SlideShare a Scribd company logo
1 of 55
Download to read offline
1
Conceptual Modelling
An Introduction
(Module M2)
The 2013 International Conference on
Collaboration Technologies and
Systems
(CTS 2013)
2
Module Layout
• Conceptual modelling for ontology building
– a key human activity
– Modeling and representation
– The nature of knowledge
– Syntax and semantics: from conceptual to
ontology modeling
– Static vs dynamic modeling (entity vs process)
– Key conceptual constructs (building bricks)
• Collaborative ontology building
• Languages, tools, and platforms
• And ... some practical exercises
3
Perception and conceptualizion
• Objects cannot be seen … they appear as a sum
of phenomena associated to them (a table?
shapes, substance, color, weight, volume, …)
• Perception is an analytic activity (over individual
phenomena) that is integrated afterward, to form
the unity of the observed entity
• From the observation to the conceptualization
• Conceptualization: of entities, categories,
relationships
• Abstraction: selection of relevant aspects, it is
fundamental in modelling the reality (too complex)
• A Conceptual Model is a set of concepts well
organized, on the base of principles independent
of the domain
4
Modeling is a key activity
Modeling is a key activity to understand /
communicate a description of a given reality
• when the modeled objects do not exist yet (e.g.,
in designing a complex artifact),
• when the fragment of reality is not tangible (e.g.,
the organization of an enterprise),
• Concerns general (e.g., a mock-up of a building)
or specific (e.g., the electric circuit schema)
aspects
• Different models for the same complex entity
(e.g., the human body)
• Objectives of modeling: understanding,
communicating, exchanging information, predict
behaviors and future situations (states)
5
General Model Theory
Three pillars of a General Model Theory
(Philosopher Herbert Stachowiak, 1973):
(1) Mimetism: Models are representative of
“something”;
(2) Reductionism: Models are reductive in the
sense that they depict some but not all
aspects of the given fragment of reality;
(3) Pragmatism: Models are created for a
purpose in the sense that a model is created
at a given time, having a purpose in mind.
Corollary: same reality at the same time may
have different models
6
Different modeling methods
• Concrete, e.g., a plastic model of a building
• Figurative, e.g., a drawing of a car, of a road
map
• Narrative, e.g., a text describing a landscape
• Schematic: schemes and diagrams that
illustrate the vectorial forces in the structure of
a bridge, the blue-print of an electric circuit
• Mathematical: e.g., a system of equations that
rigorously represents the air flowing in a wind
tunnel; a Boolean expression representing a
digital circuit
7
Conceptual Model
Symbolic Modeling: describe the reality
representing the relevant concepts and
their relationships, starting from …
• concepts, expressed by means of terms
(words) and symbols
– Figurative symbols: icons
– Mathematical symbols: letters, operators, …
– Diagrammatic symbols : boxes, ovals, arrows
Engineer tractorfixes
Language
• Needed to communicate, among people,
between people and computers
• Used to create Conceptual Models
• Natural Language: spoken by humans
• Artificial Language: conceived ad-hoc
Language, to create
• Sentences complex structures expressing
concepts, composed by
– Atomic elements: symbols and terminology
– Complex elements (... also sentences) 8
9
Language - Syntax
• Symbols & Terms and composition rules for
sentences (i.e., complex structures)
engineer tractorfixes
engineer tractor
fixes
Syntactically correct structure
Syntactically incorrect structure
Relation
Object
Actor
10
Abstract Formal Syntax
(sketch)
Unary Concept - uc: a (actor), o (object), p
(process)
Conceptual (binary) Relation: R
Conceptual Structure: cs
uc = a | o | p
cs = uc | cs R cs
Recursively, nested structures
cs = cs R (cs R2 cs)
Syntax - Example
uc1 = Engineer
uc2 = Tractor
R = fixes
(uc1 R uc2)
(Engineer fixes Tractor)
The triple pattern: (Subject Rel Object)
Nested structures:
cs R (cs R2 cs)
(Engineer fixes (Tractor ownedBy Father)) 11
engineer
tractor
fixes
12
Language - Semantics
• Symbols and sentences (Syntactically
correct )
• From the meaning of symbols & terms to
the meaning of sentences
engineer tractoreats
engineertractor fixes
engineer tractorblabla
What’s in a Concept?
Concept, is formed by
• Description (Intention)
– Purely descriptive, including its properties and
relationships
– If rigorous, it indicates necessary and sufficient
conditions
• Population (Extension)
– The collection of all individuals (instances) of the
concept
– Each individual needs to satisfy the intentional
description of the concept
13
14
Formal Semantics
(sketch)
Given a set of conceptual structures:
K = {csj}
Given a domain of interpetation:
D = {ei}
We define a semantic function S:
S : K  2D
{csi} {ei}
K D
S(Intentional level) (Extensional level)
Denotation
15
(Engineer Fixes Tractor)
Engineer
Fixes Tractor
K = {csj}
D = {ei}
S
Denotation is compositional
16
Standard FO Semantics
Semantics given by standard First Order
Interpetation theory:
Interpretation domain DIInterpretation function I
Individuals
John
Mary
Concepts
Lawyer
Doctor
Vehicle
Relationships
hasChild
owns
(Lawyer AND Doctor)(by I. Horrocks)
Conceptual Modelling
Principles
17
Synchronism vs Diachronism
• Static modelling (Synchronic)
– What we see when we take a picture
– Entities, Relationships, Properties
– Then, we automatically apply a
conceptualization process
– We recognise Categories, similarities and
differences, ... and we attach names to them
• Dynamic modelling (Diachronic)
– We understand how the reality: is evolving,
may further evolve, and we can influence it
18
19
Conceptual Modeling
Static View
An exercise of conceptual modeling (static
knowledge)
• Observe the reality, identify the relevant
elements
• Create a schematic description by means of a
suitable terminology and notation (conceptual
model)
• Static Conceptual Modeling, includes Terms
denoting:
– Entities populating the observed reality
– Properties of entities (e.g., color, size, …), and
– Relationships among entities
A First Collaborative Conceptual Model
20
A bedroom (V. VanGogh)
Exercise: Build a terminological model: entities, properties, relationships
(keep it, later we will ‘formalise’ the model)
A Collaborative Exercise
Your First Shared Conceptual Model
• Take pen ‘n paper
• Given a term, be ready to define a concept, by
using 140 characters: a Semantic Tweet
• Then, be ready to experience the rich diversity
of term interpretations
• Hence, we will collaborate and progressively
move towards a shared view of the concept
• Later, you will build a simplified ontology by
using
– SKOS and a N3 like formalization
21
22
Conceptual Modeling
Dynamic View
• Reality is continuously evolving
• Objects change state (position, form, color,
…) due to specific activities
• Activity: constructing, distroying, fixing,
traveling, cooking, washing, painting, …
• Identify, for each activity:
– Objects, that are modified / created / …
– Actors, that perform the activities
– Tools, used in performing the activities
23
Modeling an activity
TrimmingSheep[f]
wool
Sheep[s]
scissors
Shepherd
With an intuitive diagramming notation
24
Exemples of Dynamic
specification
Process: cooking
– In: egg
– Actor: cook
– Out: omelet
Process: forging
– In: steel
– Actor: blacksmith
– Out: gear
Process: designing
– In: requirement, skills
– Actor: architect
– Out: design
25
Symbolism in dynamic
representation
The great capacity of P. Bruegel (16th
cnetury) has been to organize in a single
scene almost 120 sayings drawn from the
popular wisdom that define a symbolic
universe: that of a reversed world.
Inspired by “Adagia”, a literary work of
Erasmus from Rotterdam
26
Flamish sayings (Brugel)
27
Some Flemish Sayings
• “Carring the light to the day with a
hamper” (losing time with useless
occupations) [49]
• “Filling up the well when the calf has
drowned” (repair a situation when is too
late) [57]
• “learning to bow to travel the world”
(you need to be flexible) [60]
28
A systematic approach
Conceptual Modeling
Static Dimension
29
Concepts and instances
Conceptual modeling refers (mainly) to concepts,
instances are modeled by data (numbers, strings, URIs).
With concept we mean a mental abstraction built (in
general) starting from the reality. A concept defines the
characteristics (properties) common to a set of coherent
objects.
With instance we mean any element of the extension of a
concept. It is an individual object with identity, described
by its relevant characteristics (i.e., all its properties are
evaluated).
In understanding the reality, the first primary notions
are: concepts and instances
30
Conceptualization
Classification
Conceptualization & Instantiation
DOG
animal
that
barks
------
Fido
------
------
Instance Descr.
individual
concept
Symbolic Concept Descr. Mental world
Real world
Digital world
31
Semiotics
Theory of Signs
• In linguistic theories (Semiotics)
– Relation between symbols (of the language) and concepts
(denotation)
• In formal theories
– Relation between concepts models and instances (instatiation)
• The ontological-semiotic closure (C. K. Ogden triangle1)
Symbol
Concept
Instance (Referent)
(1The Meaning of Meaning: A Study of the Influence of Language upon Thought and
the Science of Symbolism, 1923)
32
The pipe
(Surrealiste painter René.Magritte)
[this is NOT a pipe]
Ontology Building
Key Conceptual Constructs
33
34
Building a Concept
Conceptual modeling aims at creating a description
of a fragment of realty, through the definition of
some concepts, with their correlations
A concept (entity): defined by using a terminological
expression:
• Label (concept name)
– Person
• Properties
– Name, age, address (attributes – dataProperties)
– Friends, company (associations – references)
• Relations with other concepts
– Married (symmetrical)
john married mary  mary married john)
35
Entities and Relationships
• Entities (Concepts)
– Tangible: Student, Person, Cat, Bike, Chair
– Intangible: Course, Film, Sale, Story
– Abstract: Natural number, Algorithm,
Philosophy, Luck
• Relations (conceptual)
– Follows(Student,Course)
– Owner(Person,Bike)
– xxx(Person,Student)
– yyy(Bike,Weel)
36
Attributes - Associations
Cat:
• Name, Age, Owner
Person:
• Name, Age, Phone, FCode, Weight
Student:
• Name, Age, Phone, FC, Weight,
University, Faculty, AverageMark
Attribute: printable data
Association: entity-id (reference)
37
Conceptual Kinds
OPAL – Object, Process, Actor modeling
Language
• Object
– Passive entity, whose state may change by
means of the effect of a process
• Actor
– Active entity, capable of performing a
process
• Process
– Activities performed by actors, aimed at
modifying entities
(a kind may depends on the situation)
Conceptual Relations
• O, P, A are unary concepts (= entities)
• They specify what exist in a given
(fragment of the) reality
• The next step is to define their mutual
relationships: binary, n-ary relations
We have:
• Universal relations: they are valid in any
possible observable domain
• Domain specific relations: make sense
only in specific contexts 38
Universal Relation:
Refinement
• It is a vertical relation
• Associate a concept to a more refined one
that is:
– Better specified,
– Enriched in its description
Two golden hierarchical relations
• Specialization: IsA
• Decomposition: PartOf
39
Specialization
• Given a concept, refine its description
• Increase the precision of the description
• More precise classification of individuals
• Produces a Taxonomy
Ex.
Student IsA Person;
Teenager IsA Person
• Inverse
Generalization 40
41
Specialization
Given a concept, it is specialized by
applying rigorous mechanisms:
• Extension: introducing additional
properties
– Student extends person, with university,
faculty, avergeMark
• Restriction: restrincting the range (i.e.,
legal values) of one or more properties
– Teenager restrincts person on age (with
values between 13 and 19)
42
Attributes & Associations
• Cat: Name, Age, Owner
• Person: Name, Age, Phone, FC, Weight
• Student: Name, Age, Phone, FC, Weight,
University, Faculty, AverageMark
N
A
P
FC
W
U
F
AM
43
Taxonomy (ISA)
Vehicle
Public Vehicle
Plane TrainBikeCar
Private Vehicle
Example: The IsA hierarchy of Vehicle
Key feature of a Taxonomy: property inheritance
(in case with restricted range)
44
Inheritance
(of properties)
name
age
Faculty
AvarageMark
name
age
(strict inheritance)
Person
Student
ISA
45
Decomposition/Aggregation
(Mereology)
• Theory of parthood relations (Plato:
Stanford Encyclopedia of Philosophy)
• Also indicated as Part/Whole relation
• It is an important ontological relation,
since it is applicable both to instances
and concepts (but... hard to axiomatize)
• Inheritance: characteristics of relevant
parts are transmitted to the whole
– Color: body  car
– Power: engine  car
46
Decomposition / Aggregation
(PartOf )
Vehicle
Engine
Piston CarburetorHoodDoor
Body
PartOf is transitive
47
Building hierarchical structures
A hierarchy of concepts can be built in two ways:
- Top-down, when the less refined concepts are first
identified and then more refined concepts are progressively
identified
- Bottom-up, when you start identifying the most refined
concepts, and then you group them under more general
ones.
Hierarchies can be applied both to entities (objects, actors),
activities (processes, tasks, actions), and relationships
48
Universal Relation
Predication
• Identifies (HasA) the concepts that denote the
relevant charateristics of an entity: properties
• Associate the properties to the entities
Concepts
Attributes
C1 C2
C3
a4
a5
a3
a9
a9
a1
a8
Ex. Person: name, age, address(street, nr, postCode, city), tel
Invoice: nr, date, {item (lineNr, descr, cost, qty, lineTot) }, total
Universal Relation
Instantiation
On the ‘double nature’ of a concept:
• Intentional definition: a collection of
properties and constraints (e.g., a dog
has: name, owner, )
• Extensional definition: a set of instances
that satisfy the intentional definition (e.g., a
dog includes: fido, pluto, rex, ...)
49
50
Concepts - Instances
Instantiation
PersonStudent
Cat
ed
mary
miao
51
Relation between Concepts and
Instances
Concepts Instances
Persons
Students
denotes
denotes
containment
Person
Student
ISA
Other Universial Relations
• Membership, when a composite structure includes
a set of elements of the same kind (e.g.,
tennisPlayers in a tennisClub)
• Containment, among two composite structures,
when one includes the other (left-handed
TennisPlayers)
• Similarity, with a similarity degree k (typically:
k = 0 .. 1)
• Causality: a causes b (b isCausedBy a)
• Precedence (temporal): a precedes b (b follows a),
strict / loose
• Proximity (spatial): a proxTo b (symmetric)
52
53
Universal Relations Summary
• Generalization
/Specialization (ISA)
– Student ISA Person (A)
– Car ISA Vehicle (O)
– Frying ISA Cooking (P)
• Part/Whole (PartOf)
– Tail PartOf Dog
– Weel PartOf Car
– Seasoning PartOf
Cooking
• Predication (HasA)
– Person HasA Name
– Car HasA Color
– Hoven HasA
Temperature
• Similarity (SIM/k)
– Bird SIM/0.5 Airplane
– Pear SIM/0.7 Apple
– Tennis SIM/0.7 Squash
• Instantiation (InstOf)
– Pluto InstanceOf Dog
– MyAlfa InstanceOf Car
– TodayDinner InstanceOf Dining
54
Domain-dependent relations
• Defined between 2 (binary) or more (n-
ary) concepts
• Unlike Universal Relations, they assume
a meaning in a specific application
domain
• Relations valid both at concept and
instance level
– Frame hanging_on Wall
– Invoice issued_by provider
– Student attends Couse [john attends
informationSystems]
Conclusions
• Ontology engineering relies on
Conceptual Modeling principles
• Conceptualization is a basic human
activity, but here we need to make it explicit
and systematic
• An ontology
– is a socio-technical artefact, that needs a
collaboration practice for its construction and
evolution
– reflects a shared perception of an application
domain 55

More Related Content

Viewers also liked

Character Movie Ppt Version Sample
Character Movie Ppt Version SampleCharacter Movie Ppt Version Sample
Character Movie Ppt Version SampleAndrew Schwartz
 
Social Media Class Baarn - 151112
Social Media Class Baarn - 151112Social Media Class Baarn - 151112
Social Media Class Baarn - 151112Peter Wiegman
 
What is your product's social strategy?
What is your product's social strategy?What is your product's social strategy?
What is your product's social strategy?Jon Gatrell
 
Pädevuste rakendamine
Pädevuste rakendaminePädevuste rakendamine
Pädevuste rakendaminekiq
 
Christmas is for Cookies
Christmas is for CookiesChristmas is for Cookies
Christmas is for CookiesJon Gatrell
 
The future of the Adobe Flash platform
The future of the Adobe Flash platformThe future of the Adobe Flash platform
The future of the Adobe Flash platformMichael Chaize
 
WordPress and PHP - It Takes One to Know One
WordPress and PHP - It Takes One to Know OneWordPress and PHP - It Takes One to Know One
WordPress and PHP - It Takes One to Know OneLorelle VanFossen
 
The Phenomenon Of L A C R O S S E
The  Phenomenon  Of  L A C R O S S EThe  Phenomenon  Of  L A C R O S S E
The Phenomenon Of L A C R O S S Eguest9771ee
 
Fasting
FastingFasting
Fastingnonnon
 

Viewers also liked (18)

Mother The Only Truth In This World
Mother The Only Truth In This WorldMother The Only Truth In This World
Mother The Only Truth In This World
 
Character Movie Ppt Version Sample
Character Movie Ppt Version SampleCharacter Movie Ppt Version Sample
Character Movie Ppt Version Sample
 
Social Media Class Baarn - 151112
Social Media Class Baarn - 151112Social Media Class Baarn - 151112
Social Media Class Baarn - 151112
 
What is your product's social strategy?
What is your product's social strategy?What is your product's social strategy?
What is your product's social strategy?
 
Pädevuste rakendamine
Pädevuste rakendaminePädevuste rakendamine
Pädevuste rakendamine
 
PLC - Organization
PLC - OrganizationPLC - Organization
PLC - Organization
 
Christmas is for Cookies
Christmas is for CookiesChristmas is for Cookies
Christmas is for Cookies
 
Innovation manifesto v04
Innovation manifesto v04Innovation manifesto v04
Innovation manifesto v04
 
The future of the Adobe Flash platform
The future of the Adobe Flash platformThe future of the Adobe Flash platform
The future of the Adobe Flash platform
 
Rotary hardenberg
Rotary hardenbergRotary hardenberg
Rotary hardenberg
 
Amsterdam Music Ss3
Amsterdam Music Ss3Amsterdam Music Ss3
Amsterdam Music Ss3
 
Zavisimost
ZavisimostZavisimost
Zavisimost
 
WordPress and PHP - It Takes One to Know One
WordPress and PHP - It Takes One to Know OneWordPress and PHP - It Takes One to Know One
WordPress and PHP - It Takes One to Know One
 
Chebanova
ChebanovaChebanova
Chebanova
 
The Phenomenon Of L A C R O S S E
The  Phenomenon  Of  L A C R O S S EThe  Phenomenon  Of  L A C R O S S E
The Phenomenon Of L A C R O S S E
 
大家行05
大家行05大家行05
大家行05
 
Fasting
FastingFasting
Fasting
 
Lidia Pivovarova
Lidia PivovarovaLidia Pivovarova
Lidia Pivovarova
 

Similar to M2. conceptual modeling intro

Multiple representations talk, Middlesex University. February 23, 2018
Multiple representations talk, Middlesex University. February 23, 2018Multiple representations talk, Middlesex University. February 23, 2018
Multiple representations talk, Middlesex University. February 23, 2018University of Huddersfield
 
Extending the knowledge level of cognitive architectures with Conceptual Spac...
Extending the knowledge level of cognitive architectures with Conceptual Spac...Extending the knowledge level of cognitive architectures with Conceptual Spac...
Extending the knowledge level of cognitive architectures with Conceptual Spac...Antonio Lieto
 
Lecture 1 - 28-Mar-2016 - Architectural Concept development.ppt
Lecture 1 - 28-Mar-2016 - Architectural Concept development.pptLecture 1 - 28-Mar-2016 - Architectural Concept development.ppt
Lecture 1 - 28-Mar-2016 - Architectural Concept development.pptSelamMubarek
 
M1. sem web & ontology introd
M1. sem web & ontology introdM1. sem web & ontology introd
M1. sem web & ontology introdMichele Missikoff
 
Year 1 of research - presentation
Year 1 of research - presentationYear 1 of research - presentation
Year 1 of research - presentationserena pollastri
 
Data Driven Modeling Beyond Idealization
Data Driven Modeling Beyond IdealizationData Driven Modeling Beyond Idealization
Data Driven Modeling Beyond IdealizationVahid Moosavi
 
Multiple representations and visual mental imagery in artificial cognitive sy...
Multiple representations and visual mental imagery in artificial cognitive sy...Multiple representations and visual mental imagery in artificial cognitive sy...
Multiple representations and visual mental imagery in artificial cognitive sy...University of Huddersfield
 
Cloud Computing and VirtualizationSupplemental Lecture
Cloud Computing and VirtualizationSupplemental LectureCloud Computing and VirtualizationSupplemental Lecture
Cloud Computing and VirtualizationSupplemental LectureWilheminaRossi174
 
MDST 3705 2012-03-05 Databases to Visualization
MDST 3705 2012-03-05 Databases to VisualizationMDST 3705 2012-03-05 Databases to Visualization
MDST 3705 2012-03-05 Databases to VisualizationRafael Alvarado
 
Ph d course on formal ontology and conceptual modeling
Ph d course on formal ontology and conceptual modelingPh d course on formal ontology and conceptual modeling
Ph d course on formal ontology and conceptual modelingNicola Guarino
 
Lec 6 different theoretical approaches
Lec 6 different theoretical approachesLec 6 different theoretical approaches
Lec 6 different theoretical approachesMuhammad Muhyuddin
 
From digital to physical and back
From digital to physical and backFrom digital to physical and back
From digital to physical and backMirko Daneluzzo
 
Design Patterns for Interactive Graphics
Design Patterns for Interactive GraphicsDesign Patterns for Interactive Graphics
Design Patterns for Interactive GraphicsChristian Kohls
 
Interactive visualization and exploration of network data with gephi
Interactive visualization and exploration of network data with gephiInteractive visualization and exploration of network data with gephi
Interactive visualization and exploration of network data with gephiBernhard Rieder
 
Oomd2015
Oomd2015Oomd2015
Oomd2015ktosri
 
OOMD2015_KSP
OOMD2015_KSPOOMD2015_KSP
OOMD2015_KSPktosri
 
Creating new classes of objects with deep generative neural nets
Creating new classes of objects with deep generative neural netsCreating new classes of objects with deep generative neural nets
Creating new classes of objects with deep generative neural netsAkin Osman Kazakci
 

Similar to M2. conceptual modeling intro (20)

Multiple representations talk, Middlesex University. February 23, 2018
Multiple representations talk, Middlesex University. February 23, 2018Multiple representations talk, Middlesex University. February 23, 2018
Multiple representations talk, Middlesex University. February 23, 2018
 
Extending the knowledge level of cognitive architectures with Conceptual Spac...
Extending the knowledge level of cognitive architectures with Conceptual Spac...Extending the knowledge level of cognitive architectures with Conceptual Spac...
Extending the knowledge level of cognitive architectures with Conceptual Spac...
 
Narrative space
Narrative spaceNarrative space
Narrative space
 
Lecture 1 - 28-Mar-2016 - Architectural Concept development.ppt
Lecture 1 - 28-Mar-2016 - Architectural Concept development.pptLecture 1 - 28-Mar-2016 - Architectural Concept development.ppt
Lecture 1 - 28-Mar-2016 - Architectural Concept development.ppt
 
M1. sem web & ontology introd
M1. sem web & ontology introdM1. sem web & ontology introd
M1. sem web & ontology introd
 
Year 1 of research - presentation
Year 1 of research - presentationYear 1 of research - presentation
Year 1 of research - presentation
 
Data Driven Modeling Beyond Idealization
Data Driven Modeling Beyond IdealizationData Driven Modeling Beyond Idealization
Data Driven Modeling Beyond Idealization
 
Robert Gordon talk, 21 March 2018
Robert Gordon talk, 21 March 2018Robert Gordon talk, 21 March 2018
Robert Gordon talk, 21 March 2018
 
Multiple representations and visual mental imagery in artificial cognitive sy...
Multiple representations and visual mental imagery in artificial cognitive sy...Multiple representations and visual mental imagery in artificial cognitive sy...
Multiple representations and visual mental imagery in artificial cognitive sy...
 
Cloud Computing and VirtualizationSupplemental Lecture
Cloud Computing and VirtualizationSupplemental LectureCloud Computing and VirtualizationSupplemental Lecture
Cloud Computing and VirtualizationSupplemental Lecture
 
MDST 3705 2012-03-05 Databases to Visualization
MDST 3705 2012-03-05 Databases to VisualizationMDST 3705 2012-03-05 Databases to Visualization
MDST 3705 2012-03-05 Databases to Visualization
 
Ph d course on formal ontology and conceptual modeling
Ph d course on formal ontology and conceptual modelingPh d course on formal ontology and conceptual modeling
Ph d course on formal ontology and conceptual modeling
 
Lec 6 different theoretical approaches
Lec 6 different theoretical approachesLec 6 different theoretical approaches
Lec 6 different theoretical approaches
 
From digital to physical and back
From digital to physical and backFrom digital to physical and back
From digital to physical and back
 
Design Patterns for Interactive Graphics
Design Patterns for Interactive GraphicsDesign Patterns for Interactive Graphics
Design Patterns for Interactive Graphics
 
Interactive visualization and exploration of network data with gephi
Interactive visualization and exploration of network data with gephiInteractive visualization and exploration of network data with gephi
Interactive visualization and exploration of network data with gephi
 
EWIC talk - 07 June, 2018
EWIC talk - 07 June, 2018EWIC talk - 07 June, 2018
EWIC talk - 07 June, 2018
 
Oomd2015
Oomd2015Oomd2015
Oomd2015
 
OOMD2015_KSP
OOMD2015_KSPOOMD2015_KSP
OOMD2015_KSP
 
Creating new classes of objects with deep generative neural nets
Creating new classes of objects with deep generative neural netsCreating new classes of objects with deep generative neural nets
Creating new classes of objects with deep generative neural nets
 

More from Michele Missikoff

141022 ic3 k semanticsofinnovation missikoff
141022 ic3 k semanticsofinnovation missikoff141022 ic3 k semanticsofinnovation missikoff
141022 ic3 k semanticsofinnovation missikoffMichele Missikoff
 
140515 klagenf future of innovation
140515 klagenf future of innovation140515 klagenf future of innovation
140515 klagenf future of innovationMichele Missikoff
 
120619 cul knowledge based bus inno v03
120619 cul knowledge based bus inno v03120619 cul knowledge based bus inno v03
120619 cul knowledge based bus inno v03Michele Missikoff
 

More from Michele Missikoff (10)

6. open innov conclusions
6. open innov conclusions6. open innov conclusions
6. open innov conclusions
 
5. open innov ict-platf
5. open innov ict-platf5. open innov ict-platf
5. open innov ict-platf
 
4. open innov lifecycle
4. open innov lifecycle4. open innov lifecycle
4. open innov lifecycle
 
3. open innov organization
3. open innov organization3. open innov organization
3. open innov organization
 
2. open innov whatisit
2. open innov whatisit2. open innov whatisit
2. open innov whatisit
 
1. open innov framing
1. open innov framing1. open innov framing
1. open innov framing
 
141022 ic3 k semanticsofinnovation missikoff
141022 ic3 k semanticsofinnovation missikoff141022 ic3 k semanticsofinnovation missikoff
141022 ic3 k semanticsofinnovation missikoff
 
140515 klagenf future of innovation
140515 klagenf future of innovation140515 klagenf future of innovation
140515 klagenf future of innovation
 
120619 cul knowledge based bus inno v03
120619 cul knowledge based bus inno v03120619 cul knowledge based bus inno v03
120619 cul knowledge based bus inno v03
 
120626 gdansk c ai se2012-2
120626 gdansk c ai se2012-2120626 gdansk c ai se2012-2
120626 gdansk c ai se2012-2
 

M2. conceptual modeling intro

  • 1. 1 Conceptual Modelling An Introduction (Module M2) The 2013 International Conference on Collaboration Technologies and Systems (CTS 2013)
  • 2. 2 Module Layout • Conceptual modelling for ontology building – a key human activity – Modeling and representation – The nature of knowledge – Syntax and semantics: from conceptual to ontology modeling – Static vs dynamic modeling (entity vs process) – Key conceptual constructs (building bricks) • Collaborative ontology building • Languages, tools, and platforms • And ... some practical exercises
  • 3. 3 Perception and conceptualizion • Objects cannot be seen … they appear as a sum of phenomena associated to them (a table? shapes, substance, color, weight, volume, …) • Perception is an analytic activity (over individual phenomena) that is integrated afterward, to form the unity of the observed entity • From the observation to the conceptualization • Conceptualization: of entities, categories, relationships • Abstraction: selection of relevant aspects, it is fundamental in modelling the reality (too complex) • A Conceptual Model is a set of concepts well organized, on the base of principles independent of the domain
  • 4. 4 Modeling is a key activity Modeling is a key activity to understand / communicate a description of a given reality • when the modeled objects do not exist yet (e.g., in designing a complex artifact), • when the fragment of reality is not tangible (e.g., the organization of an enterprise), • Concerns general (e.g., a mock-up of a building) or specific (e.g., the electric circuit schema) aspects • Different models for the same complex entity (e.g., the human body) • Objectives of modeling: understanding, communicating, exchanging information, predict behaviors and future situations (states)
  • 5. 5 General Model Theory Three pillars of a General Model Theory (Philosopher Herbert Stachowiak, 1973): (1) Mimetism: Models are representative of “something”; (2) Reductionism: Models are reductive in the sense that they depict some but not all aspects of the given fragment of reality; (3) Pragmatism: Models are created for a purpose in the sense that a model is created at a given time, having a purpose in mind. Corollary: same reality at the same time may have different models
  • 6. 6 Different modeling methods • Concrete, e.g., a plastic model of a building • Figurative, e.g., a drawing of a car, of a road map • Narrative, e.g., a text describing a landscape • Schematic: schemes and diagrams that illustrate the vectorial forces in the structure of a bridge, the blue-print of an electric circuit • Mathematical: e.g., a system of equations that rigorously represents the air flowing in a wind tunnel; a Boolean expression representing a digital circuit
  • 7. 7 Conceptual Model Symbolic Modeling: describe the reality representing the relevant concepts and their relationships, starting from … • concepts, expressed by means of terms (words) and symbols – Figurative symbols: icons – Mathematical symbols: letters, operators, … – Diagrammatic symbols : boxes, ovals, arrows Engineer tractorfixes
  • 8. Language • Needed to communicate, among people, between people and computers • Used to create Conceptual Models • Natural Language: spoken by humans • Artificial Language: conceived ad-hoc Language, to create • Sentences complex structures expressing concepts, composed by – Atomic elements: symbols and terminology – Complex elements (... also sentences) 8
  • 9. 9 Language - Syntax • Symbols & Terms and composition rules for sentences (i.e., complex structures) engineer tractorfixes engineer tractor fixes Syntactically correct structure Syntactically incorrect structure Relation Object Actor
  • 10. 10 Abstract Formal Syntax (sketch) Unary Concept - uc: a (actor), o (object), p (process) Conceptual (binary) Relation: R Conceptual Structure: cs uc = a | o | p cs = uc | cs R cs Recursively, nested structures cs = cs R (cs R2 cs)
  • 11. Syntax - Example uc1 = Engineer uc2 = Tractor R = fixes (uc1 R uc2) (Engineer fixes Tractor) The triple pattern: (Subject Rel Object) Nested structures: cs R (cs R2 cs) (Engineer fixes (Tractor ownedBy Father)) 11 engineer tractor fixes
  • 12. 12 Language - Semantics • Symbols and sentences (Syntactically correct ) • From the meaning of symbols & terms to the meaning of sentences engineer tractoreats engineertractor fixes engineer tractorblabla
  • 13. What’s in a Concept? Concept, is formed by • Description (Intention) – Purely descriptive, including its properties and relationships – If rigorous, it indicates necessary and sufficient conditions • Population (Extension) – The collection of all individuals (instances) of the concept – Each individual needs to satisfy the intentional description of the concept 13
  • 14. 14 Formal Semantics (sketch) Given a set of conceptual structures: K = {csj} Given a domain of interpetation: D = {ei} We define a semantic function S: S : K  2D {csi} {ei} K D S(Intentional level) (Extensional level)
  • 15. Denotation 15 (Engineer Fixes Tractor) Engineer Fixes Tractor K = {csj} D = {ei} S Denotation is compositional
  • 16. 16 Standard FO Semantics Semantics given by standard First Order Interpetation theory: Interpretation domain DIInterpretation function I Individuals John Mary Concepts Lawyer Doctor Vehicle Relationships hasChild owns (Lawyer AND Doctor)(by I. Horrocks)
  • 18. Synchronism vs Diachronism • Static modelling (Synchronic) – What we see when we take a picture – Entities, Relationships, Properties – Then, we automatically apply a conceptualization process – We recognise Categories, similarities and differences, ... and we attach names to them • Dynamic modelling (Diachronic) – We understand how the reality: is evolving, may further evolve, and we can influence it 18
  • 19. 19 Conceptual Modeling Static View An exercise of conceptual modeling (static knowledge) • Observe the reality, identify the relevant elements • Create a schematic description by means of a suitable terminology and notation (conceptual model) • Static Conceptual Modeling, includes Terms denoting: – Entities populating the observed reality – Properties of entities (e.g., color, size, …), and – Relationships among entities A First Collaborative Conceptual Model
  • 20. 20 A bedroom (V. VanGogh) Exercise: Build a terminological model: entities, properties, relationships (keep it, later we will ‘formalise’ the model)
  • 21. A Collaborative Exercise Your First Shared Conceptual Model • Take pen ‘n paper • Given a term, be ready to define a concept, by using 140 characters: a Semantic Tweet • Then, be ready to experience the rich diversity of term interpretations • Hence, we will collaborate and progressively move towards a shared view of the concept • Later, you will build a simplified ontology by using – SKOS and a N3 like formalization 21
  • 22. 22 Conceptual Modeling Dynamic View • Reality is continuously evolving • Objects change state (position, form, color, …) due to specific activities • Activity: constructing, distroying, fixing, traveling, cooking, washing, painting, … • Identify, for each activity: – Objects, that are modified / created / … – Actors, that perform the activities – Tools, used in performing the activities
  • 24. 24 Exemples of Dynamic specification Process: cooking – In: egg – Actor: cook – Out: omelet Process: forging – In: steel – Actor: blacksmith – Out: gear Process: designing – In: requirement, skills – Actor: architect – Out: design
  • 25. 25 Symbolism in dynamic representation The great capacity of P. Bruegel (16th cnetury) has been to organize in a single scene almost 120 sayings drawn from the popular wisdom that define a symbolic universe: that of a reversed world. Inspired by “Adagia”, a literary work of Erasmus from Rotterdam
  • 27. 27 Some Flemish Sayings • “Carring the light to the day with a hamper” (losing time with useless occupations) [49] • “Filling up the well when the calf has drowned” (repair a situation when is too late) [57] • “learning to bow to travel the world” (you need to be flexible) [60]
  • 28. 28 A systematic approach Conceptual Modeling Static Dimension
  • 29. 29 Concepts and instances Conceptual modeling refers (mainly) to concepts, instances are modeled by data (numbers, strings, URIs). With concept we mean a mental abstraction built (in general) starting from the reality. A concept defines the characteristics (properties) common to a set of coherent objects. With instance we mean any element of the extension of a concept. It is an individual object with identity, described by its relevant characteristics (i.e., all its properties are evaluated). In understanding the reality, the first primary notions are: concepts and instances
  • 30. 30 Conceptualization Classification Conceptualization & Instantiation DOG animal that barks ------ Fido ------ ------ Instance Descr. individual concept Symbolic Concept Descr. Mental world Real world Digital world
  • 31. 31 Semiotics Theory of Signs • In linguistic theories (Semiotics) – Relation between symbols (of the language) and concepts (denotation) • In formal theories – Relation between concepts models and instances (instatiation) • The ontological-semiotic closure (C. K. Ogden triangle1) Symbol Concept Instance (Referent) (1The Meaning of Meaning: A Study of the Influence of Language upon Thought and the Science of Symbolism, 1923)
  • 32. 32 The pipe (Surrealiste painter René.Magritte) [this is NOT a pipe]
  • 34. 34 Building a Concept Conceptual modeling aims at creating a description of a fragment of realty, through the definition of some concepts, with their correlations A concept (entity): defined by using a terminological expression: • Label (concept name) – Person • Properties – Name, age, address (attributes – dataProperties) – Friends, company (associations – references) • Relations with other concepts – Married (symmetrical) john married mary  mary married john)
  • 35. 35 Entities and Relationships • Entities (Concepts) – Tangible: Student, Person, Cat, Bike, Chair – Intangible: Course, Film, Sale, Story – Abstract: Natural number, Algorithm, Philosophy, Luck • Relations (conceptual) – Follows(Student,Course) – Owner(Person,Bike) – xxx(Person,Student) – yyy(Bike,Weel)
  • 36. 36 Attributes - Associations Cat: • Name, Age, Owner Person: • Name, Age, Phone, FCode, Weight Student: • Name, Age, Phone, FC, Weight, University, Faculty, AverageMark Attribute: printable data Association: entity-id (reference)
  • 37. 37 Conceptual Kinds OPAL – Object, Process, Actor modeling Language • Object – Passive entity, whose state may change by means of the effect of a process • Actor – Active entity, capable of performing a process • Process – Activities performed by actors, aimed at modifying entities (a kind may depends on the situation)
  • 38. Conceptual Relations • O, P, A are unary concepts (= entities) • They specify what exist in a given (fragment of the) reality • The next step is to define their mutual relationships: binary, n-ary relations We have: • Universal relations: they are valid in any possible observable domain • Domain specific relations: make sense only in specific contexts 38
  • 39. Universal Relation: Refinement • It is a vertical relation • Associate a concept to a more refined one that is: – Better specified, – Enriched in its description Two golden hierarchical relations • Specialization: IsA • Decomposition: PartOf 39
  • 40. Specialization • Given a concept, refine its description • Increase the precision of the description • More precise classification of individuals • Produces a Taxonomy Ex. Student IsA Person; Teenager IsA Person • Inverse Generalization 40
  • 41. 41 Specialization Given a concept, it is specialized by applying rigorous mechanisms: • Extension: introducing additional properties – Student extends person, with university, faculty, avergeMark • Restriction: restrincting the range (i.e., legal values) of one or more properties – Teenager restrincts person on age (with values between 13 and 19)
  • 42. 42 Attributes & Associations • Cat: Name, Age, Owner • Person: Name, Age, Phone, FC, Weight • Student: Name, Age, Phone, FC, Weight, University, Faculty, AverageMark N A P FC W U F AM
  • 43. 43 Taxonomy (ISA) Vehicle Public Vehicle Plane TrainBikeCar Private Vehicle Example: The IsA hierarchy of Vehicle Key feature of a Taxonomy: property inheritance (in case with restricted range)
  • 45. 45 Decomposition/Aggregation (Mereology) • Theory of parthood relations (Plato: Stanford Encyclopedia of Philosophy) • Also indicated as Part/Whole relation • It is an important ontological relation, since it is applicable both to instances and concepts (but... hard to axiomatize) • Inheritance: characteristics of relevant parts are transmitted to the whole – Color: body  car – Power: engine  car
  • 46. 46 Decomposition / Aggregation (PartOf ) Vehicle Engine Piston CarburetorHoodDoor Body PartOf is transitive
  • 47. 47 Building hierarchical structures A hierarchy of concepts can be built in two ways: - Top-down, when the less refined concepts are first identified and then more refined concepts are progressively identified - Bottom-up, when you start identifying the most refined concepts, and then you group them under more general ones. Hierarchies can be applied both to entities (objects, actors), activities (processes, tasks, actions), and relationships
  • 48. 48 Universal Relation Predication • Identifies (HasA) the concepts that denote the relevant charateristics of an entity: properties • Associate the properties to the entities Concepts Attributes C1 C2 C3 a4 a5 a3 a9 a9 a1 a8 Ex. Person: name, age, address(street, nr, postCode, city), tel Invoice: nr, date, {item (lineNr, descr, cost, qty, lineTot) }, total
  • 49. Universal Relation Instantiation On the ‘double nature’ of a concept: • Intentional definition: a collection of properties and constraints (e.g., a dog has: name, owner, ) • Extensional definition: a set of instances that satisfy the intentional definition (e.g., a dog includes: fido, pluto, rex, ...) 49
  • 51. 51 Relation between Concepts and Instances Concepts Instances Persons Students denotes denotes containment Person Student ISA
  • 52. Other Universial Relations • Membership, when a composite structure includes a set of elements of the same kind (e.g., tennisPlayers in a tennisClub) • Containment, among two composite structures, when one includes the other (left-handed TennisPlayers) • Similarity, with a similarity degree k (typically: k = 0 .. 1) • Causality: a causes b (b isCausedBy a) • Precedence (temporal): a precedes b (b follows a), strict / loose • Proximity (spatial): a proxTo b (symmetric) 52
  • 53. 53 Universal Relations Summary • Generalization /Specialization (ISA) – Student ISA Person (A) – Car ISA Vehicle (O) – Frying ISA Cooking (P) • Part/Whole (PartOf) – Tail PartOf Dog – Weel PartOf Car – Seasoning PartOf Cooking • Predication (HasA) – Person HasA Name – Car HasA Color – Hoven HasA Temperature • Similarity (SIM/k) – Bird SIM/0.5 Airplane – Pear SIM/0.7 Apple – Tennis SIM/0.7 Squash • Instantiation (InstOf) – Pluto InstanceOf Dog – MyAlfa InstanceOf Car – TodayDinner InstanceOf Dining
  • 54. 54 Domain-dependent relations • Defined between 2 (binary) or more (n- ary) concepts • Unlike Universal Relations, they assume a meaning in a specific application domain • Relations valid both at concept and instance level – Frame hanging_on Wall – Invoice issued_by provider – Student attends Couse [john attends informationSystems]
  • 55. Conclusions • Ontology engineering relies on Conceptual Modeling principles • Conceptualization is a basic human activity, but here we need to make it explicit and systematic • An ontology – is a socio-technical artefact, that needs a collaboration practice for its construction and evolution – reflects a shared perception of an application domain 55