SIMS 202, Marti Hearst

MetaData, Objects, MetaData, Objects, Relations: Similarities and Relations: Similarities and

DifferencesDifferencesandand

Cognitive Aspects of Cognitive Aspects of CategorizationCategorization

SIMS 202, Lecture 10SIMS 202, Lecture 10

Fall, 1997Fall, 1997

Prof. Marti HearstProf. Marti Hearst

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Today: Four Related Today: Four Related QuestionsQuestions Why are we learning about Why are we learning about

metadatametadata database designdatabase design object oriented systems?object oriented systems?

How are these related to one another?How are these related to one another? How are these different from one another?How are these different from one another? Why is it hard to define/design these things?Why is it hard to define/design these things?

What cognitive science isWhat cognitive science is What cogsci tells us about categorizationWhat cogsci tells us about categorization

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Why are we learning about Why are we learning about metadata, database design metadata, database design and OO systems?and OO systems? Information Information organizationorganization These are all ways to handle complexity, These are all ways to handle complexity,

by imposing structure and order on messy by imposing structure and order on messy datadata

Each is useful in a different wayEach is useful in a different way

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How is the Relational Model How is the Relational Model Related to the Object Related to the Object Oriented Model?Oriented Model? Let’s start with a re-description of objects.Let’s start with a re-description of objects.

Objects are instantiated classes Objects are instantiated classes Classes are have attributesClasses are have attributes Attribute is the TYPE of information (kind of like a Attribute is the TYPE of information (kind of like a

data type in a programming language)data type in a programming language) Attributes have VALUES that fit their TYPEAttributes have VALUES that fit their TYPE

attribute TYPE: integer, VALUE: 9attribute TYPE: integer, VALUE: 9 attribute TYPE: suit, VALUE: club, heart, spade, attribute TYPE: suit, VALUE: club, heart, spade,

diamonddiamond attribute TYPE: name, VALUE: Juanita, Dekai, Lauraattribute TYPE: name, VALUE: Juanita, Dekai, Laura

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Attributes vs. ClassesAttributes vs. Classes

How do we make this distinction?How do we make this distinction? Say we are clothing manufacturers.Say we are clothing manufacturers.

Fur is a classFur is a class Animal is an attributeAnimal is an attribute

Say we are naturalists.Say we are naturalists. Animal is a classAnimal is a class Fur is an attributeFur is an attribute

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Garment Makers vs. Garment Makers vs. NaturalistsNaturalists

Class Class FurFur AnimalAnimal: fox, rabbit, sable: fox, rabbit, sable Color: red, black, whiteColor: red, black, white Texture: silky, thick, coarseTexture: silky, thick, coarse Garment_type: coat, stole, hatGarment_type: coat, stole, hat

Class Class AnimalAnimal Outer_Covering:Outer_Covering: fur fur, skin, scales, skin, scales Number_of_limbs: 4, 6, 8Number_of_limbs: 4, 6, 8 Circulatory_System: cold_blooded, hot_bloodedCirculatory_System: cold_blooded, hot_blooded

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Attributes vs. ClassesAttributes vs. Classes

This example showed that one user’s This example showed that one user’s classes are another user’s attributes.classes are another user’s attributes.

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Let’s Revisit RelationsLet’s Revisit Relations

Table contains rows of dataTable contains rows of data The data has attribute typesThe data has attribute types Can perform certain operations:Can perform certain operations:

select (pick out rows)select (pick out rows) project (pick out columns)project (pick out columns) join (match up 2 or more tables’ data)join (match up 2 or more tables’ data) add (add a new row)add (add a new row) delete (delete a row)delete (delete a row) update (change a value within a row)update (change a value within a row)

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Relations vs. ObjectsRelations vs. Objects ER Diagram:ER Diagram:

Entity = ClassEntity = Class Attribute = AttributeAttribute = Attribute Relation ~ MethodRelation ~ Method

Relational TableRelational Table Table ~ ClassTable ~ Class Row ~ Instantiated Object of ClassRow ~ Instantiated Object of Class Column = Attribute TYPEColumn = Attribute TYPE Value in (row,column) = Attribute VALUEValue in (row,column) = Attribute VALUE Name ~ Primary KeyName ~ Primary Key

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Relations vs. ObjectsRelations vs. Objects There are no Class-specific Methods in the Relational There are no Class-specific Methods in the Relational

ModelModel There are general-purpose methods on all data:There are general-purpose methods on all data:

update (change), select, delete, add, join, projectupdate (change), select, delete, add, join, project The Relation in the ER diagram indicates how to set up the tables The Relation in the ER diagram indicates how to set up the tables

so they can be easily joinedso they can be easily joined

There is no unique Object Id (Address) in the Relational There is no unique Object Id (Address) in the Relational Model Model

Can only access an “instantiated object” by combinations of its Can only access an “instantiated object” by combinations of its “attribute values”“attribute values”

Normalization can cause the object representation to be spread Normalization can cause the object representation to be spread out across several tablesout across several tables

No encapsulated data in the Relational ModelNo encapsulated data in the Relational Model

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Garment Maker vs. Garment Garment Maker vs. Garment MakerMaker

Class Class FurFur Animal: fox, rabbit, sableAnimal: fox, rabbit, sable Color: Color: red, black, whitered, black, white Texture: silky, thick, coarseTexture: silky, thick, coarse Garment_type: coat, stole, hatGarment_type: coat, stole, hat

Class GarmentClass Garment Material: Material: furfur, cotton, wool, cotton, wool Color: Color: red, black, brown, white, bluered, black, brown, white, blue Garment_Type: coat, stole, hatGarment_Type: coat, stole, hat

Problem: match color to materialProblem: match color to material

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Nesting Attributes and Nesting Attributes and ClassesClasses

Class GarmentClass Garment Material: Material:

Class FurClass Fur Animal: fox, rabbit, sableAnimal: fox, rabbit, sable Color: red, black, whiteColor: red, black, white Texture: silky, thick, coarseTexture: silky, thick, coarse

Class CottonClass Cotton Color: red, blue, white, brown, blackColor: red, blue, white, brown, black Thread_Count: 100, 200Thread_Count: 100, 200

Garment_type: stole, coat, hat, t-shirtGarment_type: stole, coat, hat, t-shirt Attributes often must be nestedAttributes often must be nested Alternative: two subclasses of GarmentAlternative: two subclasses of Garment

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Normalization and NestingNormalization and Nesting In the Relational Model, Normalization In the Relational Model, Normalization

“flattens out” the Nesting“flattens out” the Nesting Why?Why?

Normalization makes certain kinds of Normalization makes certain kinds of access more efficient, less likely to mess access more efficient, less likely to mess up updatesup updates

Why isn’t this confusing in the OO model?Why isn’t this confusing in the OO model? Key: Relational and OO used in different Key: Relational and OO used in different

situationssituations

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Relations vs. ObjectsRelations vs. Objects

ObjectsObjects Nomads, doing their own thing, rugged Nomads, doing their own thing, rugged

individualists, use one-at-a-timeindividualists, use one-at-a-time Example: program running on a printerExample: program running on a printer

Relations: Relations: Packed into apartments, lots and lots of items Packed into apartments, lots and lots of items

all being lined up in one place for easy all being lined up in one place for easy comparisoncomparison

Queries: Find all X that have Y and are > ZQueries: Find all X that have Y and are > Z Example: Everyone’s phone bill in the U.S.Example: Everyone’s phone bill in the U.S.

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Relations vs. ObjectsRelations vs. Objects

Can you have a table of objects?Can you have a table of objects? Can you have an object that has a table?Can you have an object that has a table?

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Metadata vs. ObjectsMetadata vs. Objects

MetaData like the Dublin Core is simpleMetaData like the Dublin Core is simple Much like the name, attribute parts of a ClassMuch like the name, attribute parts of a Class No methodsNo methods

MetaData like AACRII is messierMetaData like AACRII is messier A bunch of rules about how to deal with the A bunch of rules about how to deal with the

exceptionsexceptions Law deals quite a bit with exceptionsLaw deals quite a bit with exceptions Computer Science tries as hard as possible to Computer Science tries as hard as possible to

abstract away or ignore exceptionsabstract away or ignore exceptions

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Why are we learning about Why are we learning about this old library stuff?this old library stuff?

The computer science tradition is good at The computer science tradition is good at abstracting away details.abstracting away details.

The computer science tradition is The computer science tradition is notnot good at good at describing detail and convoluted exceptions.describing detail and convoluted exceptions.

The library tradition can teach us something The library tradition can teach us something useful about how to describe complex data.useful about how to describe complex data.

Think about how these bibliographic Think about how these bibliographic examples can be applied to other domains examples can be applied to other domains (maybe a test question!!!)(maybe a test question!!!)

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Metadata vs. Relational Metadata vs. Relational ModelModel Relational model makes use of MetadataRelational model makes use of Metadata The description of the database is often called a The description of the database is often called a

SchemaSchema The Schema is a kind of Metadata descriptionThe Schema is a kind of Metadata description Main differences:Main differences:

Exceptions not handled well in the relational model Exceptions not handled well in the relational model eithereither

Relational model focus is on the system designRelational model focus is on the system design Metadata focus is on the description of the data, Metadata focus is on the description of the data,

independent of a computer systemindependent of a computer system

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Fresh Topic: Why is this Stuff Fresh Topic: Why is this Stuff Hard?Hard?

These are all variations on CategorizationThese are all variations on Categorization Categorization is an important topic in:Categorization is an important topic in:

PhilosophyPhilosophy Language/LinguisticsLanguage/Linguistics PsychologyPsychology

How does the human mind do How does the human mind do categorization?categorization?

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What’s In a Sentence?What’s In a Sentence?““A sentence is not a verbal snapshot or movie of A sentence is not a verbal snapshot or movie of

an event. In framing an utterance, you have to an event. In framing an utterance, you have to abstract away from everything you know, or can abstract away from everything you know, or can picture, about a situation, and present a picture, about a situation, and present a schematic version which conveys the essentials. schematic version which conveys the essentials. In terms of grammatical marking, there is not In terms of grammatical marking, there is not enough time in the speech situation for any enough time in the speech situation for any language to allow for the marking of everything language to allow for the marking of everything which could possibly be significant to the which could possibly be significant to the message.”message.”

Dan Slobin, in Dan Slobin, in Language Acquisition: The state of the art, Language Acquisition: The state of the art, 19821982

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Approximating MeaningApproximating Meaning

Defining attributesDefining attributes A weak approximation to meanings and A weak approximation to meanings and

conceptsconcepts Defining methodsDefining methods

A weak approximation to how these A weak approximation to how these meanings interact and changemeanings interact and change

Necessary and Sufficient ConditionsNecessary and Sufficient Conditions Example: A prime number is an integer Example: A prime number is an integer

divisible only by itself and 1.divisible only by itself and 1.

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Properties of CategorizationProperties of Categorization

Family Resemblance: Family Resemblance: Members of a category may be related to Members of a category may be related to

one another without all members having one another without all members having any properties in common that define the any properties in common that define the category.category.

Centrality:Centrality: Some members of a category may be Some members of a category may be

“better examples” of that category than “better examples” of that category than others.others.

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CentralityCentrality A category: Prime NumbersA category: Prime Numbers

Definition: An integer divisible only by itself Definition: An integer divisible only by itself and 1and 1

Examples: 1, 2, 3, 5, 7, 11, 13, 17, …Examples: 1, 2, 3, 5, 7, 11, 13, 17, … A very clear-cut category. Or is it?A very clear-cut category. Or is it? Can one number be “more prime” than Can one number be “more prime” than

another?another? CENTRALITY: some members of a category CENTRALITY: some members of a category

may be “better examples” than othersmay be “better examples” than others

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Definition of GameDefinition of Game Famous example by WittgensteinFamous example by Wittgenstein Classic categories: clear boundaries defined by common Classic categories: clear boundaries defined by common

propertiesproperties Counterexample: GameCounterexample: Game No common properties shared by all gamesNo common properties shared by all games

card games, ball games, Olympic games, children’s gamescard games, ball games, Olympic games, children’s games competition: ring-around-the-rosiecompetition: ring-around-the-rosie skill: dice gamesskill: dice games luck: chessluck: chess

No fixed boundary; can be extended to new gamesNo fixed boundary; can be extended to new games video gamesvideo games

Alternative: Concepts related by Family ResemblancesAlternative: Concepts related by Family Resemblances

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Characteristic FeaturesCharacteristic Features

Perceived degree of category membership Perceived degree of category membership has to do with which features define the has to do with which features define the category.category.

Members usually do not have ALL the Members usually do not have ALL the necessary features, but have some subset.necessary features, but have some subset.

Those members that have more of the central Those members that have more of the central features are seen as more central members.features are seen as more central members.

People have conceptions of typical members.People have conceptions of typical members.

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Properties of CategorizationProperties of Categorization

Basic-level Categories:Basic-level Categories: Categories are organized into a hierarchy from Categories are organized into a hierarchy from

the most general to the most specific, but the the most general to the most specific, but the level that is most cognitively basic is “in the level that is most cognitively basic is “in the middle” of the hierarchymiddle” of the hierarchy

Basic-level Primacy:Basic-level Primacy: Basic-level categories are functionally primary Basic-level categories are functionally primary

with respect to factors including ease of with respect to factors including ease of cognitive processing (learning, reasoning, cognitive processing (learning, reasoning, recognition, etc).recognition, etc).

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Levels of AbstractionLevels of Abstraction Brown 1958, 65, Berlin et al., 1972, 73Brown 1958, 65, Berlin et al., 1972, 73 Folk biology:Folk biology:

unique beginner: plant, animalunique beginner: plant, animal life form: tree, bush, flowerlife form: tree, bush, flower generic name: pine, oak, maple, elmgeneric name: pine, oak, maple, elm specific name: Ponderosa pine, white pinespecific name: Ponderosa pine, white pine varietal name: western Ponderosa pinevarietal name: western Ponderosa pine

No overlap between levelsNo overlap between levels Level 3 is basicLevel 3 is basic Level 3 corresponds to genusLevel 3 corresponds to genus

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Characteristics of Basic-level Characteristics of Basic-level CategoriesCategories

LanguageLanguage People name things more readily at basic levelPeople name things more readily at basic level Name learned earliest in childhoodName learned earliest in childhood Languages have simpler names at basic levelLanguages have simpler names at basic level Sounds like the “real name” Sounds like the “real name” Name used more frequentlyName used more frequently

Strange to call a dime a coin, a metal objectStrange to call a dime a coin, a metal object

Names used in neutral contextNames used in neutral context There’s a dog on the porch.There’s a dog on the porch. There’s a terrier on the porch.There’s a terrier on the porch.

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Characteristics of Basic-level Characteristics of Basic-level CategoriesCategories ConceptsConcepts

Things perceived more wholistically at basic level Things perceived more wholistically at basic level (rather than by parts)(rather than by parts)

No difference in how people interact with the No difference in how people interact with the concept between basic and more specific levelsconcept between basic and more specific levels

Things are remembered more readily at basic Things are remembered more readily at basic levellevel

Folk biology categories correspond accurately to Folk biology categories correspond accurately to scientific biological categories only at the basic scientific biological categories only at the basic levellevel

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Superordinate and Superordinate and Subordinate LevelsSubordinate Levels

SUPERORDINATE animal furnitureSUPERORDINATE animal furniture

BASIC LEVEL dog chairBASIC LEVEL dog chair

SUBORDINATE terrier rockerSUBORDINATE terrier rocker

Children take longer to learn Children take longer to learn superordinatesuperordinate

Superordinate not associated with mental Superordinate not associated with mental images or motor actionsimages or motor actions

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Typicality and Characteristic Typicality and Characteristic FeaturesFeatures Some categories have clear boundaries, but have Some categories have clear boundaries, but have

graded membershipgraded membership What is a good example of a bird?What is a good example of a bird? Examples from language:Examples from language:

A robin is a bird. A robin is a bird. A chicken is a bird.A chicken is a bird. A bat is a bird.A bat is a bird.

Takes longer for people to say the second is true and Takes longer for people to say the second is true and the third is falsethe third is false

Features characterize the categoryFeatures characterize the category How many typical features does the object possess?How many typical features does the object possess?

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Characteristic FeaturesCharacteristic Features

Is a cat on a mat at cat?Is a cat on a mat at cat? Is a dead cat a cat?Is a dead cat a cat? Is a photo of a cat a cat?Is a photo of a cat a cat? Is a cat with three legs a cat?Is a cat with three legs a cat? Is a cat that barks a cat?Is a cat that barks a cat? Is a cat with a dog’s brain a cat?Is a cat with a dog’s brain a cat? Is a cat with every cell replaced by a dog’s cells a Is a cat with every cell replaced by a dog’s cells a

cat?cat?

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PolysemyPolysemy

Most words have more than one senseMost words have more than one sense that dog has floppy earsthat dog has floppy ears good ear for jazzgood ear for jazz three ears of cornthree ears of corn

Homonymy: same word, different meaningHomonymy: same word, different meaning Polysemy: different senses of same wordPolysemy: different senses of same word

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Category Structure and Category Structure and PolysemyPolysemy

Category membership is determined by Category membership is determined by shared subsets of featuresshared subsets of features

Different senses of a word reflect Different senses of a word reflect differences in which attributes are shareddifferences in which attributes are shared

This is reflected in language by polysemyThis is reflected in language by polysemy related meaning, but slightly differentrelated meaning, but slightly different Example: bankExample: bank

the building, the institution, the notion of where the building, the institution, the notion of where money is storedmoney is stored

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MetonymyMetonymy

Use one aspect of something to stand for Use one aspect of something to stand for the wholethe whole The building stands for the institution of the The building stands for the institution of the

bank.bank. Newscast: “The White House relased new Newscast: “The White House relased new

figures today.”figures today.” Waitperson: “The ham sandwich spilled his Waitperson: “The ham sandwich spilled his

drink.”drink.”

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SynonymySynonymy

Different ways of expressing related conceptsDifferent ways of expressing related concepts ExamplesExamples

cat, feline, Siamese catcat, feline, Siamese cat Overlaps with basic, subordinate levelOverlaps with basic, subordinate level Synonyms are almost never “true”Synonyms are almost never “true”

used in different contextsused in different contexts have different implicationshave different implications This is a point of contention.This is a point of contention.

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ThesauriThesauri

Polysemy: same word, different senses of Polysemy: same word, different senses of meaningmeaning slightly different concepts expressed slightly different concepts expressed

similarlysimilarly Synonyms: different words, related Synonyms: different words, related

senses of meaningssenses of meanings different ways to express similar conceptsdifferent ways to express similar concepts

Thesauri help draw all these togetherThesauri help draw all these together

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SummarySummary

Processes of categorization underlie many of the Processes of categorization underlie many of the issues having to do with information organizationissues having to do with information organization

Categorization is messier than our computer Categorization is messier than our computer systems would likesystems would like

Human categories have graded membership, Human categories have graded membership, consisting of family resemblances.consisting of family resemblances.

Family resemblance is expressed in part by which Family resemblance is expressed in part by which subset of features are sharedsubset of features are shared

It is also determined by underlying understandings It is also determined by underlying understandings of the world that do not get represented in most of the world that do not get represented in most systemssystems