Introduction to Conceptual Modelinginfofich.unl.edu.ar/upload/a0432aa9b79b781f0e82555bf22f...5 Conceptual Modeling The specification of a conceptual model can be viewed as a description

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Introduction to Conceptual Modeling

Gabriela P. Henning

INTEC (Universidad Nacional del Litoral - CONICET) 3000 - Santa Fe, Argentina

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•• Motivating questionsMotivating questions

•• Knowledge representation and reasoning Knowledge representation and reasoning ––Critical issuesCritical issues

•• Knowledge engineeringKnowledge engineering

•• Emerging paradigms in the 70Emerging paradigms in the 70’’ and 80and 80’’

•• Current trends in knowledge representation: Current trends in knowledge representation: Conceptual modeling todayConceptual modeling today

Introduction to Conceptual Modeling - Outline

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

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Questions

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

•• WhatWhat isis conceptual conceptual modelingmodeling??•• Why do we need explicit models in Computer Why do we need explicit models in Computer

Science?Science?

•• Which are the differences among data, information Which are the differences among data, information and knowledge?and knowledge?

•• DifferentDifferent typestypes ofof informationinformation//knowledgeknowledge??–– Extensional vs. Extensional vs. intensionalintensional informationinformation–– Declarative vs. procedural knowledgeDeclarative vs. procedural knowledge–– Particular vs. general informationParticular vs. general information

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Questions



Science?Science?



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Introduction to models

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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 modelsof insubstancial (man-made) systems, such as: – Conceptual models– Causal models– Data models– Statistical models– Business process models– Architectural models– …..

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Questions



Science?Science?



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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.”

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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 ofvarious, usually ambiguous terms, and ensure that problems with different interpretations of these terms and concepts cannot occur.

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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 supportunderstanding (learning), problem-solving, andcommunication among stakeholders about a givensubject 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.

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Conceptual Modeling

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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

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Conceptualization

Model

ModelingLanguage

ModelSpecification

representedBy

representedBy

interpretedAs

interpretedAs

usedToCompose

usedToCompose

instanceOfinstanceOf

Guizzardi, 2005

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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.

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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.

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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 representsrelationship in Ulmann’s triangle.

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Questions



Science?Science?



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Early Scientists´ Thoughts…..

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Why do we need explicit models? …..

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Need to explicitlyrepresent knowledge

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• 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.

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Why do we need explicit models? …..

Questions



Science?Science?



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Data, information, knowledge…..

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Data

Signals

Information

Knowledge

Wisdom/Intelligence

The Information Pyramid / The Knowledge Hierarchy

FilteringCollecting

SummarizingOrganizing

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.PO30478500C

• 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 Understandable by humans and

computersS O S

PO30478, P1159, 20, 30-04566291-3, 03/11/10, …..

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• 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”

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• 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 thatresearchers 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..

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Questions



Science?Science?


•• DifferentDifferent typestypes ofof informationinformation//knowledgeknowledge??–– Extensional vs. intentional informationExtensional vs. intentional information–– Declarative vs. procedural knowledgeDeclarative vs. procedural knowledge–– Particular vs. general informationParticular vs. general information

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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.

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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 predicateb) 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.

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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 designerdid 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. Proceduralknowledge, also known as imperative knowledge, is the knowledge put into effect in the execution of some task. Procedural representations are associated with “know how”.

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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.

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Knowledge Representation & Reasoning

•• KnowledgeKnowledge– 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.

•• ReasoningReasoning– Way of “thinking” that is coherent and logical– Logical inference process– The process of creating new knowledge from existing one

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Knowledge Representation & Reasoning

•• Knowledge representation and reasoning Knowledge representation and 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 knowledgeRepresentation of knowledgeDescription of the world of interest that is usable by machines to draw conclusions about such world

•• Reasoning based on explicitly represented knowledgeReasoning based on explicitly represented knowledgeWorking hypothesis: Working hypothesis: Knowledge of the world can always be articulated and used as needed.

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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?

• …..

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KR&R – Knowledge Representation

•• How information can be appropriately encoded and How information can be appropriately encoded and utilized in computational models of cognition?utilized in computational models of cognition?

•• Two primary areas of activity:Two primary areas of activity:–– Designing formats for expressing informationDesigning formats for expressing information

• Mostly "general purpose" representation languages (e.g., first order logic)

–– Encoding knowledge (Encoding knowledge (knowledge engineeringknowledge engineering))• Mostly identifying and describing conceptual

vocabularies (ontologies)

•• Declarative representations are the focus of KR technologyDeclarative representations are the focus of KR technology– Explicit knowledge that is domaindomain--specific but taskspecific but task--

independent. independent. Separating ““that/whatthat/what”” from ““howhow””..

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•• Computational methods for creating new knowledge and Computational methods for creating new knowledge and information from existing knowledge information from existing knowledge –– Very general methods: Very general methods: e.g. modus ponens from first order

logic–– TaskTask--specific methods:specific methods: algorithms for planning, scheduling,

diagnosis, constraint satisfaction, etc.–– Methods for managing reasoning:Methods for managing reasoning: e.g., hybrid reasoning,

parallel processing, etc.

•• Analysis of the reasoning capabilitiesAnalysis of the reasoning capabilities– Examination of properties such as soundness, completeness,

complexity, etc.

•• Methods for creating explanations from the obtained Methods for creating explanations from the obtained reasoning results, e.g. explanation of the line of reasoning.reasoning results, e.g. explanation of the line of reasoning.

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KR&R – Reasoning


Knowledge representation & reasoning

•• Expressiveness vs. tractability (effective reasoning) tradeExpressiveness vs. tractability (effective reasoning) trade--offoff– How to express what we know?

– How to reason with what we express?

•• Every representation ignores Every representation ignores ““somethingsomething”” about the world about the world When modeling the real world, When modeling the real world, KRsKRs are always are always

imperfect, i.e. imperfect, i.e. KRsKRs are surrogates for the real worldare surrogates for the real world

•• Given a KR, there are two questions to ask:Given a KR, there are two questions to ask:– Semantics -- For what is it a surrogate?

– Fidelity -- How accurate is it?

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Knowledge Engineering – KE

•• Early definition:Early definition: KEKE is an engineering disciplineengineering discipline thatinvolves integrating knowledge into computer involves integrating knowledge into computer systemssystems in order to solve complex problems, normally requiring a high level of human expertise (Feigenbaum & McCorduck, 1983).

• Nowadays, KE refers to the building, maintaining KE refers to the building, maintaining and development of knowledgeand development of knowledge--based systemsbased systems that can be used in many computer science domainsmany computer science domains, such as artificial intelligence, database development, data mining, intelligent systems, decision support systems and geographic information systems, among others.

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Knowledge Engineering

•• Can be defined as the process ofCan be defined as the process of–– defining the scope of a knowledgedefining the scope of a knowledge--based system,based system,–– eliciting, capturing,eliciting, capturing,–– structuring,structuring,–– formalizing,formalizing,–– validating and verifying,validating and verifying,–– operationalizingoperationalizing

information and knowledge involved in a information and knowledge involved in a knowledgeknowledge--intensive problem domain, in order intensive problem domain, in order to construct a program/system that can perform to construct a program/system that can perform a difficult task/set of tasks adequately. a difficult task/set of tasks adequately.

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Knowledge Engineering

•• Can be defined as the process ofCan be defined as the process of–– defining the scope of a knowledgedefining the scope of a knowledge--based system,based system,–– eliciting, capturing,eliciting, capturing,–– structuring,structuring,–– formalizing,formalizing,–– validating and verifying,validating and verifying,–– operationalizingoperationalizing

information and knowledge involved in a information and knowledge involved in a knowledgeknowledge--intensive problem domain, in order intensive problem domain, in order to construct a program/system that can perform to construct a program/system that can perform a difficult task/set of tasks adequately. a difficult task/set of tasks adequately.

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This is not a “sharp” list. These phases generally overlap, the whole process might be iterative, and many challenges could appear


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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 Need for Need for kknowledge management and maintenance toolsnowledge management and maintenance tools

• Errors in a knowledge-base can cause serious problems41


Issues in knowledge engineering

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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/expertsystems.

• ……


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A Short History of Knowledge Systems

1965 19851975 1995

general-purpose search engines

(GPS)

first-generationrule-based systems

(MYCIN, XCON)

emergence ofstructured methods

(early KADS)

mature KEmethodologies

(CommonKADS)

=> from art to “somehow” discipline =>

Ontologies

2000-2010

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A Short History of Knowledge-based Systems

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Early history of knowledge representation: 60’ & 70’

•• Origins:Origins:– Problem solving work at MIT, CMU, (Stanford)

– Driven by natural language understanding

•• Many AdMany Ad--hoc formalismshoc formalisms

•• ““ProceduralProcedural”” vs. vs. ““DeclarativeDeclarative”” knowledge controversyknowledge controversy

•• Informal semanticsInformal semantics– Problems:

• How do we assign meaning to things?

• How/when can we say that a computer understands?

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Emerging paradigms in the 70’ & 80’

•• Predicate logicsPredicate logics

•• Semantic netsSemantic nets

•• FramesFrames

• Production rules

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•• Semantic netsSemantic nets–– Unstructured nodeUnstructured node--link graphslink graphs

–– No semantics (minimum) to support No semantics (minimum) to support interpretationinterpretation

–– No axioms to support reasoning capabilitiesNo axioms to support reasoning capabilities

•• FramesFrames

•• Production rulesProduction rules48


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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

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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.

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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.

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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

RecipientRecipient

GiverGiver

ObjectObject

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Semantic nets – Classes and instances

• Many semantic nets distinguish:– Nodes representing classes

and instances

– The “subclass” relation from the “instance-of” link

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•• FramesFrames–– Structured semantic netsStructured semantic nets–– ObjectObject--oriented descriptionoriented description–– PrototypesPrototypes–– ClassClass--subclass taxonomiessubclass taxonomies

• Production rules

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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.

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Frames

•• A frame is similar to the notion of object in OOP, but has A frame is similar to the notion of object in OOP, but has more metadatamore metadata, , and a primitive notion of behavior. and a primitive notion of behavior.

•• A A frameframe has a set of has a set of slots slots or propertiesor properties•• A A slot slot represents a relation to another represents a relation to another frameframe (or to a (or to a

value)value)•• A slot has one or more A slot has one or more facets facets • A facetfacet represents some aspectaspect of the relation• Some facets have procedural capabilities, behaving as

demons demons • In some systems, the slots themselves are instances of

frames. In others, slots may contain methods.•• Frame systems support Frame systems support inheritanceinheritance. Issue: Simple vs. . Issue: Simple vs.

Multiple inheritanceMultiple inheritance56


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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

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Frames

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Frames

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Frames

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From frames to description logic

•• There is a family of frameThere is a family of frame--like representation like representation systems with a systems with a formal semanticsformal semantics: e.g. KL: e.g. KL--ONE, ONE, LOOM, et.LOOM, et.

•• An additional thing that can be done with these An additional thing that can be done with these systems is systems is automaticautomatic classificationclassification: : –– Finding the right place in a hierarchy of objects Finding the right place in a hierarchy of objects

(taxonomy) for a new description.(taxonomy) for a new description.

•• There is a need to keep the language simple so as There is a need to keep the language simple so as to ensure that all inferences can be done in to ensure that all inferences can be done in polynomial time. polynomial time. –– Ensuring tractability of inferenceEnsuring tractability of inference

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•• FramesFrames

• 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

– Basis for first generation of expert systems

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Knowledge representation in the 00’s

•• WebWeb--based systemsbased systems•• Driven by new classes of applications (e.g. eDriven by new classes of applications (e.g. e--

commerce, information retrieval on the Web, commerce, information retrieval on the Web, Web services, etc.)Web services, etc.)

•• Incorporation into traditional applicationsIncorporation into traditional applications•• Support to Software Engineering, collaborative Support to Software Engineering, collaborative

design process, requirements engineeringdesign process, requirements engineering•• Support for information integration processesSupport 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 PhDThesis 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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Documents

Introduction to Conceptual Modelinginfofich.unl.edu.ar/upload/a0432aa9b79b781f0e82555bf22f...5 Conceptual Modeling The specification of a conceptual model can be viewed as a description