Introduction to Conceptual Modeling

(1)

Introduction to Conceptual Modeling

Gabriela P. Henning

INTEC (Universidad Nacional del Litoral - CONICET)

3000 - Santa Fe, Argentina

1

•

Motivating questions

•

Knowledge representation and reasoning

–

Critical issues

•

Knowledge engineering

•

Emerging paradigms in the 70

’

and 80

’

•

Current trends in knowledge representation:

Conceptual modeling today

(2)

Questions

•

What is a model? Are there different types of models?

•

What

is

conceptual

modeling

?

•

Why do we need explicit models in Computer

Science?

•

Which are the differences among data, information

and knowledge?

•

Different

types

of

information

/

knowledge

?

–

Extensional vs.

intensional

information

–

Declarative vs. procedural knowledge

–

Particular vs. general information

3

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Questions

•

What

is

conceptual

modeling

?

•

Science?

•

and knowledge?

•

Different

types

of

information

/

knowledge

?

–

Extensional vs.

intensional

information

–

4

(3)

Introduction to models

5

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Human beings have used

symbols and representations

to model their environment

since the beginning of

civilization

Models

•

A model is

always an abstraction of reality

•

Model

is a widely used term

• The term model

can be interpreted in different ways

by

distinct communities

• There are models of physical things

(models of entities

and systems having actual, real existence) and

models

of insubstancial

(man-made) systems, such as:

– Conceptual models

– Causal models

– Data models

– Statistical models

– Business process models

– Architectural models

– …..

(4)

Questions

•

What is a model? Are there different types of models?

•

What

is

conceptual

modeling

?

•

Science?

•

and knowledge?

•

Different

types

of

information

/

knowledge

?

–

Extensional vs.

intensional

information

–

7

Modelado Conceptual: Aplicaciones en Ingeniería y en la Gestión Eficiente de Organizaciones– Gabriela Henning – INTEC (CONICET-UNL), FICH-FIQ

Conceptual Modeling

According to John Mylopoulos (1992)

the discipline of

conceptual modeling is:

“the activity of formally describing

some aspects of the

physical

and social

world around us for purposes of

understanding

and communication….”

“Conceptual modeling supports structuring and inferential

facilities that are phychological grounded. After all, the

descriptions that arise from conceptual modelling activities are

intended to be used by humans, not machines…”

“The adequacy of a conceptual modelling notation

rests on

its contribution to the construction of models of reality that

promote a common understanding of that reality among their

human users.”

8

(5)

Conceptual Modeling

The specification of a conceptual model can be viewed

as a

description of a

given subject

domain

. This is

why

conceptual models are

also

known as domain

models

.

The aim of a conceptual model is to

explicitly express

the meaning of terms and concepts

used by domain

experts to discuss the problem, and to

find the correct

relationships

between different concepts.

A conceptual model attempts to

clarify the meaning of

various, usually

ambiguous terms

, and ensure that

problems with different interpretations of these terms

and concepts cannot occur.

9

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Conceptual Model

A conceptual model must be explicitly

chosen to be

independent of design, implementation concerns

(e.g.,

concurrency issues) or

technological choices

(e.g. data

storage technology), that should influence the particular

applications or telematic systems based on such model.

Conceptual specifications are to be used to support

understanding

(

learning

),

problem-solving

, and

communication

among stakeholders about a given

subject domain.

Once a sufficient level of understanding and agreement

about a domain is reached, then the conceptual specifica_

tion becomes a basis for subsequent development of

applications in the domain.

(6)

Conceptual Modeling

11

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Concept

(Conceptualization)

Thing

(reality)

represents

refers to

abstracts

Symbol

(language)

Ullmann’s triangle:

the relations

between a

thing

in reality, its

conceptualization

and a

symbolic representation

of this

conceptualization.

Note de dotted line between

language and reality. It indicates

that the relation between them is

always established by the

intermediation of a certain

conceptualization

Distinction between a model and its interpretation

12

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Conceptualization

Model

Modeling

Language

Model

Specification

representedBy

interpretedAs

usedTo

Compose

usedTo

Compose

instanceOf

Guizzardi, 2005

(7)

A

conceptualization

is the set of concepts used to

articulate abstractions of state of affairs in a given

domain.

The abstraction of a portion of reality articulated

according to a domain conceptualization is termed here a

model

.

The representation of a model in terms of a language is

called a

model specification

, or simply

specification

.

The language used for the creation of a specification is

called a

modeling language

.

13

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Distinction between a model and its interpretation

A

language

can be seen as determining all possible

specifications

(i.e. all grammatically valid specifications)

that can be constructed using that language.

A

conceptualization

can be seen as determining all

possible

models

(standing for the state of affairs) which

are admissible in such domain.

Guizzardi defends the precedence of real-word concepts

over formal ones and implementational issues in the

design/adoption of conceptual modeling languages. He

points out the importance of the so-called

domain

appropriateness

and

comprehensibility appropriate_

ness

of languages.

(8)

The

domain appropriateness

of a language is a

measure of its suitability to model the phenomena in a

given domain. In other words, it can be seen as the

truthfulness of the language to a given domain or reality.

The

comprehensibility appropriateness

of a language

refers to how easy if for a user of the language to

recognize what that language’s constructs mean in terms

of domain concepts. Moreover, it refers to how easy is to

understand, communicate and reason with the

specifications produced in such language.

Both

domain appropriateness

and

comprehensibility

appropriateness

are properties of the represents

relationship in Ulmann’s triangle.

15

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Distinction between a model and its interpretation

Questions

•

What

is

conceptual

modeling

?

•

Science?

•

and knowledge?

•

Different

types

of

information

/

knowledge

?

–

Extensional vs.

intensional

information

–

16

(9)

Early Scientists´ Thoughts…..

17

Why do we need explicit models?

…..

Need to explicitly

represent knowledge

(10)

• To build systems exhibiting some kind of intelligent behavior.

Many of the problems that computers are expected to solve

require extensive and explicit knowledge about the world of study:

objects, properties, categories and relations between objects;

situations, events, states and time; causes and effects, etc.

• To capture the relevant aspects of some world, so the model can

serve as a point of agreement among members of a group, and to

communicate that common view to newcomers.

• Because explicit models are useful in rationalizing and supporting

information system development.

• To represent requirements to be considered during the early

phases of system development.

• As a foundation for the integration of different system

applications.

19

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Why do we need explicit models?

…..

Questions

•

What

is

conceptual

modeling

?

•

Science?

•

and knowledge?

•

Different

types

of

information

/

knowledge

?

–

Extensional vs.

intensional

information

–

20

(11)

Data, information, knowledge…..

21

Data

Signals

Information

Knowledge

Wisdom/

Intelligence

The Information

Pyramid / The

Knowledge Hierarchy

Filtering

Collecting

Summarizing

Organizing

Analyzing

Synthesizing

Decision Making

•

Data

– “Raw signals” in digital form. Many times obtained by

processing signals from sensors, bar code readers, etc.

– A collection of symbols without any meaning beyond its

existence.

PO30478

500C

•

Information

– A set of data which have been given a meaning by formulating

relations between the data elements in a given context.

– Meaning attached to data

¼

Understandable by humans and

computers

S O S

(12)

•

Knowledge

– Constitutes a collection of information with the intention of a

certain kind of use,

– May attach purpose and competence to information

– New knowledge may be created from existing knowledge by

using inference processes.

– Has potential to generate action

If (Reactor.temperature – Reactor.setpoint)

>

10 Then

→

Reactor.status = RunawayAlert

If Reactor.status = RunawayAlert

→

start

ShutdownProcedure

– Understanding or reasoning refers to an analytic and cognitive

process, which takes some knowledge as its input to infer new

knowledge as its output by some kind of “interpolation”

23

•

Intelligence – Intelligent systems

– Computational systems that are capable to solve problems or do

things that require intelligence when done by humans.

– Many of nowadays intelligent systems use an explicitly

represented store of knowledge to reason by considering goals,

the environment, other computational agents, etc.

– There are many particular traits, behaviors or capabilities that

researchers would like an intelligent system to display, such as:

deduction, induction, reasoning, problem solving, planning,

learning, knowledge representation, natural language

processing, motion and manipulation, perception, social

intelligence, etc..

(13)

Questions

•

What

is

conceptual

modeling

?

•

Science?

•

and knowledge?

•

Different

types

of

information

/

knowledge

?

–

Extensional vs. intentional information

–

Declarative vs. procedural knowledge

–

25

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Extensional vs. Intentional Information

• An

extensional definition

of information would define

things or concepts by listing everything that falls under such

definition.

Examples

: An extensional definition of “mother” would be a listing

of all women that are mothers in the world. Similarly, the

extensional definition of “bachelor” would be a listing of all the

unmarried men in the world.

• An

intentional definition

of information would define the

meaning of a term by specifying all the properties required

to come to such definition, that is, the necessary and

sufficient conditions for belonging to the set being defined.

Examples:

An intensional definition of “mother” is “woman with

one or more children”. An intentional definition of "bachelor" is

"unmarried man." Unmarried man is a necessary and sufficient

property that defines a bachelor.

(14)

Extensional vs. Intentional Information

To distinguish between extension and intension, let’s analyze

a predicate, like the English word “red”. Two meanings can

be given to it:

a) The set of all red things – this is called the

extension of

the predicate

b) An abstract entity which in some sense characterizes

what

it means

to be red. It refers to the notion of redness, which

may or may not be true of a given object – this is called the

intention of the predicate

.

In many philosophical theories

the intention of a predicate

is identified with an abstract function

which applies to

possible worlds and assigns to any such world a set of

extensional objects, i.e. the intention of “red” would assign to

each possible world a set of red things.

27

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Declarative vs. Procedural Knowledge

Declarative representations

have knowledge in a format

that may be manipulated, decomposed and analyzed by

various reasoning tools (i.e., reasoners). Declarative

representations are associated with “

know that” or “know

what

”.

Clear advantages of a declarative representation are:

a) the ability to use knowledge in ways that the system designer

did not foresee, and

b) the possibility of reusing the representation for different

purposes.

Procedural representations

encode knowledge in a way

that is linked to how to achieve a particular result. Procedural

knowledge, also known as

imperative knowledge

, is the

knowledge put into effect in the execution of some task.

Procedural representations are associated with “

know how

”.

28

(15)

Particular/Specific vs. General knowledge

Specific knowledge

can be regarded as knowledge

that is costly to be transferred among different agents. It

can be seen as case-specific or situation dependent

knowledge.

General knowledge

can be regarded as knowledge that

is inexpensive to transmit due to its generality. It can be

seen as knowledge that transcends or goes beyond

specific situations.

It is always desirable to extract general knowledge out of

specific one. One possible mechanism can be inductive

generalization. It proceeds from a premise about a

sample to a conclusion about the whole population.

29

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•

Motivating questions

•

–

Critical issues

•

’

and 80

’

•

Conceptual modeling today

(16)

Knowledge Representation & Reasoning

•

Knowledge

– Description of the world of interest that is usable by machines

to draw conclusions about such world

– The psychological result of cognitive processes, i.e., of

perception, learning and reasoning.

– That which is understood or can be understood

– “The wing wherewith we fly to heaven” (Shakespeare)

– Knowledge differs from data or information in that new

knowledge may be created from existing knowledge using

inference processes.

•

Reasoning

– Way of “thinking” that is coherent and logical

– Logical inference process

– The process of creating new knowledge from existing one

31

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Knowledge Representation & Reasoning

•

is an area of

artificial intelligence whose main goal is to represent

knowledge in a manner that facilitates inferencing (i.e.

drawing conclusions) from knowledge. It analyzes how to

formally think - how to use a symbol system to represent a

domain of discourse, along with functions that allow

inference.

•

Representation of knowledge

Description of the world of interest that is usable by

machines to draw conclusions about such world

•

Reasoning based on explicitly represented knowledge

Working hypothesis:

Working hypothesis: Knowledge of the world can always

be articulated and used as needed.

32

(17)

Some knowledge representation issues

• What form is the knowledge to be expressed?

• How can a reasoning mechanism generate new

knowledge?

• How can knowledge be used to influence a system’s

behavior?

• How is incomplete, inconsistent or noisy information

properly handled?

• How can practical results be obtained when

reasoning is intractable due to the complexity of the

domain?

• …..

33

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KR&R – Knowledge Representation

•

How information can be appropriately encoded and

utilized in computational models of cognition?

•

Two primary areas of activity:

–

Designing formats for expressing information

• Mostly "general purpose" representation

languages

(e.g.,

first order logic)

–

Encoding knowledge (

knowledge engineering

)

• Mostly identifying and

describing conceptual

vocabularies

(ontologies)

•

Declarative representations are the focus of KR technology

– Explicit knowledge that is

domain

-

specific but task

-

-independent.

(18)

•

Computational methods for creating new knowledge and

information from existing knowledge

–

Very general methods: e.g. modus ponens from first order

Very general methods:

logic

–

Task

-

specific methods:

specific methods: algorithms for planning, scheduling,

diagnosis, constraint satisfaction, etc.

–

Methods for managing reasoning: e.g., hybrid reasoning,

Methods for managing reasoning:

parallel processing, etc.

•

Analysis of the reasoning capabilities

– Examination of properties such as soundness, completeness,

complexity, etc.

•

Methods for creating explanations from the obtained

reasoning results, e.g. explanation of the line of reasoning.

35

KR&R – Reasoning

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Knowledge representation & reasoning

•

Expressiveness vs. tractability (effective reasoning) trade

-

-off

off

– How to express what we know?

– How to reason with what we express?

•

Every representation ignores

“

something

”

about the world

¼

When modeling the real world,

KRs

are always

imperfect, i.e.

KRs

are surrogates for the real world

•

Given a KR, there are two questions to ask:

– Semantics -- For

what

is it a surrogate?

– Fidelity -- How accurate is it?

36

(19)

•

–

Critical issues

•

’

and 80

’

•

Current trends in knowledge representation:

Introduction to Conceptual Modeling - Outline

37

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Knowledge Engineering – KE

•

Early definition:

KE

is an

engineering discipline

that

involves integrating knowledge into computer

systems

in order to solve complex problems, normally

requiring a high level of human expertise (

Feigenbaum &

McCorduck, 1983

).

• Nowadays,

KE refers to the building, maintaining

and development of knowledge

-

based systems

that

can be used in

many computer science domains

,

such as artificial intelligence, database development,

data mining, intelligent systems, decision support

systems and geographic information systems, among

others.

(20)

Knowledge Engineering

•

Can be defined as the process of

–

defining the scope of a knowledge

-

based system,

–

eliciting, capturing,

–

structuring,

–

formalizing,

–

validating and verifying,

–

operationalizing

information and knowledge involved in a

knowledge

knowledge-

-intensive problem domain, in order

intensive problem domain, in order

to construct a program/system that can perform

a difficult task/set of tasks adequately.

39

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Knowledge Engineering

•

Can be defined as the process of

–

defining the scope of a knowledge

-

based system,

–

eliciting, capturing,

–

structuring,

–

formalizing,

–

validating and verifying,

–

operationalizing

information and knowledge involved in a

knowledge

knowledge-

-intensive problem domain, in order

intensive problem domain, in order

to construct a program/system that can perform

a difficult task/set of tasks adequately.

40

This is not a “sharp” list. These

phases generally overlap, the

whole process might be iterative,

and many challenges could appear

(21)

Problems in Knowledge Engineering

• Complex information and knowledge are difficult to

observe/elicit, make explicit, comprehend and capture

• Experts and other sources generally differ on their views

• Multiple knowledge sources which coexists have

intrinsic different information “structures”:

– textbooks

– graphical representations

– heuristics

– Skills

• Knowledge is valuable and often outlives a particular

implementation. Knowledge is not static

¼

Need for

k

nowledge management and maintenance tools

• Errors in a knowledge-base can cause serious problems

41

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Issues in knowledge engineering

There are:

•

Different types of knowledge

, and that influences the right

approach and technique that should be used for the type of

knowledge being required.

•

Distinct ways of representing knowledge (different

languages and formalisms)

, which can aid the acquisition,

validation, and re-use of knowledge

•

Different types of experts and expertise

, such that

methods should be chosen appropriately.

•

Distinct goals

drive the development of intelligent/expert

systems.

(22)

•

–

Critical issues

•

’

and 80

’

•

43

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A Short History of Knowledge Systems

1965

1975

1985

1995

general-purpose

search engines

(GPS)

first-generation

rule-based systems

(MYCIN, XCON)

emergence of

structured methods

(early KADS)

mature KE

methodologies

(CommonKADS)

=> from art to “somehow” discipline =>

Ontologies

2000-2010

44

(23)

A Short History of Knowledge-based Systems

45

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Early history of knowledge representation: 60’ & 70’

•

Origins:

– Problem solving work at MIT, CMU, (Stanford)

– Driven by natural language understanding

•

Many Ad

-

hoc formalisms

•

“

Procedural

”

vs.

“

Declarative

”

knowledge controversy

•

Informal semantics

– Problems:

• How do we assign meaning to things?

(24)

Emerging paradigms in the 70’ & 80’

•

Predicate logics

•

Semantic nets

•

Frames

• Production rules

47

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Emerging paradigms in the 70’ & 80’

•

Predicate logics

•

Semantic nets

–

Unstructured node

-

link graphs

–

No semantics (minimum) to support

interpretation

–

No axioms to support reasoning capabilities

•

Frames

•

Production rules

48

(25)

Semantic nets – Semantic memory motivation

Quillian, 1966

• Understand the structure of human memory, and its use in

language understanding

• What sort of representational format can permit the

“meanings” of words to be stored, so that humanlike use of

these meanings is possible?

• Psychological evidence that memory uses associative links

in understanding words.

• Claim that people use same memory structure for a variety

of tasks

49

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Semantic nets

• Directed, labeled graphs used to represent concepts

and the relationships between them.

• Arcs define binary relationships that hold between the

objects that define the nodes.

(26)

Semantic nets

• The ISA and AKO relationships

were sometimes used to:

– Link a class and its superclass

– Link a class with its instances

• Some links are inherited along ISA

paths (e.g. “has part” relationship )

• The semantics can range from very

formal (Krypton), to formal

(KL-ONE), and informal. It depends on

the implementation.

51

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Semantic nets - Reification

• Non-binary relationships can be represented by

“turning the relationship into an object”:

• Logicians call this issue “reification”

– Reify v: Consider the abstract object “v” to be real

Give

Peter

Hans

Chemistry

book

Recipient

Giver

Object

52

(27)

Semantic nets – Classes and instances

• Many semantic nets

distinguish:

– Nodes representing classes

and instances

– The “subclass” relation from

the “instance-of” link

53

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Emerging paradigms in the 70’ & 80’

•

Predicate logics

•

Semantic nets

•

Frames

–

Structured semantic nets

–

Object

-

oriented description

–

Prototypes

–

Class

-

subclass taxonomies

(28)

Motivations for frame-based representations

• Minsky’s original motivations and observations: Famous analysis

of a birthday party.

• An attempt to model of human cognition (the structure of

knowledge memory) and some foundations for “common sense”

reasoning (e.g. the capability to represent things like a room, an

animal, etc.).

• Memory is full of

prototypical

situations, richly interconnected. A

frame-based representation is organized around

prototypes

.

• Semantic networks evolved into frames. Frames have a less

shallow structure than semantic networks.

• A frame may contain information about the components of the

concept being described, links to other concepts, as well as

procedural information on how the frame can be accessed and

change over time.

55

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Frames

•

A frame is similar to the notion of object in OOP, but has

more metadata

,

and a primitive notion of behavior.

•

A

frame

has a set of

slots

or properties

•

A

slot

represents a relation to another

frame

(or to a

value)

•

A slot has one or more

facets

•

A

facet

represents some

aspect

of the relation

•

Some facets have procedural capabilities, behaving as

demons

•

In some systems, the slots themselves are instances of

frames. In others, slots may contain methods.

•

Frame systems support

inheritance

. Issue: Simple vs.

Multiple inheritance

56

(29)

Frames

• A

slot

in a frame holds more than a value or a set of values.

•

Facets

participate in the specification of slots. Facets may

include:

– Current fillers (e.g., values)

– Default fillers

– Cardinality: minimum and/or maximum number of fillers

– type restriction on fillers (valuetype or valueclass: usually

expressed as another frame object)

– constraints on the inheritance mechanisms (inheritance roles)

– Demons (attached procedures) that are triggered when

something changes in the slot values (if-added, if-removed,

etc.)

– Salience measure (for inference mechanisms)

– Attached constraints or axioms

57

Modelado Conceptual: Aplicaciones en Ingeniería y en la Gestión Eficiente de Organizaciones– Gabriela Henning – INTEC (CONICET-UNL), FICH-FIQ

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Frames

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Modelado Conceptual: Aplicaciones en Ingeniería y en la Gestión Eficiente de Organizaciones– Gabriela Henning – INTEC (CONICET-UNL), FICH-FIQ

Frames

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From frames to description logic

•

There is a family of frame

-

like representation

systems with a

formal semantics

formal semantics: e.g. KL

: e.g. KL

-

ONE,

LOOM, et.

•

An additional thing that can be done with these

systems is

automatic

classification

classification:

:

–

Finding the right place in a hierarchy of objects

(taxonomy) for a new description.

•

There is a need to keep the language simple so as

to ensure that all inferences can be done in

polynomial time.

–

Ensuring tractability of inference

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Modelado Conceptual: Aplicaciones en Ingeniería y en la Gestión Eficiente de Organizaciones– Gabriela Henning – INTEC (CONICET-UNL), FICH-FIQ

Emerging paradigms in the 70’ & 80’

•

Predicate logics

•

Semantic nets

•

Frames

• Production rules

– Situation-action rules:

IF (warning-light on) THEN (turn-off unit)

– If-then inference rules:

IF (warning-light on) THEN (reactor overheating),

IF (warning-light on) THEN (reactor overheating) 0.95)

– Hybrid procedural-declarative representation

(32)

•

–

Critical issues

•

’

and 80

’

•

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Modelado Conceptual: Aplicaciones en Ingeniería y en la Gestión Eficiente de Organizaciones– Gabriela Henning – INTEC (CONICET-UNL), FICH-FIQ

Knowledge representation in the 00’s

•

Web

-

based systems

•

Driven by new classes of applications (e.g. e

-

-commerce, information retrieval on the Web,

commerce, information retrieval on the Web,

Web services, etc.)

•

Incorporation into traditional applications

•

Support to Software Engineering, collaborative

design process, requirements engineering

•

Support for information integration processes

•

Business process representation – Support of

business process reengineering

•

Ontologies!!

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References

• Brachman, R. J. The future of knowledge representation, Proceedings of AAAI-90,

1084 –1092, 1990.

• Davis, R.; Shrobe H.; Szolovitz P. What is a knowledge representation. AI Magazine

14, 17–33, 1993.

• Guizzardi, G. Ontological Foundations for Structural Conceptual Models. CTIT PhD

Thesis Series, No. 05-74, Universiteit Twente, Enschede, The Netherlands, 2005

• Minsky, M. A Framework for representing knowledge. In: Brachman, R.J.; Levesque,

H. (Eds.) Readings in Knowledge Representation. Morgan Kaufmann, San Mateo,

California, 1985.

• Mylopoulos, J. Conceptual Modeling and Telos. In: Loucopoulos, P. and Zicari, R.

(Eds), Conceptual Modeling, Databases and CASE, Chapter 2, 49-68, Wiley, 1992.

• Mylopoulos, J. Conceptual Modeling Information Modeling in the Time of the

Revolution, Information Systems 23 (3-4), June 1998.

• Olivé, A. Conceptual Modeling of Information Systems, 2007.

• Woods, W. A. What´s in a link: Foundations for semantic networks. In: Bobrow, D.G.,

Collins A. M. (Eds.), Representation and Understanding: Studies in Cognitive

Science, Academic Press, New York, 35-82, 1985.

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