(α)) = Content(α) fur Abbildungen
Extension : Symbol → Content und Intension : Content → Symbol
wobei i.a. nicht gilt Intension(Content(α)) = Sym(α)
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Semiotics
Model suitePatternKnowledgeConceptsPhilosophyParadigmsFoundationsFinally
Concept Topic
Content
Information
c⃝B. Thalheim
Semiotics of Models: Pragmatics:Community of Practice
study how languages are used for intended deployment functions in dependence on
the purposes and goals within a community of practice
• descriptive-explanatory and persuasive-normative functions of a model: (1) ac-
ting (2) within a community, especially the modeller and have (3) different
truth or more generally quality
• far-side versus near-side pragmatics separating the ‘why’ from the ‘what’ side
of a model
• development and deployment modes, model surface compositionality (metho-
dological principle), model presentation order’s strict linearity relative to space
(empirical principle), model interpretation and production analysed as cogniti-
ve processes (ontological principle), reference modelled in terms of matching
an model’s meaning with context (functional principle)
• methodologically valid, support subjective deductive (paradeductive) inference
with an open world interpretation, allow context-dependent reasoning (impli-
cature), provide means for collaborative interaction, weaken connectives and
quantifications, and integrate deductive, inductive, abductive and paradeduc-
tive reasoning.
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Semiotics
Model suitePatternKnowledgeConceptsPhilosophyParadigmsFoundationsFinally
Concept Topic
Content
Information
c⃝B. Thalheim
Semiotics of Models: Pragmatics:Visualisation (Phonology)
Principles of visual communication: Vision, cognition, and processing and
memorizing characteristics.
visual features: contrast, visual analogies, presentation dramaturgy, reading
direction, visual closeness, symmetric presentation and space and movement
Principles of visual cognition: ordering , effect delivery, and visualisation
model organisation, model economy , skills of users, and standards.
Principles of visual design: optical vicinity , similarity , closeness, symme-
try , conciseness, reading direction.
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Semiotics
Model suitePatternKnowledgeConceptsPhilosophyParadigmsFoundationsFinally
Concept Topic
Content
Information
c⃝B. Thalheim
Semiotics of Models: W∗H-SpecificationFrame
classical rhetorical frame
Hermagoras of Temnos: Quis, quid, quando, ubi, cur, quem ad modum, quibus
adminiculis
Who, what, when, where, why, in what way, by what means
• W4: wherefore (purpose), whereof (origin), wherewith (carrier, e.g., language),
and worthiness ((surplus) value)
• secondary characterisation W17H:
• user or stakeholder or community of practice characteristics: by whom, to
whom, whichever;
• characteristics imposed by the application domain: wherein, where, for what,
wherefrom, whence, what;
• purpose characteristics characterising the solution: how, why, whereto,
when, for which reason; and
• additional context characteristics: whereat, whereabout, whither, when.
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Semiotics
Model suitePatternKnowledgeConceptsPhilosophyParadigmsFoundationsFinally
Concept Topic
Content
Information
c⃝B. Thalheim
Result of Conceptual Modelling DependsOn
• information available about the UoD,
• information about the UoD, regarded as not relevant for the concept or conceptual
model at hands, and therefore abandoned or renounced,
• philosophical background to be applied in the modeling work,
• additional knowledge included by the modeler, e.g. knowledge primitives, con-
ceptual ‘components’, selected logical or mathematical presuppositions, mathematical
structures, etc.,
• collection of problems that may be investigated in this environment,
• ontology used as a basis of the conceptualization process,
• epistemological theory, which directs how ontology should be applied in reco-
gnizing and formulating concepts, conceptual models or theories, and in constructing
information, data, and knowledge, on different levels of abstraction,
• the purpose and goal of the conceptual modeling work,
• collection of methods for conceptual modeling,
• the process of the practical concept formation and modeling work,
• knowledge and skill of the person making modeling, as well as those of
the people giving information for the modeling work.
c⃝H. Kangassalo: : Approaches for Active Conceptual Modeling of Learning; Workshop San Diego 2006
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Semiotics
Model suitePatternKnowledgeConceptsPhilosophyParadigmsFoundationsFinally
Concept Topic
Content
Information
c⃝B. Thalheim
Separation of Concern: The ZachmanFramework Living in a world full of different models
Coherence, calibration, mapping problems
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ConceptualModellingScienceSS 2012
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Semiotics
Model suitePatternKnowledgeConceptsPhilosophyParadigmsFoundationsFinally
Concept Topic
Content
Information
c⃝B. Thalheim
Model Suites: Collaboration of Models Handling abstraction
Explicit collaboration of models based on
• constructors
• mappings
• contracts among models
Dimensions of models based on the minimalisation of models and
constructors
Abstractions of models among mappings
Constructors for construction of new models
shuffle product, reduct , scope , integration
Theory extension for model context representation and context in-
tegration
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ConceptualModellingScienceSS 2012
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Semiotics
Model suitePatternKnowledgeConceptsPhilosophyParadigmsFoundationsFinally
Concept Topic
Content
Information
c⃝B. Thalheim
Challenges in Multi-Model Environments
Explicit specification of model collaboration: interdependencies
among models
Integrated development of different models: different views of the
same problem or application
Co-evolution of models: exchange between models and change propa-
gation
Combining different (e.g., graphical) representations with ma-
thematical rigor of models
Evolution of different representations: refinements of previous mo-
dels or explicit revisions of models
Management of multi-model IS development: scheduling mecha-
nisms, rollback
Version handling for multi-model IS development: different versi-
ons
Explicit refinement and abstraction treatment: systems develop-
ment abstraction layers
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Semiotics
Model suitePatternKnowledgeConceptsPhilosophyParadigmsFoundationsFinally
Concept Topic
Content
Information
c⃝B. Thalheim
Layers of Models for IS Managing model suites by stratification
Collaboration speci-
fication layers
Infrastructure Collaboration
Application domain
layer
Application environment Collaboration policy, prin-
ciples, acts
Requirements layer System sketches, require-
ments, system decisions
Collaboration tasks, con-
tracts, style, pattern
Business user layer System view, parties, port-
folio
Collaboration stories
Conceptual layer Information system specifi-
cation, context support
3C-C schemata,
informational processes,
exchange frames
Logical layer Information system - logical
view
Collaboration supporting
system
Physical layer IS programs Collaboration programs
Deployment layer ... ...
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Semiotics
Model suitePatternKnowledgeConceptsPhilosophyParadigmsFoundationsFinally
Concept Topic
Content
Information
c⃝B. Thalheim
Model Suite: Constituents
• set of models M1, ....,Mn ,
• association or collaboration schema among the models,
• controllers that maintain consistency or coherence of the model
suite,
• application schemata for explicit maintenance and evolution of the
model suite, and
• tracers for the establishment of the coherence.
Coherence describes a fixed relationship between the models in a model
suite. only inductive languages with compositionality principle concentration on discrete domains
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Semiotics
Model suitePatternKnowledgeConceptsPhilosophyParadigmsFoundationsFinally
Concept Topic
Content
Information
c⃝B. Thalheim
Model Suite: LanguagesModel language L: signature S and a set of constructors C
ΣS,C well-formedness conditions
Model type TLS = (LS,ΣLS)language of the model andconstraints ΣLS ∈ L(ΣWellFormed
S )
Partial mappings Ri,j : LSi → LSj among LS1 , ...LSn
Model M: structM in LSthat obeys ΣLS ,
and set of constraints ΣM defined in the logics of this language.
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Semiotics
Model suitePatternKnowledgeConceptsPhilosophyParadigmsFoundationsFinally
Concept Topic
Content
Information
c⃝B. Thalheim
Model Suite: Model Association andContracting
Collaboration contract among models
Collaboration
• Communication is used in a variety of facets as an act or instance
of transmitting or a process by which information is exchanged
between models through a common system.
• Coordination expresses the act or action of coordinating the har-
monious functioning of models for effective results.
• Cooperation expresses the action of cooperating.
Collaboration style: supporting programs, data access pattern, style
of collaboration, coordination workflows
Collaboration pattern: supporting access and configuration, event
processing, synchronization, and parallel execution
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Semiotics
Model suitePatternKnowledgeConceptsPhilosophyParadigmsFoundationsFinally
Concept Topic
Content
Information
c⃝B. Thalheim
Model SuiteModel suite type ST = (TLS1
, ..., TLS ,ΣLS1 ,...,LSn)
model types TLSidefined on a set LSi , ...,LSn
ΣS1,...,Sn constraints
Model suite Son a model suite type ST
models (M1, ...,Mn) of type TLSithat obey ΣLS1 ,...,LSn
Contract on C:
• constraints ΣLS1∪ ... ∪ ΣLSn
∪ ΣLS1 ,...,LSn,
• description of the enforcement mechanisms for any operation that
can be used for modification of one model, and
• set of consistent evolution transformations.
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Semiotics
Model suitePatternKnowledgeConceptsPhilosophyParadigmsFoundationsFinally
Concept Topic
Content
Information
c⃝B. Thalheim
Model Suite: Synchronisation andCoherence
Explicit mapping among models
Mi Mi,to(j) Mj,from(i) Mj
extract ei,j transform ti,j load li,j- - -
Mi Mi,from(j) Mj,to(i) Mj
load lj,i transform tj,i extract ej,i
modeli modelj
ei,j
ej,ilj,i(tj,i(ej,i))
li,j(ti,j(ei,j))-ei,j
tj,i(ej,i)
ti,j(ei,j)
ej,i
ModelTransformer
-
Coordinationprofile
evolution-prone
completed to a model suite architecture
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ConceptualModellingScienceSS 2012
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Semiotics
Model suitePatternKnowledgeConceptsPhilosophyParadigmsFoundationsFinally
Concept Topic
Content
Information
c⃝B. Thalheim
Model Suite: Co-evolution
Databaseschema
Staticintegrity
constraints
Database structuremodelMstruct
Databasefunctionality
model
Dynamicintegrity
constraints
Database functionmodelMfunc
Contentmodel
Mcontent
Interactionmodel
Minteract
... model
Database support models
Information system modelMIS
M′struct M′
content
Mstruct Mcontent
put∗struct,content
put∗content,struct
-
?
changecontent
M′funct M′′
struct
Mfunct
put∗struct,funct
put∗funct,struct
-
changefunct
?M′′
content
put∗struct,content- M′′interact
put∗content,interact-
M′interact
put∗content,interact-
Minteract
put∗content,interact-
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Semiotics
Model suitePatternKnowledgeConceptsPhilosophyParadigmsFoundationsFinally
Concept Topic
Content
Information
c⃝B. Thalheim
Collaboration within model suites Binding, integration, and calibration of models
Structural and functional integration among layers with
the
• bindings for export and import interfaces of models
provisioning of data and schemata
• exchange and mapping facility
Coordination with model suites for consistency management
responsibilities, obligations, permissions and protection
Co-evolution of model suites with evolution choreography, con-
figuration, versioning, orchestration
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Semiotics
Model suitePatternKnowledgeConceptsPhilosophyParadigmsFoundationsFinally
Concept Topic
Content
Information
c⃝B. Thalheim
Collaboration of models Explicit and eager bindings for models in suites
Export/import interfaces S = (I,F ,ΣS) specifying
Infon models I = (V,M,ΣT ) specifyingthe content V based on media types (“what”),the collaboration manager M (“how”), andthe competence ΣT through a set of tasks (“for what”)
Collaboration characteristics F specifying the organization frame (“how”),the parties (“who”) and the context (“whereby”)
Quality of collaboration ΣS agreeing on the quality and motivation (“why”)
Exchange frame specifying
Architecture drafting the general engine (“where”)
Collaboration style drafting the flow (“when”)
Collaboration pattern describing the functionality (“how”, “whereby”)
as a generalization of distributed systems, communication systems,
groupware systems, and collaboration architectures
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Semiotics
Model suitePatternKnowledgeConceptsPhilosophyParadigmsFoundationsFinally
Concept Topic
Content
Information
c⃝B. Thalheim
Model change propagation in a model suite
Simple merge: M1 = M1,0 dM1,2, M2 = M2,0 dM1,2
with M1,0 gM1,2, M2,0 gM1,2
Complex collaboration among Mi = Mi,0 Mi,1 from Li
and Mj = Mj,0 Mj,1 from Lj
Mappings ti,j : Mi,1 7−→ Mj,1 tj,i : Mj,1 7−→ Mi,1
for which extensions of Si,j, Sj,i exist in Li and Lj
Mi Mi,1 Mj,1 Mj
extract ei,j transform ti,j load li,j- - -
puti,j := ei,j ti,j li,j
Mi and Mj are coexisting if puti,j(Mi) 4Mj and putj,i(Mj) 4Mi
constant complement ~(Mj, i) = Mj ej,i(Mj)
Mj = put∗i,j(Mi, ~(Mj, i))
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Semiotics
Model suitePatternKnowledgeConceptsPhilosophyParadigmsFoundationsFinally
Concept Topic
Content
Information
c⃝B. Thalheim
Hierarchical Layered Model SuitesTypical example for models at the same abstraction level
microDBS
mesoDBS
analysisDBS
presentationDBS
DBS(Sp,ΣSp ,
Op,ΣOp )
inject
insert--modifiable
injected
auxiliarydatabase
?injectedDBS
(Sc,ΣSc
Oc,ΣOc )
inject
insert--modifiable
injected
auxiliarydatabase
?injectedDBS
(Sr,ΣSr ,
Or,ΣOr )
inject
insert--modifiable
injected
auxiliarydatabase
?injectedDBS
(Sm,ΣSm ,
Om,ΣOm )
auxiliarydatabase
?injected
sensors,observations,
transaction data
storage, capturing,
historical integra-
tion, leverage, ar-
chiving
analysis, mining,
exploration, hypo-
theses generation
business sheets,
appendix for pu-
blication, web
presentation
explicit inheritance of underlying data
ownership principle
explicit explanation based on underlying data
agreed stratification of data and schemata
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Semiotics
Model suitePatternKnowledgeConceptsPhilosophyParadigmsFoundationsFinally
Concept Topic
Content
Information
c⃝B. Thalheim
Strictly vertically layered model suites Layers as a building block of multi-layered architectures
Architecture of a multi-layer model suite
Operators: Oi,1, ...
Data objects: ti,1, ...
Operators: Oi+1,1, ...
Data objects: ti+1,1, ...
Layer i “realises” -
Layer i+1 “uses”
+ Layer language
Oi+1,p(ti+1,q)
Oi,r(ti,1, ..., ti,k), ..., Oi,s(ti,1, ..., ti,m)
?
6 Y
The general transformer structure for transformation of data among layersLayer i+ 1
Transformation meta-data Rules
Layer i
Inject ControlTransformer
?6
-
?6
Abbildung 1: The general transformer structure for transformation of data among
layers
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Semiotics
Model suitePatternKnowledgeConceptsPhilosophyParadigmsFoundationsFinally
Concept Topic
Content
Information
c⃝B. Thalheim
Model suites at the same abstraction layer
The four dimensions of the collaboration space for model suites: Con-
tent dimension through data, foundation dimension through concepts,
motivation dimension through targets and goals, validation dimension
through hypotheses
Concept space
Data space
Hypothesis space
Target and goal space
Model suitecollaboration
Tasks, aimassociation
Services,culture, user context
Databasespartial integration
Media typesfunctionality, adaptation
Semantic theoriesontology
Pragmaticsgeneral culture
Assumptionsmodality
Probability spacedistribution
AttributesIndicatorsSchemataBehaviourSimilarity/Separation
AttributesData
CorrelationsProbability space
Metrics/Amalgamations
AlgorithmsQuality
MappingsSchemata
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Semiotics
Model suitePatternKnowledgeConceptsPhilosophyParadigmsFoundationsFinally
Concept Topic
Content
Information
c⃝B. Thalheim
Institutions and (co-)evolutions of models
concept
world W1
concept
world W2
e.g., concept realisation/representation
evolved concept
world W3
e.g. ASM
BPMN
e.g. BPMN
XPDL, BPEL,...
e.g. revised BPMN
adapted XPDL, BPEL,...
content objectso1 ∈ DB1
content objectso2 ∈ DB2
selection /restriction
content baseDB1
schemaS1
observations,scopes
languageL1
modelling =⇒
=⇒
=⇒
→
→
→
→
→
---category mapping
---language mapping
---compiled schema mapping
---ETL (info)morphism
signatureτ1
inducedselection/restriction
content baseDB2
schemaS2
languageL2
signatureτ2
inducedmodelling
inducedobservations,
scopes
evolved content objectso3 ∈ DB3
evolved content baseDB3
evolved schemaS3
evolved languageL3
evolved signatureτ3
?
?
?
?
?
?
?
?
j
j
j ?
?
?
?
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ConceptualModellingScienceSS 2012
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Semiotics
Model suitePatternKnowledgeConceptsPhilosophyParadigmsFoundationsFinally
Concept Topic
Content
Information
c⃝B. Thalheim
Model suites for OLTP / DW / Marts Handling abstraction
Portfolioof user groups
Cubefunctions
OLAP schemata
OLTP-DW/OLAPload
function
?
-
?
Family ofgrouping functions
Family oftransformations
Cube schema
OLTP-DW/OLAPtransformation
function
?
-
?
OLTP schema
Extended extraction schema
Parameterisedattributes
Aggregationfunctions
OLTP-DW/OLAPextractionfunction
?
?
-
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ConceptualModellingScienceSS 2012
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Semiotics
Model suitePatternKnowledgeConceptsPhilosophyParadigmsFoundationsFinally
Concept Topic
Content
Information
c⃝B. Thalheim
Model suites for streaming / microdata /mesodata / macrodata Handling abstraction
data stream profile
• characterising data such as precision, granularity and encoding of
the data,
• context association data such as spatial and temporal data for geo-
referencing etc.,
• quality data such as distribution of errors,
• docket data that characterise the data gathering and utilisation
process,
• restrictions for access, and
• privacy and security profiles specifying allowed tasks, forbidden ser-
vices, rights for access, contracts and consent applicable.
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ConceptualModellingScienceSS 2012
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Semiotics
Model suitePatternKnowledgeConceptsPhilosophyParadigmsFoundationsFinally
Concept Topic
Content
Information
c⃝B. Thalheim
Dimensions of Data Modelling: Profile andPortfolio, Abstraction and Extension, and
Quality of Data
-
6
Data profile and portfolio
Data quality improvement
Data aggregation, abstraction and enrichment
Application A Application B
micro-datamacro-data
annotated aggregated macro-datafounded annotated aggregated macro-data
Gossip/raw/sensor/source dataStaged/cleansed data with data profiles
Consolidated and transformed data with hocks for data change captureIntegratable data ready for on-demand use in federations
Coherent data enhanced by services for in-line delivery in enterprise data farms
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Semiotics
Model suitePatternKnowledgeConceptsPhilosophyParadigmsFoundationsFinally
Concept Topic
Content
Information
c⃝B. Thalheim
Principles of Multi-Layered Modelling
Downward-dependency principle: The main data dependency structure is top-
down. Objects at a higher level depend on objects at a lower level.
Upward-notification principle: Objects at a higher level act as subscribers to data-
base changes at lower level. They may decide whether they eagerly or lazily
enforce observed changes at lower level. Objects at lower level report however
their changes.
Neighbor-communication principle: Objects may exchange data only at the same
layer with other objects. The neighborhood may also require that neighboring
databases should be synchronised.
Explicit association principle: The data exchange between databases is explicitly
documented and recorded. Whenever a database at a higher level perceives
data from a lower level then this exchange is logged.
Cycle elimination principle: Cyclic data exchange between layers is broken based
on the log information.
Layer naming principle: Data belong to their level and can be identified at their
level.
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Semiotics
Model suitePatternKnowledgeConceptsPhilosophyParadigmsFoundationsFinally
Concept Topic
Content
Information
c⃝B. Thalheim
Structural and Functional Integration ofModels
Mappings and actual data mappings (synonyms, homonyms, model suite
hypernyms and hyponyms)
partiality of data mapping, abstractions
• Microdata as the starting point for collaboration
• Mesodata through abstractions, filtering, scoping, summarisation
• Macrodata for model injection
Handling missing, intentionally not available, not applicable, biased and low
quality data
Informorphism among different equivalent presentations
Function integration as generic functions depending on the model
Constructor integration depending on the data profile and on the task port-
folio
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Semiotics
Model suitePatternKnowledgeConceptsPhilosophyParadigmsFoundationsFinally
Concept Topic
Content
Information
c⃝B. Thalheim
Requirements for model suite handling• Explicit specification of model suite collaboration: Interdependencies among models must be
given in an explicit form. The consistency of models must be recursive.
• Integrated development of different models: Models are used to specify different views of the
same problem or application. They must be used consistently in an integrated form. Their
integration must be made explicit. Simultaneous updates of models must be allowed.
• Co-evolution of models: The model suite must allow data exchange between models. Changes
within one model must be propagated to all dependent models.
• Combining different representations with mathematical rigor of models: Each model must
have a well-defined semantics as well as a number of representations for display of model
content. The representation and the model must be tightly coupled.
• Evolution of different representations: Changes within any model, must either be refinements
of previous models or explicit revisions of such models. These changes must be enforced for
other representations as well whenever those are concerned too.
• Management of model suites: The propagation of changes must be supported by scheduling
mechanisms, e.g., ordering of propagation of model changes. The management must support
rollback to earlier versions of the model suite. The management should also allow model
change during propagation.
• Version handling for model suites: Model suites may have different versions.
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Model suitePatternKnowledgeConceptsPhilosophyParadigmsFoundationsFinally
Concept Topic
Content
Information
c⃝B. Thalheim
Modellierung mit vielen Modellen
O
Φ1(O)
Φ2(O)
...
Φm(O)
*
j
^
Ψ1(G) --
Ψ2(G) --
... --
Ψm(G) --
G1 Φ1(G)-
G2 Φ2(G)-
... ...-
Gm Φm(G)-
Konstruktion
Kommunikation
Dokumentation
Analyse
Hinzukommende Forderung: Modellkoharenz
O Φ(O)-
Ψ1(G) -Ψ2(G) -*
... -j
Ψm(G) -^
G1 Φ1(G)-
G2 Φ2(G)-
... ...-
Gm Φm(G)-
Verstehen
Optimierung
Beherrschung
Simulation
BindungBedingungen
Vertrag
6 6 6
Koordination von Modellen nach Separation nach Aspekten
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Model suitePatternKnowledgeConceptsPhilosophyParadigmsFoundationsFinally
Concept Topic
Content
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c⃝B. Thalheim
Tool Support for Model Suites
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Model suitePatternKnowledgeConceptsPhilosophyParadigmsFoundationsFinally
Concept Topic
Content
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c⃝B. Thalheim
Model Suite Realisability?!!: Life Cases
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Semiotics
Model suitePatternKnowledgeConceptsPhilosophyParadigmsFoundationsFinally
Concept Topic
Content
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c⃝B. Thalheim
Model Suite Realisability?!!: Life Cases
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Semiotics
Model suitePatternKnowledgeConceptsPhilosophyParadigmsFoundationsFinally
Concept Topic
Content
Information
c⃝B. Thalheim
Model Suite Realisability?!!: Life Cases
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Semiotics
Model suitePatternKnowledgeConceptsPhilosophyParadigmsFoundationsFinally
Concept Topic
Content
Information
c⃝B. Thalheim
Choices for Specification: Model Pattern Depending on the style of development
Structure-oriented pattern such as• Compacting patterns• Typing patterns• Unfolding patterns• Union patterns
separationpattern
variationpattern
state transitionpattern
controlpattern
virtual machinepattern
conveniencepattern
IS rule pattern
Advantages
efficient development with controlled refinement, repeatability, robust-
ness, incrementality Disadvantages
restrictions in the development style, incrementality
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Semiotics
Model suitePatternKnowledgeConceptsPhilosophyParadigmsFoundationsFinally
Concept Topic
Content
Information
c⃝B. Thalheim
Responsibility Pattern
Timeframe
6
?
Responsibility
-requested by
responsible-
ResponsibilitytypeRegulations Party
type
6
Party(0,1)(0,n)
-requested by
responsible-
?
-for
Action
Knowledge/strategic level
Temporal/tactical level
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Semiotics
Model suitePatternKnowledgeConceptsPhilosophyParadigmsFoundationsFinally
Concept Topic
Content
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c⃝B. Thalheim
Evolution of Data
6
?
Observation⊕ -
R
-Measurement
*
R
Method ofmeasurement
Planning
State
Date
6
-Realstate
IDuration
State type
-Plannedstate
]
Planningof state
Comparisonoperator
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Semiotics
Model suitePatternKnowledgeConceptsPhilosophyParadigmsFoundationsFinally
Concept Topic
Content
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c⃝B. Thalheim
Planning and Variations
performingRelation to
timeInterested
party
Actionstate
6
?
-Action
LocationProposedaction
Implementedaction
]
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Semiotics
Model suitePatternKnowledgeConceptsPhilosophyParadigmsFoundationsFinally
Concept Topic
Content
Information
c⃝B. Thalheim
Knowledge in the ModelThomas Kuhn: Neue Uberlegungen zum Begriff des Paradigma. (1974)
Bernd Mahr: Das Wissen im Modell.
disziplinare Matrix (zur Scharfung des Paradigmen-Begriffes) mit min-
destens drei Eigenschaften:
• ‘symbolische Verallgemeinerungen’
• ‘Referenz-Modelle’
Kuhn (1978), S. 393: “Modelle liefern der Gruppe bevorzugte Analogien oder, wenn sie von großer
Uberzeugungskraft getragen sind, eine Ontologie. Am einen Extrem sind sie heuristischer Natur: Der
Stromkreis laßt sich mit Nutzen als stationares hydrodynamisches System begreifen, oder ein Gas als
Menge winziger Billardkugeln in regloser Bewegung. Am anderen Extrem sind sie Gegenstande meta-
physischer Festlegungen: Die Warme eines Korpers ist die kinetische Energie seiner Teilchen, oder, noch
deutlicher metaphysisch, alle wahrnehmbaren Erscheinungen gehen auf die Bewegung und Wechselwirkung
qualitativ neutraler Atome im leeren Raum zuruck.”
und
• ‘Musterbeispiele’.
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Model suitePatternKnowledgeConceptsPhilosophyParadigmsFoundationsFinally
Concept Topic
Content
Information
c⃝B. Thalheim
Knowledge Versus Content, Concepts,Topics
Content space
Concept space
Information space
Topic space
Knowledgespace
Topicsontology
Annotation, linkingculture, context
Semantic theoriesontology
Pragmaticsgeneral culture
Databasesutilisation
Media typesfunctionality, adaptation
User profilesuser portfolio
Memescultural units
The four dimensions of the knowledge space:
Data dimension through content,
foundation dimension through concepts,
language dimension through topics,
user dimension through information
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Verdichtung von Konzeptfeldern zumModell
Das Begriff des Modelles ist einer der grundlegenden Begriffe fast jeder Wissenschaftsdisziplin. Er
wird jedoch in verschiedener Art, mit unterschiedlichem Zweck, mit verschiedenen Begriffsgerust
und mit verschiedener Grundlage verwendet. Ein Modell ist ein System, das als Reprasentant
eines komplizierten Originals auf Grund mit diesem gemeinsamer, fur eine bestimmte Aufgabe
wesentlicher Eigenschaften von einem dritten System unter Nutzung einer Theorie und mittels
einer Sprache benutzt, ausgewahlt oder geschaffen wird, um letzterem die Erfassung oder Be-
herrschung des Originals zu ermoglichen oder zu erleichtern, beziehungsweise um es zu ersetzen.
Es gibt mathematische Modelle, Modelle der Logik, Modelle der Wissenschaftstheorie, Modelle
in den Sozialwissenschaften, Modelle von Dingen, padagogische Modelle, Modelle der Informatik
usw.
Was kann nicht durch ein Konzeptfeld ausgedruckt werden?
• intext√
• context /
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Information
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Concept Frames, Conceptual Spaces andRefinement
Concept frames
Symbolic space Geometrical space
Context space
Refinement level
Generic element G (Initial algebra)
I
Instance element I1
Instance element I2
I
Blended element B (Final element)
own live in
owner resident
house
Conceptual space
own ride
owner passenger
boat
Conceptual space
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Content
Information
c⃝B. Thalheim
Typischerweise sind Konzeptsystemegefaltet
see ISTA’09 bzw. auch weitere Arbeiten der Arbeitsgruppe Seiendes - Beschreibendes - Bewertendes - Benutzendes
Konzepta priori
Benutzungs-konzept
Konzepta
posteriori
Bewertungs-konzept
sowie auch Zachman-Dimensionen
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Typischerweise sind Konzeptsystemegeschichtet
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c⃝B. Thalheim
(C++) Modern Notion of the Concept of
Concept1. Typicality of the feature: (typical, moderately typical, atypical, borderli-
ne)• necessary feature• sufficient feature (in relation to other features), commonality of features• measures of typicality, weights• goodness of prototypes
2. Relevance of a feature
3. Importance of a feature:• recognised• used• frequency of occurrence, number of individuals, effect of ideals
Constructing expressions: features with certain ordering
• hierarchical structuring• containment relations (concept contains concept/knowledge): is a,
has a component, contains another concept
driver of car is a person, car contains engine, car is red
G.L. Murphy, The big book of concepts. MIT press
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(C++) Views the Concept of Concept
• Prototype view: representation through best example or examples
with some summary representation, partially with special hint on importance
of special properties
prototype and computational explosion
Schema: KL-ONE-artig (frame(slot,filler ∈ Dom(slot)))• Exemplar view: concept of a person = collection of things a person remem-
bers
Characterisation through similarity relation with measures, weights, stimu-li for their acceptancemultiplicative rules for typicality
see also Concept lattice (Ganter/Wille)
• Knowledge view: concepts are the basic items of knowledge
assuming that concepts are consistent
coexistence and coevolution of concepts and knowledge
concepts learning through knowledge and concepts obtained before
build a language, a theory, a reasoning system
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c⃝B. Thalheim
(C++) Separation of Concerns Based on theSemiotic Triangle of Content, Concepts
and Topics
Semantics Pragmatics
Syntax
Content
Computation
Concept
Validation
Topic
Presentation
Presentation theory
Computation theory
Model theory
Infon
Semanticalunit
Asset
interpretation
foundation
-presentation
explanation
K
contentdelivery
U
annotation
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c⃝B. Thalheim
(C++) Concepts with Definition Manyfold
Kindof Definition
DefinitionItem
Term Language
-DefinitionKind
Concept
(0,1)
(0,n)
Descriptor
6
DefinedThrough
ValidityRestriction
UsageTime
Preference
StructuralExpression
?
-SpecOrderedTree
-CommunityContext
UsageProfile
AcceptanceLevel
Community
-CharacterisedThrough
MetaInformation
SpecOrderedTree(StructuralExpression,TreeExpression(
(DefinitionItem, Modality(Sufficiency, Necessity), Fuzziness, Importance,Rigidity, Relevance, GraduationWithinExpression, Category)
))
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(C++) Example of a Mathematical Concept
Definition⟨Definition:⟩ A sequence of real (or complex) numbers is said to converge to a
real (or complex) number c if for every ϵ > 0 there is an integer nϵ > 0 such that
if j > nϵ then |aj − c| < ϵ. The number c is called the limit of the sequence and
we sometimes write aj −→ c.If a sequence does not converge, then we say that it diverges.
In other words, a sequence can be denoted by f(1), f(2), f(3), ..... Usually, we will denote such a
sequence by the symbol (aj)j , where aj = f(j).
⟨Sub-concept:⟩ Sequences that converge to zero are called null sequences.
⟨Annotation⟩: A sequence which converges.
⟨Prerequisite concept:⟩ A sequence of real numbers is a function f : N −→ R.⟨More specific prerequisites:⟩ monotone de/increasing sequences
For example, the sequence 1, 12 ,
12 ,
13 ,
14 ,
15 , ... is written as ( 1
n )n. Keep in mind that despite the strange
notation, a sequence can be thought of as an ordinary function. However, in many cases, that may not be the
most expedient way to look at the situation. It is often easier to simply look at a sequence as a ‘string’ of
numbers that may or may not exhibit a certain pattern.
⟨Context⟩: The limit of a sequence is one of the oldest concepts in mathematical analysis. It provides a rigorous
definition of the idea of a sequence converging towards a point called the limit.⟨Related concepts:⟩ A convergent sequence is bounded and the limit is unique. Cauchy sequences ....
Do not confuse this with the idea of a series defined by the result of summarising infinitely many numbers∑∞j=1 aj . Despite the fact that the common non-mathematical meaning of “sequence” and “series” is identical
there are separate definitions of convergence for sequences and series, and separate theories for these with some
important differences that you need to be aware of. An infinite series is an expression of the form∑n
j=1 aj ,
where (an) is a sequence.
limit inferior, limit superior
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c⃝B. Thalheim
(C++) Concept-Content-Topic Space
Kindof Definition
DefinitionItem
Term Language
-DefinitionKind
Concept
(0,1)
(0,n)
Descriptor
6
DefinedThrough
ValidityRestriction
UsageTime
PreferenceStructuralExpression
?
-SpecOrderedTree
-CommunityContext
UsageProfile
AcceptanceLevel
Community
-CharacterisedThrough
MetaInformation
6
?
-ApplicationContext
KindOfAssociation
ApplicationSchema
6
?
-SchemaContext Culture
Application
6
?
Extension/Foundation
Typicality
Content -AnnotatedThrough Topic
?
Explains/Depicted
*
In
TopicSpace/MapCommunity
6
SharedWithin
j
Ontology Language
6
?
UsedIn
AssociatedTo
AssociationType
R
UsedFor
ApplicationArea
-GivenByMediaObjectSuite
?
-SpecifiedBy
MediaType
?
SpecifiedOnTop
1* Database
SchemaYAuxiliarySchema
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c⃝B. Thalheim
Models Defined Through Concepts
Arts: working of plastic materials by hand to build up form
Mathematics: method of scientific investigation of systems
no necessity to construct an actual physical model of the system
mathematical model: a description of the system in some algorithmic language
divided into individual parts and the state of each part described by some
system of parameters
description of the relationships between the separate parts
Cybernetic systems: self-improvement, self-teaching and self-development
to model
• to plan or form after a pattern or shape
• to make into an organization (as an army, government, or parish)
• to produce a representation or simulation to model a problem
• to construct or fashion in imitation of a particular model
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c⃝B. Thalheim
(C6) Specification of Concepts throughConcept Fields
Concept field
context [ ]
semantics
syntax
pragmatics
representation form
parameter*
icon, symbolconditions
rules
environmentassociatedconcepts
history
semanticalcases
applicationportfolio semantics
extension
constraints
optionality
null
default
valency
binding form
kernelsemantics
model world
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c⃝B. Thalheim
(C6) Concepts Maps: Person and Address
Person(( characteristics, relation, context),
(ΣDeontTempPL/1Person , MPerson, Σ
EpistemLogikPerson ),
( enterpriseIS, taskActor))
τ( relation) = employeed·∪ partner
τ( characteristics) = name·∪ birthData
·∪ identData
·∪ gender
·∪ family
·∪ furtherCharact
·∪ profile
Examples of ΣDeontTempPL/1Person :
F(update(Person. birthData))
α“divorced′′( person) → ∃past y
(Association(Is.Partner. y,Of.Partner. person,Since,Till)
∧ from < todayAddress(( geographAddr, contactAddr, history),
(ΣPL/1Address, MAddress, Σ
QualityAddress),
( enterpriseIS, taskActor))
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(C6) Example of a Concept Diagram:Person
kinddescription
property
[priority]
status
description
6
6
relation
is of
6
partner
sinceuntil
[comment]
PersonfirstNames title calledAsbirthData biometryDatagender family history
familyName calledAs
[passport] characteristics
?
-employee
intervalcharacteristics
abilitiesprofile
-profile
yearsInProfession
+
6
?educationprofile
CV
lastEntry
educationalinstitution
6
-certificateby
date
specialisation
topic
place
name
+
I
organi-sation
name
?
external internal
roletype
description
[comment]
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(C6) Example: Concept of a Conference mapping daily life
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c⃝B. Thalheim
Concept Board as a Special Map: PanelExample
The panel panellist: provide & discuss
an interesting topic
panellist: talk about some-
thing and anything
participant: activelyparticipate, argument,adverse & contradict
(R,R)(win,win)(e.g. (3,3))
(S,T)(lose much, win much)
(e.g., (0,5))
participant: listen only,read emails,
prepare another paper, ...
(T,S)(win much, lose much)
(e.g. (5,0))
(P,P)(lose, lose)(e.g., (1,1))
non-participant: don’tattend, have some fun (?,?) (?,?)
T: Temptation to defect, R: Reward for mutual cooperation,
P: Punishment for mutual defection, S: Sucker’s payoff
T > R > P > S or better 2R > T +S otherwise better to be in the (T,S) or (S,T) mode
equilibrium: T + S = P +R
Panel options:
• talk, talk, talk, ..., some more questions?
• Octavian circle: live discussion with harsh punishment not to be in the cloud
• (talk; challenge)∗ as a small repetitive story patternwith incremental and creative co-evolution
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Concept ER Schema as a Special Map:Panel Example
Panel
6
?
uses
Story -within Scene
?
-acts
Θ
6
Θ := uses.Story 1within.Story
-Participant Attendee
Contributionplay
role
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c⃝B. Thalheim
General Concept ER Schema asSpecialisation
Panel
6
?
uses
Story -within Scene
?
-acts
Θ
6
Θ := uses.Story 1within.Story
-Participant Attendee
Contributionplay
role
Panel
6
?
uses
Octaviancircle
-within(1,1)
Scene
?
-acts
-OctavianParticipant Attendee
Contributionplay
role
Panel
6
?
uses
Fullydouble-active
panel
-within⊕
Oppositionscene
Requestedscene
6
?
-acts
-Activeparticipant Attendee
Contributionplay
role
Panel
6
?
uses
Classicalsemi-activepanel story
6
?
-within
Contribution
⊕ Questionscene
Attendee-‘Normal’Participant
Statement
scene
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Concept Topic
Content
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c⃝B. Thalheim
(C1-6) Deficiencies of the ClassicalConcept of Concept
Classical concept understanding
(1) concepts are mentally represented through definitions
(2) objects either belong or do not belong to an extension of a concept
(3) there is no internal distinction between members of an occurrence of an
extension of the concept
Pitfalls
importance of things for conceptsimportance, necessity, sufficiency of of characteristicsfuzzy concepts beyond exact conceptsgraduation of properties of conceptsconcepts are culturally and group dependentthere are typical and atypical objects for a conceptconcepts are time, space, culture dependent
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c⃝B. Thalheim
Beschreibung von Konzepten durchKonzeptfelder
Konzeptfeld
Kontext[ ]
Semantik
Syntax
Pragmatik
Wortformen
Parameter*
Wort
BedingungenRegeln
UmfeldAssoziierteKonzepte
Historie
SemantischeFalle
Anwendungs-portfolio Erweiterungs-
semantik
Constraints
Optionalitat
Null
Default
Valenz
Bindungsform
Kern-semantik
Modellwelt
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Konzepte und Wissenschaftstheorie Wissenschaftstheorie nach Wolfgang Deppert
• wie wir Begriffe gebrauchen
• wozu sie zu gebrauchen sind, d. h.,
• wie mit ihnen hantiert wird und
• wie sie funktionieren
• verschiedenen Arten des Gebrauchs und der Funktion von Begriffen
# Benennung eines spezifischen Gebrauchs oder einer Funktion wird
im folgenden summarisch durch die Angabe von Merkmalen oder auch
von Kennzeichen
Verhaltnis der Begriffe untereinander
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Umgang mit Begriffen Wissenschaftstheorie nach Wolfgang Deppert
(1) Zusammenhangsformen der Begriffe: Begriffe stehen untereinander in gewissen Verhaltnissen
oder in bestimmten Beziehungen, die gesondert von den Funktionen der Begriffe behandelt
(2) Wirkung als die Funktion der Begriffe: Umgang mit Begriffen bewirkt etwas
# Begriffe sind diejenigen sprachlichen Elemente, mit denen wir je nach Hinsicht etwas
Allgemeines oder etwas Einzelnes beschreiben.
# relationale oder der rein begriffliche Bezug der Begriffe von existentiellem zu unterscheiden
# ellt in der Rege definitorische Zusammenhang zwischen Begriffen als eine einseitige
Abhangigkeitsbeziehung
(Das Definierte (das Definiendum) wird von den definierenden Bestandteilen (dem Definiens) abhangig gemacht wird und nicht
umgekehrt. Wenn eine Klasse von Begriffen auf diese Weise verbunden wird, dann sei dieses definitorische System von Begriffen
ein definitorisch-hierarchisches Begriffssystem genannt. Man kann auch von Begriffspyramiden sprechen, deren Begriffe definito-
risch auf undefinierte Grundbegriffe zuruckgefuhrt werden. Stellen wir hingegen begriffliche Beziehungen in Form wechselseitiger
Bedeutungsabhangigkeiten von Begriffen untereinander fest, so sei von ganzheitlichen Begriffssystemen.
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c⃝B. Thalheim
Gebrauch von Begriffen Wissenschaftstheorie nach Wolfgang Deppert
(1) Erstes Kennzeichen von Begriffen: Begriffe sind solche sprachlichen Bedeu-
tungstrager, die je nach Hinsicht entweder etwas Allgemeines oder etwas Ein-
zelnes bedeuten.
• Funktion der Begriffe: mit ihnen in bestimmten Existenzbereichen Klassen
oder Mengen von Gegenstanden, bilden und bezeichnen klassenbildend zu-
sammenfassen mit Begriffen Gegenstande
# extensionale Verstandnis der Begriffe
(2) ...
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Gebrauch von Begriffen Wissenschaftstheorie nach Wolfgang Deppert
(1) ...
(2) Zweites Kennzeichen von Begriffen: Existenzbereiche werden durch die Anwen-
dung von Begriffen strukturiert und unterschieden.
• strukturierendes Merkmal der Begriffe
• verbundene Funktion der Begriffe laßt sich nur erfullen, wenn die Begriffe
schon eine Bedeutung haben, bevor sie zur Klassenbildung benutzt werden
• Intension eines Begriffs: bestimmt die Absicht, den Sinn oder die Bedeutung
• Rudolf Carnap: Theorie der drei Begriffe aufzustellen ((1) klassifikatorische
oder qualitative, (2) komparative und (3) metrische oder quantitative Be-
griffe)
(3) ...
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c⃝B. Thalheim
Gebrauch von Begriffen Wissenschaftstheorie nach Wolfgang Deppert
(1) ...
(2) Drittes Kennzeichen von Begriffen: existentieller Bezug der Begriffe
Anwendungsfunktion der Begriffe bezieht Begriffe auf einen bestimmten Exi-
stenzbereich
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Existentieller, begrifflicher undanwendender Bezug Wissenschaftstheorie nach Wolfgang Deppert
• existentielle Bezug der Begriffe
• Funktion der Begriffe besteht in ihrer strukturgebenden Anwendung auf be-
stimmte Existenzbereiche.
• relationale oder der rein begriffliche Bezug der Begriffe
Beziehungen der Begriffe untereinander
• Definitorischer Zusammenhang zwischen Begriffen stellt in der Regel eine ein-
seitige Abhangigkeitsbeziehung.
definitorisch-hierarchisches Begriffssystem
Begriffspyramiden: Begriffe definitorisch auf undefinierte Grundbegriffe zuruckgefuhrt
ganzheitlichen Begriffssystemen: begriffliche Beziehungen in Form wechselseitiger Bedeu-
tungsabhangigkeiten von Begriffen untereinander
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Systembildendes Merkmal der Begriffe Wissenschaftstheorie nach Wolfgang Deppert
Drittes Kennzeichen von Begriffen:
Begriffe sind solche Bedeutungstrager, die untereinander in einseitiger oder in wech-
selseitiger Bedeutungsabhangigkeit stehen konnen.
Verwendung hierarchischer Begriffsysteme
Durch die wechselseitige oder gegenseitige Bedeutungsabhangigkeit kann es aber
ganzheitliche Begriffsysteme geben.
Begriffe sind sprachliche Bedeutungstrager, die das zweiseitige, das strukturie-rende und das systembildende Merkmal besitzen.stets an eine sprachliche Reprasentation gebunden
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Unterscheidung von existentiellem,begrifflichem und anwendendem oder
ergastischem Denken Wissenschaftstheorie nach Wolfgang Deppertdrei verschiedene Arten des Denkens
(1) Das Denken uber etwas in einer bestimmten Existenzform Gegebenes. Dieses Denken mag
das existentielle Denken genannt werden.20
(2) Das Denken in den rein begrifflichen Bezugen der Begriffe, indem Begriffe konstruiert oder
etwa in Definitionen miteinander kombiniert werden. Dieses Denken heißt das begriffliche
Denken.
(3) Das Denken, durch das begriffliche mit existentiellen Vorstellungen verbunden werden. Dies
kann dadurch geschehen, daß Existenzbereiche durch Begriffe bestimmt werden oder daß
Begriffe auf Inhalte dieser Existenzbereiche angewendet werden. Dieses verbindende Denken
mag als das anwendende oder auch als das ergastische Denken bezeichnet werden. Es ord-
net den vom existentiellen Denken gedachten Existenzbereichen Strukturen des begrifflichen
Denkens zu.
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Funktion 1: Begriffe als Werkzeuge desBeschreibens Wissenschaftstheorie nach Wolfgang Deppert
(1) was wir,
(2) womit wir und
(3) wie wir, d. h., mit welchen Werkzeugen wir etwas beschreiben(a) Das Denken uber das Existierende, uber das, was es gibt, was vorhanden ist und was wir beschreiben
wollen.
(b) Das Denken uber die Mittel und Moglichkeiten, etwas Existierendes zu beschreiben.
(4) weitere Dimensionen: wofur, wozu, weshalb, wann, wer, fur wen, ...
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Semiotics
Model suitePatternKnowledgeConceptsPhilosophyParadigmsFoundationsFinally
Concept Topic
Content
Information
c⃝B. Thalheim
Konsequenzen aus den definiertenMerkmalen der Begriffe Wissenschaftstheorie nach Wolfgang Deppert
(1) Innen- und Außenbetrachtungen (intext, context) und ganzheitliche und hier-
archische Begriffssysteme:
einschließlich: unbestimmte Innen- und Außenbetrachtungen
(2) Begrundungsendpunkte
ordnungstiftende Funktion fur die Existenzbereiche
Grenzbegriffe sind keine Begriffe
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Begriffliche Gliederungen in derAnwendung von Begriffen zum
Erkenntnisgewinn uber einen bestimmtenObjektbereich Wissenschaftstheorie nach Wolfgang Deppert
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Nutzung bei expliziter Berucksichtigungder Analogie der Modelle
Grad der strukturellen Analogie: Wie weitgehend wird Struktur abge-
bildet?
Grad der qualitativen Analogie: Wie weitgehend wird Beschaffenheit
abgebildet?
Grad der strukturellen Angleichung: Wie weitgehend wird Struktur
abgebildet unabhangig von Realisierung?
Grad der qualitativen Angleichung: Was wird codiert, was dagegen
nicht?
Grad der funktionalen Angleichung: Wie weitgehend werden Funk-
tioen abgebildet?
Grad der Kontrastierung: Was wird besonders betont?
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Modellierung bei Beachtung derBeschrankungen Beispiel der Mathematik
• Orientierung auf Gesetze mit Beschrankung durch Striktheit
• Typisierung ohne einzelne Individuen
• Orientierung auf Funktionsaspekt (auch Zeit funktional; Raum?)
• Abstraktion von Qualitat
• striktes Auswerungssystem (z.B. durch Einfuhrung von“wahr”)
• Adaquatheit von Modellen (Zweck, Absichten, Nutzen, ethische Fragen)
• Abstraktion von Wesens-, Sinn- und Zielfragen
Damit auch Verstehen der Grenzen der Modellierung:
• relative Grenzen: veranderlich im Kontext (Zeit, Raum, Einsatz, ..; Sprache,
Befahigung des Autors)
• absolute und unveranderliche Grenzen Damit: Erfassung der Kapazitat eines Modelles.
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Programm zur Grundlegung
Auswahl einer Logik zur Unterstutzung von Schlußweisen
Kontextfreiheit und Unabhangigkeit je nach Grundalphabet
(mit kanonischer Interpretation) und Signatur S = (P ,F , C)fur Aritatsfunktion ϵ mit freier Interpretation unter S-StrukturenA = (A,RA,FA, constA)
Definitorische Erweiterung als syntaktischer Zucker
Einschrankung auf objekt-relationale Strukturen ohne Ver-
lust der Ausdrucksstarke
Elementare Aquivalenz statt Isomorphie aufgrund der End-
lichkeitsforderung
Auszeichnung der Pradikatenlogik durch die Satze von Lind-
strom
Kontentfelder als parametrische Spezifikationen
Abbildung auf Contentwelt
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Auswahl einer Logik zur Unterstutzung von Schlußweisen
Probleme bei Integration von Konzepten mit PL/1• oft
”zu viele “ Informationen zu bestimmten Sachverhalten
• klassische Inkonsistenzen• trivialisierte Logik• nutzlose Informationen
Quasi-klassische Logik als Alternative je nach Version der Logik• ausgerichtet auf Schlußfolgerungen im Kontext von Inkonsistenzen• basiert auf Schlußfolgerungen von Formeln in CNF• erhalt naturliche Deduktionsregeln• abgeschwacht in der Verwendung der Regeln• (→ Parakonsistente Logik)Sei S eine Menge. Sei O eine Menge von Objekten definiert wie folgt, wobei
+α ein positives und −α ein negatives Objekt ist:
O = +α|α ∈ S ∪ −α|α ∈ SDann nennen wir jedes X ∈ ρ(O) ein Modell.
+α ∈ X: α erfullbar unter X −α ∈ X: ¬α erfullbar unter X
+α ∈ X: α nicht erfullbar unter X −α ∈ X: ¬α nicht erfullbar unter X
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The Notion of the Theory Different flavours of the notion of a theory
A theory is any systematic and coherent collection of ideas that relateto a specific subject.
• a coherent group of general propositions used as principles of explanation for
a class of phenomena;
• a proposed explanation whose status is still conjectural, in contrast to well-
established propositions that are regarded as reporting matters of actual fact;
• Mathematics: a body of principles, theorems, or the like, belonging to one
subject;
• the branch of a science or art that deals with its principles or methods, as
distinguished from its practice;
• a particular conception or view of something to be done or of the method of
doing it; a system of rules or principles;
• contemplation or speculation.
• guess or conjecture.
Dictionary.com/
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The Notion of the Theory Different flavours of the notion of a theoryA theory is any systematic and coherent collection of ideas that relate to a specific
subject.
• Thesaurus @ wordnet.com/:
(1) a well-substantiated explanation of some aspect of the natural world; an organized
system of accepted knowledge that applies in a variety of circumstances to explain
a specific set of phenomena;
(2) a tentative insight into the natural world; a concept that is not yet verified but
that if true would explain certain facts or phenomena;
(3) a belief that can guide behavior.
• Collins English Dictionary:
(1) a system of rules, procedures, and assumptions used to produce a result
(2) abstract knowledge or reasoning
(3) a speculative or conjectural view or idea
(4) an ideal or hypothetical situation (esp in the phrase in theory)
(5) a set of hypotheses related by logical or mathematical arguments to explain and
predict a wide variety of connected phenomena in general terms the theory of
relativity
(6) a nontechnical name for hypothesis.
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The Notion of the Theory• a well-substantiated explanation of some aspect of the natural world; an organized system of accepted knowledge that
applies in a variety of ...• hypothesis: a tentative insight into the natural world; a concept that is not yet verified but that if true would explain certain
facts or phenomena;• a belief that can guide behavior;
wordnetweb.princeton.edu
• A hypothesis that has withstood extensive testing by a variety of methods, and in which a higher degree of certainty may
be placed. A theory is NEVER a fact, but instead is an attempt to explain one or more facts.blue.utb.edu/biology/
• a plausible or scientifically acceptable general principle or body of principles offered to explain phenomena. In other words,
it is the general or abstract principles of a body of fact, a science.www.bioethismscience.org/
• The body of rules, ideas, principles, and techniques that applies to a particular subject.www.edgateway.net/
• is an explanation of some phenomenon.roanoke.edu/
• several related propositions that explain some domain of inquiry.oregonstate.edu/
• An explanation of a natural occurrence that is testable and capable of predicting future occurrences.spaceplace.nasa.gov/
• A set of propositions, assertions, and accepted facts that attempts to provide a plausible or rational explanation of cause-
and-effect (causal) relationships among a group of observed phenomenon.BusinessDictionary.com/
• In mathematical logic, a theory is a set of sentences in a formal language.
en.wikipedia.org/wiki/Theory (mathematical logic)
• A theory, in the general sense of the word, is an analytic structure designed to explain a set of observations.en.wikipedia.org/wiki/Theory
• An unproven conjecture; An expectation of what should happen, barring unforeseen circumstances; (sciences) A coherent
statement or set of statements that attempts to explain observed phenomena; (sciences) A logical structure that enables
one to deduce the possible results of every experiment;en.wiktionary.org/wiki/theory
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Definition of a Concept Gathering Systems
C: set of concepts and available experience
D: set of domain knowledge
M: set of representable meta knowledge
G: set of learning goals
H: set of representable hypotheses
R = C ∪ D ∪M∪ G ∪H: set of representable knowledge and concepts
Concept generator: γ : C ×R → CLearning function λ : C ×R → HEvaluator: ν : C ×R → Q
Q - set of quality characteristics
Learning system (γ, λ, ν, C,R)
Concept sequence: C1, C2, ..., Cf with Ci ∈ CLearning sequence: R0, R1, R2, ..., Rf with Ri ∈ RR0 - initial knowledge ... Rf final knowledge
Background knowledge of the learner: B ⊆ D ∪M∪ Gactual available knowledge B ∪H′
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Auswahl einer Logik Wissensinseln
Beschriftete quasi-klassische Logik
• erlaubt eindeutige Identifikation jeder Information
• Beschriftungen von Folgerungen ergeben sich aus Vereinigung der Beschriftun-
gen der Voraussetzungen
• Auswirkungen jeder Information ablesbar
• keine Auswirkungen auf Semantik oder Beweistheorie der Logik
Sei S eine Menge von atomaren Symbolen, etwa ein Alphabet. Wenn i ⊆ S und α
eine Formel, dann ist i : α eine beschriftete Formel.
Beweistheorie als Gentzen-Kalkul fur beschriftete Formeln
CON(∆) = Γ ⊆ ∆|Γ ⊢Q i : ⊥
INC(∆) = Γ ⊆ ∆|Γ ⊢Q i : ⊥
MI(∆) = Γ|Γ ∈ INC(∆) und ∀Φ ∈ INC(∆) Φ ⊂ Γ
MC(∆) = Γ|Γ ∈ CON(∆) und ∀Φ ∈ CON(∆) Γ ⊂ Φ
FREE(∆) =∩
MC(∆) (= ∆−∪
MI(∆) )
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Kontextfreiheit und Unabhangigkeit Koinzidenz und Substituierbarkeit
Interpretation I: Abbildung auf S-StrukturenVariablenbelegung η : V ar → A
volle semantische Struktur A∗ = (A, (I, η))Modellbeziehung ModS(A∗, ϕ)
Kontextfreiheit (Koinzidenzlemma): Gegeben seien eine For-
mel ϕ von S, Signaturen S1 ⊇ S, S2 ⊇ S, StrukturenA1,A2, Inter-
pretationen I1, I2 mit I1|S = I2|S und Variablenbelegungen η1, η2mit η1|S = η2|S . Dann gilt ModS1(A∗
1, ϕ) gdw. ModS2(A∗2, ϕ)
Unabhangigkeit (Substitutionslemma): Es seien ϕ von S,A∗ eine S-Struktur, x1, ...., xn paarweise verschiedene Varia-
ble und t1, ...., tn beliebige S-Terme, deren Variable paar-
weise disjunkt sind und nicht in ϕ vorkommen. Dann
gilt V alA∗(Subst(t, (x1, t1), ..., (xn, tn))) = V alA∗(t), sowie
V alA∗(Subst(ϕ, (x1, t1), ..., (xn, tn))) = W gdw. V alA∗(ϕ) = W .
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Definitorische Erweiterung als Verallgemeinerung der Definitionstheorie der PL
S-Definition ∀v0...∀vn−1(P (v0, ..., vn−1) ↔ ϕ(v0, ..., vn−1)) mit Definiens ϕund Definiendum P (v0, ..., vn−1) fur neue Pradikatenvariable
S-Definition ∀v0...∀vn−1(f(v0, ..., vn−1) = vn ↔ ϕ(v0, ..., vn−1, vn))mit Definiensϕ und Definiendum f(v0, ..., vn−1) fur neue Funktionenvariablefalls Σ ⊢ ∀v0...∀vn−1∃!ϕ(v0, ..., vn−1)
S-Definition ∀v0(c = v0 ↔ ϕ(v0)) mit Definiens ϕ und Definiendum cfur neue Funktionenvariable falls Σ ⊢ ∃!ϕ(v0)
Es seien S1 und S Signaturen mit S1 ⊇ S, Σ eine Menge von S-Formeln und ∆
eine S-Definitionsmenge von S1 \ S bezuglich Σ.
Eindeutige Existenz von Definitionserweiterungen: Dann existiert fur
jede (volle) Struktur A, die S-Modell von Σ ist, genau eine (volle) S1-Struktur
A′, die S ′-Modell von ∆ ist und deren S-Redukt gerade A ist.
Eliminierbarkeits- und Nichtkreativitatstheorem: Dann existiert eine
Abbildung ∇, die fur beliebiges n ∈ ω einem ϕ ∈ LS′
n eine Formel ϕ∇ ∈LSn zuordnet, so daß fur alle vollen S-Strukturen A mit ModS(A,Σ) gilt
ModS′(A∆, ϕ) gdw. ModS(A, ϕ∇) und Σ ∪∆ ⊢ ϕ ↔ ϕ∇.
Galoiskorrespondenz zwischen Formelmengen und S-Strukturen
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Einschrankung auf objekt-relationaleStrukturen ohne Verlust der Ausdrucksstarke
A = (A, I) und A1 = (A, I1) fur Signaturen S1 und S mit S1 ⊆ SRedukt A1 von A: I1 = I|S1 (eindeutig bestimmt)
Expansion A von A1
Relativierungstheorem: Partialitat kann durch einstelliges Pradikatensymbol
definiert werden.
Relationalisierungstheorem: S sei eine Signatur und A eine voll S-Struktur.Sr sei eine der Menge S mittels einer geeigneten Relationalisierungsfunkti-
on zugeordnete relationale Signatur. Ar sei die A entsprechende relationale
Sr-Struktur. Dann gibt es zu jeder S-Formel ϕ, die hochstens n freie Varia-
ble v0, ..., vn−1 enthalt, eine Sr-Formel ϕr, die ebenfalls hochstens dies freien
Variablen enthalt, so daß fur jede volle S-Struktur A gilt ModS(A, ϕ) ↔ModSr (Ar, ϕr).
Damit Reduktion auf objekt-relationale Strukturen mit Elimination der Funktions-
variablen und insbesondere der Aggregationsfunktionen (z.B. fur abgeleitete Eigen-
schaften).
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Elementare Aquivalenz und IsomorphieIsomorphie A ≃ B zwischen S-Strukturen durch Bijektion der Tragermengen
Elementar aquivalente Strukturen A ≡ B falls fur alle FormelnModS(A, ϕ) ↔ ModS(B, ϕ) Aus Isomorphie folgt Aquivalenz
Semantische Theorie SemTh(A) = ϕ ∈ LS0 |ModS(A, ϕ)
Prapartieller Isomorphismus fur Teilmengen der Tragermengen
Isomorphielemma: Isomorphe Strukturen lassen sich durch Formeln der ersten
Stufe nicht unterscheiden.
Die Klasse B|B ≃ A ist fur unendliche Strukturen A so nicht darstellbar.
Semantische Theorien A ≡ B ↔ Mod(A, SemTh(B)) ↔Mod(B, SemTh(A))
Die Klasse B|B ≡ A ist darstellbar als Klasse aller Strukturen C mit
Mod(C,Σ) fur geeignetes Σ.Endlich isomorphe Strukturen sind isomorph, falls sie endlich sind.
Satz von Fraisse: S sein eine endliche Signatur und A und B seien S-Strukturen. Dann gilt A ≡ B gdw. A ≃e B.Ist S relational. Dann gibt es fur jedes n ∈ ω bis auf logische Aquivalenz
nur endlich viele Formeln, die hochstens v0, ...., vn−1 frei enthalten und deren
Quantorenrang ≤ n ist.
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Der Verband der Theorien innerhalb einer Sprache
TL(S) Menge aller Theorien Σ+ innerhalb einer Sprache L(S)T⊆L(S) Menge aller Theorien mit Sprache, die Teilsprache von L(S) ist
Halbordnung (T⊂L(S),⊆) ist ein vollstandiger Verband
Durchschnitt beliebig vieler Theorien ist wieder Theorie
T1 ∪ T2 ist Theorie gdw. T1 ⊆ T2 oder T1 ⊆ T2Halbordnung (TL(S),⊆) ist ein vollstandiger Verband
Distributivitat T1 ∩ (T2 d T3) = (T1 ∩ T2) d (T1 ∩ T3) auch fur Indexmengen
duales Distributivitatsgesetz gilt nicht i.a. sondern nur fur endliche Erzeugen-
denmengen Σ
kein komplimentarer Verband
Existenz einer Axiomatisierung gdw. Folgerungsoperator ist kompakt, refle-
xiv, monoton und abgeschlossen (4. Hauptsatz der Logik)
Sind zwei Folgerungsoperatoren , jeweils reflexiv, monoton, abgeschlossen,
kompakt, abgeschlossen bzgl. Generalisierung und invariant bzgl. Inferenzei-
genschaft (MP), und Deduktionseigenschaft (Aus α ∈ (Σ ∪ β) fur ge-
schlossenes β folgt (β → α) ∈ (Σ)) und es gilt ∅ = ∅ ,dann gilt Σ = Σ fur alle Σ (2. Hauptsatz der Logik)
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Knowledge = Content Chunks +Representation + Validation Different truth definitions
Material, logical, and normative connectives , e.g. implication
• ψ → ϕ means ϕ necessarily if ψ (strict, logical)
• ψ ⇒ ϕ means ‘It is the case that if ψ (can be
observed) then also ϕ.’ (material)
• ψ ⊃ ϕ means ‘In situations for which the-
re exists a dependence then ϕ follows from ψ
(norms) (counter-example-based)
ψ ϕ ψ → ϕ,
ψ ⇒ ϕ
ψ ⊃ ϕ
1 1 1 1
1 0 0 0
0 1 1 ??
0 0 1 ??
Generalisation operators e.g. (t,f)-quantifier Qt,f with validity
dependence of Qr,sα(x) in structure A such that
|o ∈ πx(dom(A)) | Iox(α) = 1| = t and
|o ∈ πx(dom(A)) | Iox(α) = 0| = f
classical ∀ ≡ Q∗,0, ∃ ≡ Qt,∗ for t ≥ 1, Majority ≡ Qn+k,n, k, n ∈ N+, k ≥ 1
Models for the knowledge operator KA for actors A
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Knowledge and Content Chunks
Content chunks supporting knowledge by associating know-
ledge items with chunks as samples, with hocks for memes, with
varying abstraction and condensation
Meme-based knowledge maps supporting users who already lear-
ned the specific basic knowledge items of the domain under concern
Dynamic extension of knowledge and content chunks
depending on renewal portfolio, priority of changes, and notificati-
on
Trackable content chunks with annotation (topics), explanation
(concepts, mini-concepts), with decomposition for reading, with
adaptation to users and their environments, with recharge
Supported functionality e.g. navigation, variety of links, search
and intelligent retrieval, intelligent extraction facilities, intelligent
narrowing, survey and summarisation, etc.
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Knowledge and Content Chunks
Content chunks supporting knowledge by associating know-
ledge items with chunks as samples, with hocks for memes, with
varying abstraction and condensation
Meme-based knowledge maps supporting users who already lear-
ned the specific basic knowledge items of the domain under concern
Dynamic extension of knowledge and content chunks
depending on renewal portfolio, priority of changes, and notificati-
on
Trackable content chunks with annotation (topics), explanation
(concepts, mini-concepts), with decomposition for reading, with
adaptation to users and their environments, with recharge
Supported functionality e.g. navigation, variety of links, search
and intelligent retrieval, intelligent extraction facilities, intelligent
narrowing, survey and summarisation, etc.
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Knowledge ChunkKnowledge pieces cannot be considered in an isolated form. They are composed.
Knowledge chunk C: a suite of knowledge pieces consisting of content,
concepts, topics and information.
Content chunk D = D1, ...,Dntypically given as a set of media objects
Concept chunk C = C1, ...,Cktypically given as a small ‘theory’
Topic chunk T = T1, ...,Tmtypically given as a map of associated topics
Associated by generalised mappings
interpretation: D → C (opposite to foundation)
explanation: T → C (opposite to presentation)
annotation: D → C (opposite to content delivery)
Information chunk of a user
for a given universe of contexts IA of an agent A
with corresponding associations (partial)
to content DA, concepts CA, topics TA of the user
May also be extended by the agent, ... context.
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Relations among Knowledge Chunks:Redundancy, Paraphrase, Entailment,
Contradiction, ...Redundancy based on data redundancy, derivation redundancy, and representation redundan-
cy. Allows lifting relations and content-based publish/subscribe is a communication abstrac-
tion.
Paraphrase through recomposition of a phrase, expression and embedding into a different
environment
Entailment based on the minimal inferential capacity: If A believes that Σ, and α is an obvious
consequence of Σ, then A believes that α.
Other properties known for epistemic reasoning such as consistency, conjunctive composition
and division.
Entailment is different from material implication and relates knowledge chunks by forward
propagation of truth and backward propagation of falsity.
Specific form: subsumption z |= X ⊔ Y (description logics) for terminology z. It forms
IsA-hierarchies.
Contradiction awareness
Compatibility based on entailment and consistency. Compatibility relation is used for entail-
ment and satisfiability.
Generalisations of linguistic relationships: Synonym, meronymy, homonymy, perspec-
tive and complementary antonyms, entailment(proper inclusion, backward presupposition,
cause, troponymy).
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Challenges
• Knowledge adaptation to user profiles, portfolio, to knowledge
demand
• Knowledge gathering on demand if not available
• Knowledge harmonisation for integrated delivery of
• Knowledge framing for economic delivery
• Knowledge can be based on content and concepts ???
• Meta-description of knowledge for delivery, gathering, stora-
ge
• Distributed knowledge of communities
• User characterisation for profile adaptation
• Life case capturing for portfolio adaptation
• Separation of concern for knowledge web systems
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The Big Achievements and Failures ofKnowledge Systems
• Explicit consideration of knowledge as an asset for our society.
Knowledge Management (KM) comprises a range of practices used in an organi-
sation to identify, create, represent, distribute and enable adoption of insights and
experiences. Such insights and experiences comprise knowledge, either embodied in
individuals or embedded in organisational processes or practice.
• Development of knowledge management systems mainly conside-
ring formula management, maintenance, playout, playin
It was and is a hype however.
• T.D. Wilson, 2002: Knowledge management (whatever it is) alsoshows signs of being offered as a Utopian ideal and the results arelikely to be similar.Happily, it is quite easy to distinguish between ’knowledge’ and ’information’ in such
a way as to remove ambiguity and, at the same time, demonstrate the fundamental
nonsense of ’knowledge management’.
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Knowledge Management CyclesInformation management cycle as the basis for knowledge ma-
nagementAcquire and capture e.g., get, create, import, observe, generalise, extract, abstract,
identify, create relations, obtain pattern, develop associations, verify
Refine by analysing, interpreting, reporting, editing, combining, decomposing and compo-
sing, indexing, linking, ...
Store knowledge chunks with metadata, build and sustain, forget and divest, establish,
update
Distribute and disseminate
Present for using
User learning cycle considering the knowledge of the userGet: seeking out information, e.g. tacit and explicit, being selective when faced with infor-
mation overload
Use: combine content in new and interesting ways to foster innovation in the organization
Learn: learning from experiences
Contribute: motivate employees to post what they have learned to a knowledge base,
e.g., link individual learning and knowledge to organizational memory
Assess: evaluation of intellectual capital, e.g., identify assets, metrics to assess them and
link these directly to business objectives
Build and Sustain: allocate resources to maintain knowledge base, e.g., contribute to
viability, competitiveness
Divest: should not keep assets that are no longer of any business value, e.g., transfer
outside the organization, apply and patent and spin off companies etc.
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Knowledge Codification and Modelling
• Represent knowledge in our minds by building mental models
• Model knowledge by assembling declarations and relational state-
ments into a coherent whole
• Document knowledge in books and manuals
• Encode knowledge into knowledge bases
• Organize new knowledge for specific uses, e.g., sequence for
diagnostics, help desk, FAQs
• Organize new knowledge according to an established framework
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Knowledge Sharing
• Based on sources: Remember knowledge and internalize it; cumulate know-
ledge in repositories (encode it); embed knowledge in repositories (within pro-
cedures); archive knowledge
• Pooling knowledge: Coordinating knowledge of collaborative teams; crea-
ting expert networks to identify who knows what; assembling knowledge –back-
ground references from libraries and other knowledge sources; accessing and
retrieving knowledge
• Use established knowledge to perform routine tasks, make standard products,
provide standard services; use general knowledge to survey exceptional situati-
ons, identify problem, consequences; use knowledge to describe situation and
scope problem; select relevant special knowledge to handle situation, identify
knowledge sources; observe and characterize the situation, collect and orga-
nize information; analyze situation, determine patterns, compare with others,
judge what needs to be done; synthesize alternative solutions, identify options,
create new solutions evaluate potential alternatives, appraise advantages and
disadvantages of each, determine risks and benefits of each; use knowledge to
decide what to do, which alternative to select; implement selected alternative
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Dynamic Associations: Content, Concepts,Topics with Varying Availability
Inhalt, Begriff, Beschreibung nach http://wortschatz.uni-leipzig.de/
Begriff
Inhalt
Beschreibung
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Extending Classical Logical KnowledgeRepresentation
Classical knowledge representation based on formulas or ex-
pressions
typical case for research in artificial intelligence; must however be
extended by meta-properties (importance, relevance, significance,
...)
Visual knowledge representation based on maps or graphs or
other visualisation means
Abstraction layer-based representation similar to data ware-
house approaches
Summarisation, cut-off, lead, header, teaser, scoping: for
simple capturing, linking
Non-classical reasoning: abductive, paraconsistent, qualitative,
approximative, ...
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Compare Visual Representation withTextual One
The database aims in supporting lecture and course scheduling within a university
application. A course is typically proposed. If the proposal becomes accepted then
the course is going to be planned. Typically planned course may be also held. The
course proposal, planning and organisation is bound to a semester. Some university
employees may be responsible for a course. Typically a course has one responsible
person. Responsibilities may vary by semesters. Courses are taught by professors.
Professors are specialisations of a person.
Courses may be given for various programs. The proposal also includes the assi-
gnment of a course kind. The course proposal typically also requests for a room
and for a time at which the course preferably could be given. Additionally time slots
may be in conflict with other proposals. Therefore, conflicting time slots are given
as well. The room and time preference may be overwritten during the planning
phase. The same opportunity exists for proposals for the kind of the course to be
given. If the time is assigned then typically a time slot is assigned. A course may
have several non-overlapping time slots.
The proposal for a course should be recorded. A person may act in the role of
somebody who inserted the course.
Finally, courses may also be held at a different location than originally planned.
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Compare Visual Representation withTextual One Is this better to read for users of ER diagrams?
Course Semester -Professor Person
Room
6
proposedCourse
Kind
6
Courseheld
[]
6
plannedCourse
[]
[]
Program
Time(Proposal,
SideCondition)
TimeFrame
Request
Proposal
Teacher
inserted
Responsible4Course
*1k
-
*
+
For more information: http://www.is.informatik.uni-kiel.de/∼thalheim/slides.htm
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Constraints for Knowledge
Entailment constraints based on internalised and externalised in-
clusion
e.g., inclusion or exclusion dependencies both at the external level as well for
the internal level
redundancy constraints for managed or at least controlled redundancy
Knowledge generating dependencies are typically reflected
through meta-properties
e.g., multi-valued and tuple-generating dependencies
Integration constraints e.g. functional dependencies
Identification constraints for identifying concept, content and to-
pic chunks within a given context
Abstraction constraints such as unique-name, identification,
unique-flavour assumptions
Explicit equivalence for paraphrase reconstruction
Localisation constraints for binding knowledge islands and sepa-
rating contradicting knowledge chunks (arbitrary agglutination is
senseless).
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Extending Classical Logical KnowledgeRepresentation
Bounded rationality theory: The capacity of the human mind for formu-
lating and solving complex problems is very small. When confronted with a highly
complex world, the mind constructs a simple mental model of reality and tries to
work within that model.
Spiral of knowledge: Knowledge creation always begins with the individual.
The main issue is making personal knowledge available to others by socialisation,
externalisation, internalisation (novice .. master) and combination.
Well-founded multi-level organisation: For knowledge to be useful
and organized it must be organized, e.g., in networks. Typical points of scope are
completeness connectedness, congruency, and perspective and purpose.
Separation of concern depending on level of sharing:
Factual; conceptual (perspectives, concepts, gestalt); expecta-
tional (judgments, hypotheses, expectations); methodological
(reasoning, strategies, methods, techniques)
Shared context for knowledge diffusion: codified or uncodified; ab-
stract or concrete; undiffused or diffused
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Eigenschaften der Analogiebeziehunganaloges Schließen zwischen Original und Modell bzw. Modell und Rea-
lisierung bzw. ...
mit Schlußregeln der Form
t ≈α s , α β
β(t) := β(s)
fur s(ource), t(arget)t ≈α s , α(s)
⋄α(t)mit lifting-Beziehungen zwischen Original und Modell bzw. ...
z : L1 × C → L2
mit Kontext Cα in Kontext Ci
(α1, i1) . . . (αn, in)
(α, i)φ
(α1, i1) . . . (αn, in) fuhren zu (α, i) mit der Seitenbedingung φ
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Eigenschaften der Analogiebeziehung:Adaquatheit
Ahnlichkeit von Modell und Original je nach Zweck und Nutzung
mit expliziter Ahnlichkeitsrelation
Regelhaftigkeit fur den Gebrauch mit exakten Regeln im Rahmen
eines (wohlfundierten) Systems
mit entsprechenden (formalen) Systemen zur Ableitung
Fruchtbarkeit aufgrund von Potential zur Gewinnung moglichst vie-
ler Aussagen
Kapazitat des Modelles
Einfachheit durch Abstraktion, Verkurzung, Konzentration auf das
Wesentliche und Relevante
damit bessere Erklarung, einfachere Losung, einfache Realisierung,
...
Plausibles Schließen auf der Grundlage von Abduktion, von Induktion
von analogem / autoepistemischen / Default- / defeasible / nicht-monotonen ...
Schließens, von Schließen mit Gegenbeispielen
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Parametrisierung als Verallgemeinerung der Parametertheorie der ADT
Spezifikation gegeben durch Signatur S und Axiome Σ aus LSAxiome bei ADT’s i.a. Gleichungen uber Termen
Parametrische Spezifikation ParSpec P gegeben durch Paar von Spezifikationen
((SF ,ΣF ), (SB ,ΣB)) von formalen Parametern und Basisspezifikation mit einer
Einbettbarkeitsbeziehung β : SF → SB und ΣB |= β(ΣF ) d.h. Eigenschaften der
formalen Parameter gelten auch in Zielspezifikation
• Anwendung von einer Spezifikation P1 auf P2 ist gegeben als Pushout des
Morphismus α : SF1 → SB2
Apply(P1,P2, α) ist assoziativ;
umfaßt Call-by-value, Call-by-reference, Call-by-value-result, Call-by-name, ...
• Sichten in der klassischen Form und Reduktion
• Kombination zweier ParSpec: Vereinigung der Basisspez. und Uberlagerung
der Typkonstruktoren
• Klassische Operationen: Erweiterung bzw. Anreicherung von ParSpec
durch Sorten und Signatur bzw. nur Signatur; Umbenennung von Sorten
und Signatur
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Returning to the notion of a MODELThomas, 2005: Unterscheidung in allgemeiner Modellbegriff nach Stachowiak, axiomatischer Modell-
begriff, abbildungsorientierter Modellbegriff und konstruktionsorientierter Modellbegriff Ein Modell ist
eine durch einen Konstruktionsprozeß gestaltete, zweckrelevante Reprasentation eines Objekts.
Abts, Mulder 2004: Unter Modell wird hierbei die allgemeine, meist mathematische Beschreibung eines
betriebswirtschaftlichen Problems angesehen.
Alpar et al. 2002: Ein Modell ist das Ergebnis eines Konstruktionsprozesses, bei dem die Wahrnehmung
von Inhalten eines ausgewahlten Gegenstandes zweckorientiert reprasentiert wird.
Balzert 2001: Modellbegriff allgemein: Vereinfachte, auf ein bestimmtes Ziel hin ausgerichtete Darstel-
lung der Funktion eines Gegenstandes oder des Ablaufs eines Sachverhalts, die eine Untersuchung oder
Erforschung erleichtert oder erst moglich macht.
Becker 1995: Modelle konnen aufgefaßt werden als ein Abbild der Realwelt fur Zwecke eines Subjektes.
[...] Modelle werden [...] als Hilfsmittel zur Erklarung und Gestaltung realer Systeme eingesetzt.
Erkenntnisse uber Sachverhalte bei realen Problemen konnen mit Hilfe von Modellen aufgrund der
Ahnlichkeit gewonnen werden, die zwischen dem realen betrieblichen System und dem Modell als Abbild
dieses System bestehen.
Becker, Schutte 1997: Modelle stellen das immaterielle und abstrakte Abbild eines Realweltausschnitts
fur Zwecke eines Subjekts dar.
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Returning to the notion of a MODELBecker, Schutte 2004: Ein Modell ist die Reprasentation eines Objektsystems eines Originals fur Zwecke
eines Subjekts. Es ist das Ergebnis einer Konstruktion eines Subjekts des Modellierers, das fur eine
bestimmte Adressatengruppe Modellnutzer eine Reprasentation eines Originals zu einer Zeit als relevant
mit Hilfe einer Sprache deklariert. Ein Modell setzt sich somit aus der Konstruktion des Modellierers,
dem Modellnutzer, einem Original, der Zeit und einer Sprache zusammen.
Donath et al. 1999: Modelle dienen – in unserem Betrachtungsfeld – der Beschreibung und Gestaltung
von Geschaftsprozessen. Sie dienen der Untersuchung von Struktur- und Verhaltenseigenschaften
von Unternehmen oder Institutionen. Modelle dienen der vereinfachenden Abbildung eines realen
Systems oder Systemausschnitts, wobei trotz aller Vereinfachung Strukturgleichheit oder zumindest
Strukturahnlichkeit zwischen Wirklichkeit gefordert wird.
Erzen 2001: Modelle sind adaquate, vereinfachende bzw. idealisierende Abbilder der Realitat.
Ferstl, Sinz 2001: In informaler Definition ist ein Modell ein System, das ein anderes System zielorientiert
abbildet.
Hars 1994: Dabei ist Vorraussetzung, dass es eine Abbildungsbeziehung zwischen den Elementen des
Modellsystems und den Elementen des Objektsystems gibt, uber die ein Teil der im Objektsystem be-
schriebenen Sachverhalte auf das Modellsystem ubertragen werden kann und von dort auf weitere Sach-
verhalte im Objektsystem zuruckgeschlossen werden kann. Die dreistellige Beziehung zwischen einem
Objektssystem, einem Modellsystem und einem Zweck wird als Modell bezeichnet.
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Returning to the notion of a MODELFischer et al. 2002: In den einzelnen Fachdisziplinen wird der Ausdruck Modell mit wechselnder Bedeutung
verwandt. Haufig wird darunter eine maßstabliche Nachbildung der Oberflache eines Systems z. B. Modell
einer Dampflokomotive, Landschafts- oder Gebaudemodell oder ein Ideal- oder Durchschnittsbild z. B.
Modell in der Modebranche, Modell eines Studiums verstanden. Das nachgebildete System kann dabei
bereits existieren oder lediglich geplant sein.
Ein Modell stellt aber kein moglichst vollstandiges Abbild der Realitat dar. Das ware die Realitat, z. B. das
Hochregallager, das betrachtete Burogerat oder das zu entwerfende Auto selbst. Ein Modell enthalt zur
Reduktion von Aufwand und Komplexitat gemaß Modelldefinition und - zweck nur die zu untersuchenden
Gesichtspunkte. Damit sind immer der Modellzweck z. B. Darstellung des Verhaltens und die Grenzen der
Modellgultigkeit anzugeben.
Grochla et al. 1974: Jedes System kann als Abbild oder Vorbild – d. h. als ‘Modell’ – fur ein anderes System
verwendet werden, falls zwischen den betreffenden Systemen eine partielle oder totale Strukturgleichheit
nachweisbar ist. [...] Modelle sind also stets entweder real-konkrete oder formalkonzeptionelle Systeme,
die als Reprasentation real-konkreter oder formal-konzeptioneller Systeme verwendet werden.
Hansen, Neumann 2005: Ein Modell engl.: model ist eine Abstraktion des betrachteten Realitatsausschnitts.
Unter Modellierung engl.: modeling werden die Tatigkeiten verstanden, die zur Definition eines Modells
fuhren.
Holey, Welter, Wiedemann 2004: Das Ziel der Modellierung ist, die Ablaufe im Unternehmen so darzu-
stellen, daß sie durch informationstechnische Anwendungssysteme unterstutzt oder vollstandig in diesen
Systemen abgebildet werden konnen.
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Returning to the notion of a MODELHeinrich 2001: [...] ein Modell ist die Beschreibung einer vereinfachenden Abbildung daruber, wie ein
bestimmter Ausschnitt der Wirklichkeit tatsachlich aussieht.
Heinrich, Roithmayr 1998: Modell model Methodensystem
Im allg. S. jede vereinfachende Abbildung eines Ausschnitts der Wirklichkeit oder eines Vorbilds fur die
Wirklichkeit Beschreibungsmodell, wobei trotz aller Vereinfachung Strukturgleichheit oder zumindest
Strukturahnlichkeit zwischen Wirklichkeit und Abbildung bzw. Vorbild und Wirklichkeit gefordert wird.
Zwischen M. und Wirklichkeit besteht eine bestimmte Beziehung, die Modellrelation genannt wird. Von
bestimmten Merkmalen des M.s kann auf bestimmte Merkmale der Wirklichkeit geschlossen werden
und umgekehrt Isomorphierelation.
In der Betriebswirtschaftslehre wird zwischen Erklarungsmodell und Entscheidungsmodell unterschieden.
Erklarungsmodelle sind Theorieteile, Entscheidungsmodelle sind fur den Entscheidungstrager in der
Praxis entwickelte Hilfsmittel zur Ermittlung optimaler Alternativen.
In der Wirtschaftsinformatik ist im Zusammenhang mit Methodenbanksystemen ein M. eine Menge von
Methoden zum Problemlosen.
Jost 1993: Aus den zuvor angefuhrten Interpretationen wird deutlich, daß unter einem Modell eine u. a.
aus Komplexitatsgrunden vereinfachte Beschreibung eines Ausschnittes aus der Realitat verstanden wird.
Kruse 1996: In Anlehnung an Grochla wird unter einem Modell ein abstraktes, vereinfachendes Abbild
eines Systems verstanden, das zu einem bestimmten erkenntnistheoretischen oder gestaltungsspezifischen
Zweck entwickelt wurde.
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Returning to the notion of a MODELKaschek:
Kuhn 1999: Produktionsaufgaben und PPS-Aufgaben werden in einer gemeinsamen Modellwelt be-
schreiben. Die zur Erstellung von Modellen anzuwendende Modellierungsmethode muss uber Konstrukte
und Regeln verfugen, die es erlauben Produktionsaufgaben und PPS-Aufgaben in allen relevanten
Attributen und Auspragungen zu beschreiben.
Lang 1997: Nach Stachowiak stellt ein Modell eine Entitat mit folgenden mindestens funf Auspragungen
dar: ‘X ist Modell des Originals Y fur den Verwender K in der Zeitspanne t bezuglich der Intention Z’.
Nonnenmacher 1994: Ein Modell ist ein Objekt, das von einem Subjekt auf der Grundlage einer
Struktur-, Funktions- oder Verhaltensanalogie zu einem Original eingesetzt und genutzt wird, um
Aufgaben zu losen, deren Durchfuhrung am Original selbst nicht moglich oder zu aufwendig ist.
Rosemann 1996: Derartige abstrahierende, immaterielle Abbilder eines Ausschnitts der realen Strukturen
bzw. des realen Verhaltens fur Zwecke eines Subjekts werden als Modelle bezeichnet.
Scharl 1997: Das Definiendum ‘Modell’ [X] steht fur eine immaterielle Reprasentation [Y] innerhalb
einer bestimmten Zeitspanne [t] fur Zwecke eines Subjektes, im konkreten Fall die Erkenntnis- und
Gestaltungsziele [Z] des Autors [g].
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Returning to the notion of a MODELScheer 1990: Unter einer Methode wird ein Verfahren zur Losung von Problemen einer Klasse und unter
Modell die Abbildung eines realen Systems verstanden.
Schlagheck 2000: Ein Modell ist das Ergebnis einer Konstruktion eines Modellierers, der fur die Modell-
nutzer eine Reprasentation eines Originals [...] zu einer Zeit als relevant [. . . ] mit Hilfe einer Sprache
deklariert [...]. Ein Modell setzt sich somit aus der Konstruktion des Modellierers, dem Modellnutzer,
einem Original, der Zeit und einer Sprache zusammen.
Scholz-Reiter 1990: Ein Modell ist ein Objekt, das von einem Subjekt auf der Grundlage einer Struktur-,
Funktions-, oder Verhaltensanalogie zu einem Original eingesetzt und genutzt wird, um Aufgaben zu
losen, deren Durchfuhrung am Original selbst nicht moglich oder zu aufwendig ist [...].
Schutte 1998: Ein Modell ist das Ergebnis einer Konstruktion eines Modellierers, der fur Modellnutzer
eine Reprasentation eines Originals zu einer Zeit als relevant mit Hilfe einer Sprache deklariert [...]. Ein
Modell setzt sich somit aus der Konstruktion des Modellierers, dem Modellnutzer, einem Original, der
Zeit und einer Sprache zusammen.
Schwarze 2000: Ein Datenmodell ist ein realitatskonformes, widerspruchsfreies Abbild der zu einem
bestimmten Aufgaben- oder Anwendungsbereich gehorigen Daten, Datenstrukturen und der Beziehungen
zwischen den Daten.
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Returning to the notion of a MODELSchwarzer, Krcmar 2004: Aus diesen Uberlegungen wird deutlich, dass das “neue” Modellierungs-
verstandnis keineswegs den Gedanken des Modells als Abbild der Realitat aufgibt. Ganz im Sinne der
gestaltungsorientierten BWL tritt jedoch neben die Abbildung heutiger Realitat die Abbildung und
damit erste Vorstellung moglicher zukunftiger Realitaten.
Wie alle Modelle sind auch Modelle von Informationssystemen ISModelle Verkurzungen der Realitat, in-
dem sie die reichhaltigen und konkreten Realitaten in abstrakte Sprachen abbilden. Bei der Modellierung
ist somit festzulegen, hinsichtlich welcher Aspekte eine Abstraktion vorgenommen wird. Da die Auswahl
der Elemente und Beziehungen nicht abschließend begrundet werden kann, ist davon auszugehen, dass
sie durch eine subjektive Vorstellung von Relevanz getroffen wird.
Schwegmann 1999: Der Begriff ‘Modell’ wird definiert als ,das Ergebnis einer Konstruktion eines
Modellierers, der fur Modellnutzer Elemente eines Systems zu einer Zeit als relevant mit Hilfe einer
Sprache deklariert.
Simoneit 1998: Ein Modell ist eine vereinfachende und abstrahierende Darstellung eines Rea-
litatsausschnitts, anhand dessen die wichtigsten Eigenschaften eines Originals erkannt, verstanden und
analysiert werden konnen. Dieses darzustellende Original wird auch Diskursbereich oder Objektsystem
genannt und bezeichnet real existierende Gegenstande, Phanomene oder Systeme. Modelle ermoglichen
somit Erklarung, Gestaltung und Kommunikation uber reale Objekte, ohne daß diese physisch prasent
sein mussen: Ein Modell stellt eine empirische Hypothese als vereinfachte Reprasentation eines spezifi-
schen Realphanomens auf.
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Returning to the notion of a MODELSteinmuller 1981: Jedes Modell setzt also ein Subjekt voraus, das seinen Verwendungszweck vorgibt,
namlich einem bestimmten Verhalten jemandes zu dienen. Oder anders: ein Modell ist stets nur ein
‘Modell – wovon – fur wen – wofur’; Modelle sind Abbildungen von etwas fur jemand zu einem
Verhalten. Oder schließlich: Modelle sind subjektrelativ.
Vetschera 1995: Die dritte Sichtweise von Modellen als Problembeschreibungen stellt einen Mittelweg
zwischen beiden diesen Extremen dar. In dieser Sichtweise versteht man unter einem Modell die
hinreichend allgemeine, meist mathematisch formulierte Beschreibung eines Problems.[...] In dieser
Arbeit wird der dritten Sichtweise gefolgt, Modelle werden hier als allgemeine mathematische Problem-
beschreibungen definiert.
vom Brocke 2003a: Ein Modell ist die Verdichtung von Wahrnehmungen zu Inhalten eines Gegenstands,
[. . . ] um auf diese Weise einen spezifischen Zweck zu dienen. Die Gestaltung von Modellen erfolgt in
Konstruktionsprozessen.
Wenzel 2000: Ein Modell ist eine vereinfachte Nachbildung eines geplanten oder real existierenden Sy-
stems mit seinen Prozessen in einem anderen begrifflichen oder gegenstandlichen System. Es unterschei-
det sich hinsichtlich der untersuchungsrelevanten Eigenschaften nur innerhalb eines vom Untersuchungs-
ziel abhangigen Toleranzrahmens vom Vorbild.
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Returning to the notion of a MODELGrounding G: concepts, foundations, language as carrier, cargo
(Meta-)Basis B: basement, paradigms, theories; status in application;
context; paradigmatic evolution; abstraction, scale
Deployment D: goal, purpose, function; reason
Community of practice P: stakeholder with their roles and plays,
with their interests, portfolio and profiles
Context C: time, space, scope
Quality Q: correctness (...), generality (...), usefulness (...), compre-
hensibility (..., parsimony, ...), robustness, novelty (...)
Viability V : corroboration, coherence, falsifiability, stability, assuran-ce(restrictions, modality, confidence)
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Returning to the notion of a MODELGiven a collection of artifacts M∗,M1, ...,Mk, a community of
practice P (⊆ P), a grounding G (⊆ G), viability V (⊆ V), bases B (⊆ B),
context C (⊆ C), and deployment D (⊆ D).
The artifact M∗ is called a modelfor M1, ...,Mk by P for D with V
if is is appropriate with Qa ∪Qf ∪Qu and within C
based on B using G , i.e.,
• it is adequate (has potential for goals) [similar + regular + fruitful
+ simple] according to the relation between M∗ and M1, ...,Mk
at level of Qa for D with V within B and grounded by G,
• it is fit for D with Qf within C and compliant with G and B and
• it is useful for P within their D and at level of Qu.
Model suite: M∗1, ...,M∗
n instead of M∗
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...out of scope of the definition ...
to model as the construction process; artifact can be constructed
constructive models versus immaterial models versus virtual models versus imagina-
tions
recognition of a stakeholder
see however assessment as more objective element
homomorphic image as specific quality criterion
see however similarity as element of adequacy
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Comparing Our Notion to IS CoP Notions
Abts: betriebsw. Problem, math. Sprache, allgemeinAlpar: Wahrnehmung, Zweck, Reprasentation, KonstruktionBalzert: vereinfacht, Ziel, ErforschungBecker: Abbild, ZweckBecker, Schutte: Abbild, Original, Zweck, SubjektBecker, Schutte: Abbild, Zweck, Subjekt, Sprache, Zeit, KonstruktionDonath: Zweck, Original,HomomorphieErzen: Qualitat, adaquat, Abbild, OriginalFerstl: Abbild, zielorientiertFischer: Abbild, Verkurzung, ZweckGrochla: Abbild, Vorbild, Verkurzung, Zweck, HomomorphieHansen: Verkurzung, OriginalHars: Abbildung, Zweck, OriginalHeinrich: Abbild, Vereinfachung, ZweckHeinrich, Roihmayr: Abbild, Vorbild, HomomorphieHoley: Ablaufdarstellung, Vorbild fur SystemJost: Original, Abbild Nur einige Aspekte reflektiert. Konstruktion als das Modellieren -Herstellen.
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Comparing Our Notion to IS CoP Notions
Kaschek:Kruse: Abbild, Zweck, VerkurzungKuhn: Original, Abbild, SpracheLang: Original, Community, Kontext, ZweckMahr: Original, Community, Abbild, Cargo, SpracheNonnenmacher: Original, Abbild, ZweckRosemann: Abbild, Original, Zweck, CommunityScharl: Original, Abbild, Kontext, Zweck, CommunityScheer: Abbild, OriginalSchlagheck: Abbild, Original, Kontext, Sprache, KonstruktionScholz-Reiter: Abbild, Original, Zweck,HomomorphieSchutte: Abbild, Original, Zweck, Sprache, Qualitat, KonstruktionSchwarze: Abbild, Original, Zweck, QualitatSchwarzer: Abbild, Original, Verkurzung, subjektivSchwegmann: Abbild, Original, Qualitat, Sprache, KonstruktionSimoneit: Abbild, Original, Verkurzung, ZweckSteinmuller: Abbild, Original, Community, ZweckThomas: goal, representation, origin, KonstruktionVetschera: Problem, Beschreibung, Zweckvom Brocke: Verkurzung, Abbildung, Wahrnehmung, KonstruktionWenzel: Verkurzung, Toleranz (Qualitat) Nur einige Aspekte reflektiert. Konstruktion als das Modellieren - Herstellen.
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Relationship to Stachowiak and Extensions
Mapping property: one kind of model association
Truncation property: abstraction as some kind of association
Pragmatic property: goal, community, context (time)
Extension property: as specific partiality of mapping into instead surjective
Distortion property: as specific kind of mapping related to the goal
Idealisation property: as specific kind of mapping
Carrier property: grounding
Added value property: as one kind of combined quality
Purpose property: part of pragmatic property, more explicit Mapping-based models.
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Returning to the notion of a MODEL Language-based constructive models
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Parameters for being a modelkind of artifacts deployment CoP viability basis grounding quality context
model
M∗M1, ...,Mk D P V B G Q C
conceptual
DB mo-
del (DB
schema)
k=2: appli-
cation do-
main, reali-
sation
G+F:
construct-
ion of
augmented
reality
modeler,
implemen-
ter
∗ CS & IS
paradigms
concepts,
CM lan-
guages
similarity,
...
R-DBMS,
data,
current,
branch
chemical
model
... ... ... ∗ ... ... ... ...
physical
model as
special
theory
... ... ... ∗ ... ... ... ...
physical
model as
theory
substitute
... ... ... ∗ ... ... ... ...
physical
toy model
... ... ... ∗ ... ... ... ...
developmental
physical
model
... ... ... ∗ ... ... ... ...
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Stereotypes for Disciplines
kind of model M∗
artifacts M1, ...,Mk:
deployment D
CoP P:
viability V:
basis B:
grounding G:
quality Q:
context C:
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Stereotypes for Disciplines: InformationSystems
kind of model M∗
artifacts M1, ...,Mk:
deployment D
CoP P:
viability V:
basis B:
grounding G:
quality Q:
context C:
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Stereotypes for Disciplines: Programs
kind of model M∗
artifacts M1, ...,Mk:
deployment D
CoP P:
viability V:
basis B:
grounding G:
quality Q:
context C:
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Stereotypes for Disciplines
kind of model M∗
artifacts M1, ...,Mk:
deployment D
CoP P:
viability V:
basis B:
grounding G:
quality Q:
context C:
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Stereotypes for Disciplines
kind of model M∗
artifacts M1, ...,Mk:
deployment D
CoP P:
viability V:
basis B:
grounding G:
quality Q:
context C:
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Publications on Science and Art ofConceptual Modelling
• A. Dahanayake and B. Thalheim. Towards a framework for emergent modeling. In ER Work-
shops, volume 6413 of Lecture Notes in Computer Science, 128–137. Springer, 2010.
• A. Dahanayake and B. Thalheim. Enriching conceptual modelling practices through design
science. In BMMDS/EMMSAD, volume 81 of Lecture Notes in Business Information Processing,
497–510. Springer, 2011.
• B. Thalheim. Towards a theory of conceptual modelling. Journal of Universal Computer Science,
2010, 16, 20, 3102–3137.
• B. Thalheim. The theory of conceptual models, the theory of conceptual modelling and foun-
dations of conceptual modelling. In The Handbook of Conceptual Modeling: Its Usage and Its
Challenges, chapter 12, 543–578. Springer, Berlin, 2011.
• B. Thalheim. The science of conceptual modelling. In Proc. DEXA 2011, volume 6860 of LNCS,
12–26, Berlin, 2011. Springer.
• B. Thalheim. Integrity constraints in (conceptual) database models. In The Evolution of Con-
ceptual Modeling, volume 6520 of Lecture Notes in Computer Science, 42–67, Berlin, 2011.
Springer.
• B. Thalheim. The art of conceptual modelling. In Proc. EJC 2011, 203–222, Tallinn, 2011.
• B. Thalheim. Culture and art of conceptual modelling. Anwendungsorientierte Organisationsge-
staltung, 127–144. baar, Hamburg, 2011.
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Publications on Model Suites, Evolution,Migration
• A. Dahanayake and B. Thalheim. Co-evolution of (information) system models. In EMMSAD
2010, volume 50 of LNBIP, 314–326. Springer, 2010.
• A. Dahanayake and B. Thalheim. Towards a framework for emergent modeling. In ER Work-
shops, volume 6413 of Lecture Notes in Computer Science, 128–137. Springer, 2010.
• M. Klettke and B. Thalheim. Evolution and migration of information systems. In The Handbook
of Conceptual Modeling: Its Usage and Its Challenges, chapter 12, 381–420. Springer, Berlin,
2011.
• B. Neumayr and M. Schrefl und B. Thalheim. Modeling techniques for multi-level abstraction.
In The Evolution of Conceptual Modeling, volume 6520 of Lecture Notes in Computer Science,
68–92, Berlin, 2011. Springer.
• B. Thalheim. Model suites. In H. Jaakkola, editor, Selected Topics on Distributed Disaster
Management: Towards Collaborative Knowledge Clusters., 108 – 128. Tampere University Press,
Porin yksikko, 2008.
• B. Thalheim. The conceptual framework to multi-layered database modelling. In Proc. EJC,
118–138, Maribor, Slovenia, 2009.
• B. Thalheim. Model suites for multi-layered database modelling. In Information Modelling
and Knowledge Bases XXI, volume 206 of Frontiers in Artificial Intelligence and Applications,
116–134. IOS Press, 2010.
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Publications on Tool-Based Development• M. Albrecht, M. Altus, E. Buchholz, H. Cyriaks, A. Dusterhoft, J. Lewerenz, H. Mehlan, M. Steeg,
K.-D. Schewe, and B. Thalheim. RADD - Rapid application and database development. Rea-
dings - Main papers published in the RADD project. CAU Kiel, Department of Computer Science,
http://www.is.informatik.uni-kiel.de/∼thalheim/indeeerm.htm, 1998.• G. Fiedler, H. Jaakkola, T. Makinen, B. Thalheim, and T. Varkoi. Co-design of web information systems
supported by SPICE. Information Modelling and Knowledge Bases, XIX, 2009.• H. Jaakkola and B. Thalheim. A framework for high quality software design and development: A
systematic approach. IET Software, 2010. to appear.• H. Ma, K.-D.Schewe, B. Thalheim, and J. Zhao. View integration and cooperation in databases, data
warehouses and web information systems. Journal on Data Semantics, LNCS 3730, 213–249, 2005.• M. Steeg. RADD/raddstar - A rule-based database schema compiler, evaluator, and optimizer. PhD
thesis, BTU Cottbus, Computer Science Institute, Cottbus, October 2000.• B. Thalheim. Entity-relationship modeling – Foundations of database technology. Springer, Berlin,
2000.• B. Thalheim, K.-D. Schewe, and Hui Ma. Conceptual application domain modelling. In APCCM,
volume 96 of CRPIT, 49–57. Australian Computer Society, 2009.• B. Thalheim. Co-design of structuring, functionality, distribution, and interactivity of large information
systems. Technical Report 15/03, BTU Cottbus, Computer Science Institute, Cottbus, September 2003.
190pp.• B. Thalheim. Conceptual modeling in information systems engineering. In J.Krogstie and A. Lothe,
editors, Challenges to Conceptual Modelling, 59–74, Berlin, 2007. Springer.
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Publications on Pattern Development
• T. Feyer, K.-D. Schewe, and B. Thalheim. Conceptual design and development of information
services. In Proc. ER’98, LNCS 1507, Springer, 1998, 7–20. Springer, Berlin, 1998.
• T. Feyer and B. Thalheim. Many-dimensional schema modeling. In ADBIS 2002, LNCS 2435,
305–318. Springer, 2002.
• T. Feyer and B. Thalheim. A model for defining and composing interaction pattern. In EJC’2002,
volume Information Modelling and Knowledge Bases XIV, 277–289, 2002.
• Hui Ma, K.-D. Schewe, and B. Thalheim. Modelling and maintenance of very large databa-
se schemata using meta-structures. In UNISCON, volume 20 of Lecture Notes in Business
Information Processing, 17–28. Springer, 2009.
• K.-D. Schewe and B. Thalheim. Development of collaboration frameworks for web informa-
tion systems. In IJCAI’07 (20th Int. Joint Conf on Artificial Intelligence), Section EMC’07
(Evolutionary models of collaboration), 27–32, Hyderabad, 2007.
• B. Thalheim. Many-dimensional database modeling on the basis of application frameworks.
Technical Report Preprint I-08-2000, Brandenburg University of Technology at Cottbus, Institute
of Computer Science, 2000.
• B. Thalheim. The person, organization, product, production, ordering, delivery, invoice, accoun-
ting, budgeting and human resources pattern in database design. Technical Report I-07-2000,
Computer Science Institute, Brandenburg University of Technology at Cottbus, 2000.
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Publications on Component Development
• A. Dusterhoft and B. Thalheim. Linguistic based search facilities in snowflake-like database schemes.
Data and Knowledge Engineering, 48:177–198, 2004.
• T. Feyer and B. Thalheim. Component-based interaction design. In EJC’2003, volume Information
Modelling and Knowledge Bases XV, 19 – 36, 2003.
• G. Fiedler and B. Thalheim. An approach to conceptual schema evolution. Technical report,
Christian-Albrechts-Universitat Kiel, 2007.
• K.-D. Schewe and B. Thalheim. Component-driven engineering of database applications. In Markus
Stumptner, Sven Hartmann, and Yasushi Kiyoki, editors, Third Asia-Pacific Conference on Concep-
tual Modelling (APCCM2006), volume 53 of CRPIT, 105–114, Hobart, Australia, 2006. ACS.
• P. Schmidt and B. Thalheim. Component-based modeling of huge databases. In ADBIS’2004,
LNCS 3255, 113–128, 2004.
• B. Thalheim. Component construction of database schemes. In Proc. ER’02, LNCS 2503, 20–34.
Springer, 2002.
• B. Thalheim. Component development and construction for database design. Data and Knowledge
Engineering, 54:77–95, 2005.
• B. Thalheim. Engineering database component ware. In TEAA’06 post proceedings, LNCS 4473,
1–15, Berlin, 2007. Springer.
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Publications on Genericity• A. Bienemann. A generative approach to functionality of interactive information systems. PhD
thesis, CAU Kiel, Dept. of Computer Science, 2008.
• A. Bienemann, K.-D. Schewe, and B. Thalheim. Towards a theory of genericity based on government
and binding. In Proc. ER’06, LNCS 4215, 311–324. Springer, 2006.
• A. Binemann-Zdanowicz, B. Thalheim, and B. Tschiedel. Storyboarding for adaptive content gene-
ration for e-learning web services. In Computer Science Report I-10/2003, Brandenburg University
of Technology at Cottbus, 2003.
• A. Binemann-Zdanowicz. Towards generative engineering of content-intensive applications. In Proc.
Principles of Software Engineering Conference (PRISE 2004), 41–49, 2004.
• M. Klettke. Reuse of database design decisions. In P. P. Chen, D. W. Embley, J. Kouloumdjian,
S. W. Liddle, and J. F. Roddick, editors, Proc. Advances in Conceptual Modeling, LNCS 1727,
213–224. Springer, Berlin, 1999.
• T. Moritz. Visuelle Gestaltungsraster interaktiver Informationssysteme als integrativer Bestandteil
des immersiven Bildraumes. PhD thesis, HFF Berlin-Babelsberg, 2006.
• B. Thalheim. The conceptual framework to multi-layered database modelling. In Proc. EJC, 118–
138, Maribor, Slovenia, 2009.
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Publications on Co-Design• Dusterhoft, A., Thalheim, B.: SiteLang: Conceptual Modelling of Internet Sites. Proc. ER’2001, LNCS 2224,
179 - 192. Application to webservices• Feyer, Th.; Thalheim, B.: E/R Based Scenario Modelling for Rapid Prototyping of Web Information Services.
Proc. WWWCM’99, 253 - 263. Application to webservices generation• G. Fiedler, H. Jaakkola, T. Makinen, B. Thalheim, and T. Varkoi. Co-design of web information systems
supported by SPICE. Information Modelling and Knowledge Bases, XX:123–138, 2009.
• Goldin, D., Srinivasa, S., Thalheim, B.: IS=DBS + Interaction: Towards principles of information system
design. Proc. ER 2000, LNCS 1920, 140 - 153. The theoretical foundation• Klettke, M.: Reuse of database design decisions. Proc. REIS’2000, LNCS 1727, 213-224. Reuse structures
and intelligently acquire integrity constraints• Lewerenz, J., Schewe, K.-D., Thalheim, B.: Modelling data warehouses and OLAP applications by means of
dialogue objects. Proc. ER’1999, LNCS 1728, 354-368. OLAP in a consistent, powerful and simple way• K.-D. Schewe and B. Thalheim. The co-design approach to web information systems development. International
Journal of Web Information Systems, 1(1):5–14, March 2005.
• Schewe, K.-D.; Thalheim, B.: Towards a theory of consistency enforcement. Acta Informatica, 36, 1999, 97-141.
Instead of falling into the traps of rule triggering systems• Steeg, M; Thalheim, B.: Conceptual Database Application Tuning. Proc. SCI’2000, 226-231. Tune instead of
normalize• Thalheim, B.: Entity-Relationship Modelling - Foundations of Database Technology. Springer, Berlin, 2000.
The HERM “bible”• Thalheim, B.: Logics and Database Modelling. Proc. ICLP ‘99, MIT Press, 6-21. The relationship to logics• Thalheim, B.: Codesign of database systems and interaction - Thin and consistent UML. Proc. OTS’2000,
1-17. Codesign - the ultimate basis for best practices UML
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c⃝B. Thalheim
Publications on Web IS Engineering• A. Binemann-Zdanowicz. Sitelang::edu - towards a context-driven e-learning content utilization model. In Proc.
SAC’2004 (ACM SIGAPP), Nicosia, Cyprus, March 2004, 924–928. ACM, 2004.• A. Dusterhoft and B. Thalheim. Linguistic based search facilities in snowflake-like database schemes. Data and
Knowledge Engineering, 48:177–198, 2004.• T. Feyer, K.-D. Schewe, and B. Thalheim. Conceptual design and development of information services. In Proc.
ER’98, LNCS 1507, Springer, 1998, 7–20. Springer, Berlin, 1998.• R. Kaschek, K.-D. Schewe, B. Thalheim, and Lei Zhang. Integrating context in conceptual modelling for web
information systems, web services, e-business, and the semantic web. In WES 2003, LNCS 3095, 77–88. Springer,
2003.• T. Moritz, R. Noack, K.-D. Schewe, and B. Thalheim. Intention-driven screenography. In Proceedings ISTA
2007, volume LNI 107, 128–139, 2007.• T. Moritz, K.-D. Schewe, and B. Thalheim. Strategic modelling of web information systems. International Journal
on Web Information Systems, 1(4):77–94, 2005.• K.-D. Schewe and B. Thalheim. Conceptual modelling of web information systems. Data and Knowledge
Engineering, 54:147–188, 2005.• K.-D. Schewe and B. Thalheim. Pragmatics of storyboarding for web information systems: Usage analysis. Int.
Journal Web and Grid Services, 3(2):128–169, 2007.• K.-D. Schewe and B. Thalheim. Personalisation of web information systems - a term rewriting approach. Data
and Knowledge Engineering, 62(1):101–117, 2007.• B. Thalheim. Readings in fundamentals of interaction in information systems. Reprint, BTU-Cottbus, acces-
sible through http://www.is.informatik.uni-kiel.de/∼thalheim, Collection of papers by C. Binder, W. Clauß, A.
Dusterhoft, T. Feyer, T. Gutacker, B. Heinze, J. Lewerenz, M. Roll, B. Schewe, K.-D. Schewe, K. Seelig, S.
Srinivasa, B. Thalheim, 2000.• B. Thalheim and A. Dusterhoft. Sitelang: Conceptual modeling of internet sites. In H. S. Kunii, S. Jajodia, and
A. Sølvberg, editors, ER, volume 2224 of LNCS, 179–192. Springer, 2001.
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Publications on Database Theory• E. Borger and B. Thalheim. A method for verifiable and validatable business process modeling.
In Software Engineering, volume 5316 of Lecture Notes in Computer Science, 59 – 115. Springer,
2008.
• D. Goldin, S. Srinivasa, and B. Thalheim. IS = DBS + interaction - towards principles of
information systems. In A. H. F. Laender, S. W. Liddle, and V. C. Storey, editors, ER, volume
1920 of LNCS, 140–153. Springer, 2000.
• H.-J. Lenz and B. Thalheim. A formal framework of aggregation for the OLAP-OLTP model.
Journal of Universal Computer Science, 15(1):273 – 303, 2009.
• K.-D. Schewe and B. Thalheim. Reasoning about web information systems using story algebra.
In ADBIS’2004, LNCS 3255, 54–66, 2004.
• K.-D. Schewe and B. Thalheim. Fundamental concepts of object oriented databases. Acta
Cybernetica, 11(4):49–81, 1993.
• K.-D. Schewe and B. Thalheim. Readings in object-oriented databases. Reprint, BTU-Cottbus,
accessible through http://www.is.informatik.uni-kiel.de/∼thalheim, Collection of papers by C.
Beeri, K.-D. Schewe, J.-W. Schmidt, D. Stemple, B. Thalheim, I. Wetzel, 1998.
• O. Seleznev and B. Thalheim. Average case analysis in database problems. Methodology and
Computing in Applied Probability, 48:177–198, 2003.
• B. Thalheim. Entity-relationship modeling – Foundations of database technology. Springer,
Berlin, 2000.
• B. Thalheim. Model suites. In 2nd International Workshop on Knowledge Cluster Systems,
20–40. IOS Press, 2008.
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