1. SIMS 202, Marti Hearst MetaData, Objects, Relations: Similarities and Differences and Cognitive Aspects of Categorization SIMS 202, Lecture 10 Fall, 1997 Prof. Marti Hearst

2. UCB SIMS 202 Today: Four Related Questions Why are we learning about Why are we learning about metadata metadata database design database 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 is What cognitive science is What cogsci tells us about categorization What cogsci tells us about categorization

3. UCB SIMS 202 Why are we learning about metadata, database design and OO systems? Information organization Information organization These are all ways to handle complexity, by imposing structure and order on messy data These are all ways to handle complexity, by imposing structure and order on messy data Each is useful in a different way Each is useful in a different way

4. UCB SIMS 202 How is the Relational Model Related to the Object 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 attributes Classes are have attributes Attribute is the TYPE of information (kind of like a data type in a programming language) Attribute is the TYPE of information (kind of like a data type in a programming language) Attributes have VALUES that fit their TYPE Attributes have VALUES that fit their TYPE attribute TYPE: integer, VALUE: 9 attribute TYPE: integer, VALUE: 9 attribute TYPE: suit, VALUE: club, heart, spade, diamond attribute TYPE: suit, VALUE: club, heart, spade, diamond attribute TYPE: name, VALUE: Juanita, Dekai, Laura attribute TYPE: name, VALUE: Juanita, Dekai, Laura

5. UCB SIMS 202 Attributes 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 class Fur is a class Animal is an attribute Animal is an attribute Say we are naturalists. Say we are naturalists. Animal is a class Animal is a class Fur is an attribute Fur is an attribute

6. UCB SIMS 202 Garment Makers vs. Naturalists Class Fur Class Fur Animal: fox, rabbit, sable Animal: fox, rabbit, sable Color: red, black, white Color: red, black, white Texture: silky, thick, coarse Texture: silky, thick, coarse Garment_type: coat, stole, hat Garment_type: coat, stole, hat Class Animal Class Animal Outer_Covering: fur, skin, scales Outer_Covering: fur, skin, scales Number_of_limbs: 4, 6, 8 Number_of_limbs: 4, 6, 8 Circulatory_System: cold_blooded, hot_blooded Circulatory_System: cold_blooded, hot_blooded

7. UCB SIMS 202 Attributes vs. Classes This example showed that one user’s classes are another user’s attributes. This example showed that one user’s classes are another user’s attributes.

8. UCB SIMS 202 Let’s Revisit Relations Table contains rows of data Table contains rows of data The data has attribute types The 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)

9. UCB SIMS 202 Relations vs. Objects ER Diagram: ER Diagram: Entity = Class Entity = Class Attribute = Attribute Attribute = Attribute Relation ~ Method Relation ~ Method Relational Table Relational Table Table ~ Class Table ~ Class Row ~ Instantiated Object of Class Row ~ Instantiated Object of Class Column = Attribute TYPE Column = Attribute TYPE Value in (row,column) = Attribute VALUE Value in (row,column) = Attribute VALUE Name ~ Primary Key Name ~ Primary Key

10. UCB SIMS 202 Relations vs. Objects There are no Class-specific Methods in the Relational Model There are no Class-specific Methods in the Relational Model There are general-purpose methods on all data: There are general-purpose methods on all data: update (change), select, delete, add, join, project update (change), select, delete, add, join, project The Relation in the ER diagram indicates how to set up the tables so they can be easily joined The Relation in the ER diagram indicates how to set up the tables so they can be easily joined There is no unique Object Id (Address) in the Relational Model There is no unique Object Id (Address) in the Relational Model Can only access an “instantiated object” by combinations of its “attribute values” Can only access an “instantiated object” by combinations of its “attribute values” Normalization can cause the object representation to be spread out across several tables Normalization can cause the object representation to be spread out across several tables No encapsulated data in the Relational Model No encapsulated data in the Relational Model

11. UCB SIMS 202 Garment Maker vs. Garment Maker Class Fur Class Fur Animal: fox, rabbit, sable Animal: fox, rabbit, sable Color: red, black, white Color: red, black, white Texture: silky, thick, coarse Texture: silky, thick, coarse Garment_type: coat, stole, hat Garment_type: coat, stole, hat Class Garment Class Garment Material: fur, cotton, wool Material: fur, cotton, wool Color: red, black, brown, white, blue Color: red, black, brown, white, blue Garment_Type: coat, stole, hat Garment_Type: coat, stole, hat Problem: match color to material Problem: match color to material

12. UCB SIMS 202 Nesting Attributes and Classes Class Garment Class Garment Material: Material: Class Fur Class Fur n Animal: fox, rabbit, sable n Color: red, black, white n Texture: silky, thick, coarse Class Cotton Class Cotton n Color: red, blue, white, brown, black n Thread_Count: 100, 200 Garment_type: stole, coat, hat, t-shirt Garment_type: stole, coat, hat, t-shirt Attributes often must be nested Attributes often must be nested Alternative: two subclasses of Garment Alternative: two subclasses of Garment

13. UCB SIMS 202 Normalization and Nesting In the Relational Model, Normalization “flattens out” the Nesting In the Relational Model, Normalization “flattens out” the Nesting Why? Why? Normalization makes certain kinds of access more efficient, less likely to mess up updates Normalization makes certain kinds of access more efficient, less likely to mess up 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 situations Key: Relational and OO used in different situations

14. UCB SIMS 202 Relations vs. Objects Objects Objects Nomads, doing their own thing, rugged individualists, use one-at-a-time Nomads, doing their own thing, rugged individualists, use one-at-a-time Example: program running on a printer Example: program running on a printer Relations: Relations: Packed into apartments, lots and lots of items all being lined up in one place for easy comparison Packed into apartments, lots and lots of items all being lined up in one place for easy comparison Queries: Find all X that have Y and are > Z Queries: 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.

15. UCB SIMS 202 Relations 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?

16. UCB SIMS 202 Metadata vs. Objects MetaData like the Dublin Core is simple MetaData like the Dublin Core is simple Much like the name, attribute parts of a Class Much like the name, attribute parts of a Class No methods No methods MetaData like AACRII is messier MetaData like AACRII is messier A bunch of rules about how to deal with the exceptions A bunch of rules about how to deal with the exceptions Law deals quite a bit with exceptions Law deals quite a bit with exceptions Computer Science tries as hard as possible to abstract away or ignore exceptions Computer Science tries as hard as possible to abstract away or ignore exceptions

17. UCB SIMS 202 Why are we learning about this old library stuff? The computer science tradition is good at abstracting away details. The computer science tradition is good at abstracting away details. The computer science tradition is not good at describing detail and convoluted exceptions. The computer science tradition is not good at describing detail and convoluted exceptions. The library tradition can teach us something useful about how to describe complex data. The library tradition can teach us something useful about how to describe complex data. Think about how these bibliographic examples can be applied to other domains (maybe a test question!!!) Think about how these bibliographic examples can be applied to other domains (maybe a test question!!!)

18. UCB SIMS 202 Metadata vs. Relational Model Relational model makes use of Metadata Relational model makes use of Metadata The description of the database is often called a Schema The description of the database is often called a Schema The Schema is a kind of Metadata description The Schema is a kind of Metadata description Main differences: Main differences: Exceptions not handled well in the relational model either Exceptions not handled well in the relational model either Relational model focus is on the system design Relational model focus is on the system design Metadata focus is on the description of the data, independent of a computer system Metadata focus is on the description of the data, independent of a computer system

19. UCB SIMS 202 Fresh Topic: Why is this Stuff Hard? These are all variations on Categorization These are all variations on Categorization Categorization is an important topic in: Categorization is an important topic in: Philosophy Philosophy Language/Linguistics Language/Linguistics Psychology Psychology How does the human mind do categorization? How does the human mind do categorization?

20. UCB SIMS 202 What’s In a Sentence? “A sentence is not a verbal snapshot or movie of an event. In framing an utterance, you have to abstract away from everything you know, or can picture, about a situation, and present a schematic version which conveys the essentials. In terms of grammatical marking, there is not enough time in the speech situation for any language to allow for the marking of everything which could possibly be significant to the message.” Dan Slobin, in Language Acquisition: The state of the art, 1982

21. UCB SIMS 202 Approximating Meaning Defining attributes Defining attributes A weak approximation to meanings and concepts A weak approximation to meanings and concepts Defining methods Defining methods A weak approximation to how these meanings interact and change A weak approximation to how these meanings interact and change Necessary and Sufficient Conditions Necessary and Sufficient Conditions Example: A prime number is an integer divisible only by itself and 1. Example: A prime number is an integer divisible only by itself and 1.

22. UCB SIMS 202 Properties of Categorization Family Resemblance: Family Resemblance: Members of a category may be related to one another without all members having any properties in common that define the category. Members of a category may be related to one another without all members having any properties in common that define the category. Centrality: Centrality: Some members of a category may be “better examples” of that category than others. Some members of a category may be “better examples” of that category than others.

23. UCB SIMS 202 Centrality A category: Prime Numbers A category: Prime Numbers Definition: An integer divisible only by itself and 1 Definition: An integer divisible only by itself and 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 another? Can one number be “more prime” than another? CENTRALITY: some members of a category may be “better examples” than others CENTRALITY: some members of a category may be “better examples” than others

24. UCB SIMS 202 Definition of Game Famous example by Wittgenstein Famous example by Wittgenstein Classic categories: clear boundaries defined by common properties Classic categories: clear boundaries defined by common properties Counterexample: Game Counterexample: Game No common properties shared by all games No common properties shared by all games card games, ball games, Olympic games, children’s games card games, ball games, Olympic games, children’s games competition: ring-around-the-rosie competition: ring-around-the-rosie skill: dice games skill: dice games luck: chess luck: chess No fixed boundary; can be extended to new games No fixed boundary; can be extended to new games video games video games Alternative: Concepts related by Family Resemblances Alternative: Concepts related by Family Resemblances

25. UCB SIMS 202 Characteristic Features Perceived degree of category membership has to do with which features define the category. Perceived degree of category membership has to do with which features define the category. Members usually do not have ALL the necessary features, but have some subset. Members usually do not have ALL the necessary features, but have some subset. Those members that have more of the central features are seen as more central members. Those members that have more of the central features are seen as more central members. People have conceptions of typical members. People have conceptions of typical members.

26. UCB SIMS 202 Properties of Categorization Basic-level Categories: Basic-level Categories: Categories are organized into a hierarchy from the most general to the most specific, but the level that is most cognitively basic is “in the middle” of the hierarchy Categories are organized into a hierarchy from the most general to the most specific, but the level that is most cognitively basic is “in the middle” of the hierarchy Basic-level Primacy: Basic-level Primacy: Basic-level categories are functionally primary with respect to factors including ease of cognitive processing (learning, reasoning, recognition, etc). Basic-level categories are functionally primary with respect to factors including ease of cognitive processing (learning, reasoning, recognition, etc).

27. UCB SIMS 202 Levels of Abstraction Brown 1958, 65, Berlin et al., 1972, 73 Brown 1958, 65, Berlin et al., 1972, 73 Folk biology: Folk biology: unique beginner: plant, animal unique beginner: plant, animal life form: tree, bush, flower life form: tree, bush, flower generic name: pine, oak, maple, elm generic name: pine, oak, maple, elm specific name: Ponderosa pine, white pine specific name: Ponderosa pine, white pine varietal name: western Ponderosa pine varietal name: western Ponderosa pine No overlap between levels No overlap between levels Level 3 is basic Level 3 is basic Level 3 corresponds to genus Level 3 corresponds to genus

28. UCB SIMS 202 Characteristics of Basic-level Categories Language Language People name things more readily at basic level People name things more readily at basic level Name learned earliest in childhood Name learned earliest in childhood Languages have simpler names at basic level Languages have simpler names at basic level Sounds like the “real name” Sounds like the “real name” Name used more frequently Name used more frequently Strange to call a dime a coin, a metal object Strange to call a dime a coin, a metal object Names used in neutral context Names 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.

29. UCB SIMS 202 Characteristics of Basic-level Categories Concepts Concepts Things perceived more wholistically at basic level (rather than by parts) Things perceived more wholistically at basic level (rather than by parts) No difference in how people interact with the concept between basic and more specific levels No difference in how people interact with the concept between basic and more specific levels Things are remembered more readily at basic level Things are remembered more readily at basic level Folk biology categories correspond accurately to scientific biological categories only at the basic level Folk biology categories correspond accurately to scientific biological categories only at the basic level

30. UCB SIMS 202 Superordinate and Subordinate Levels SUPERORDINATE animal furniture BASIC LEVEL dog chair SUBORDINATE terrier rocker Children take longer to learn superordinate Children take longer to learn superordinate Superordinate not associated with mental images or motor actions Superordinate not associated with mental images or motor actions

31. UCB SIMS 202 Typicality and Characteristic Features Some categories have clear boundaries, but have graded membership Some categories have clear boundaries, but have graded 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 the third is false Takes longer for people to say the second is true and the third is false Features characterize the category Features characterize the category How many typical features does the object possess? How many typical features does the object possess?

32. UCB SIMS 202 Characteristic 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 cat? Is a cat with every cell replaced by a dog’s cells a cat?

33. UCB SIMS 202 Polysemy Most words have more than one sense Most words have more than one sense that dog has floppy ears that dog has floppy ears good ear for jazz good ear for jazz three ears of corn three ears of corn Homonymy: same word, different meaning Homonymy: same word, different meaning Polysemy: different senses of same word Polysemy: different senses of same word

34. UCB SIMS 202 Category Structure and Polysemy Category membership is determined by shared subsets of features Category membership is determined by shared subsets of features Different senses of a word reflect differences in which attributes are shared Different senses of a word reflect differences in which attributes are shared This is reflected in language by polysemy This is reflected in language by polysemy related meaning, but slightly different related meaning, but slightly different Example: bank Example: bank the building, the institution, the notion of where money is stored the building, the institution, the notion of where money is stored

35. UCB SIMS 202 Metonymy Use one aspect of something to stand for the whole Use one aspect of something to stand for the whole The building stands for the institution of the bank. The building stands for the institution of the bank. Newscast: “The White House relased new figures today.” Newscast: “The White House relased new figures today.” Waitperson: “The ham sandwich spilled his drink.” Waitperson: “The ham sandwich spilled his drink.”

36. UCB SIMS 202 Synonymy Different ways of expressing related concepts Different ways of expressing related concepts Examples Examples cat, feline, Siamese cat cat, feline, Siamese cat Overlaps with basic, subordinate level Overlaps with basic, subordinate level Synonyms are almost never “true” Synonyms are almost never “true” used in different contexts used in different contexts have different implications have different implications This is a point of contention. This is a point of contention.

37. UCB SIMS 202 Thesauri Polysemy: same word, different senses of meaning Polysemy: same word, different senses of meaning slightly different concepts expressed similarly slightly different concepts expressed similarly Synonyms: different words, related senses of meanings Synonyms: different words, related senses of meanings different ways to express similar concepts different ways to express similar concepts Thesauri help draw all these together Thesauri help draw all these together

38. UCB SIMS 202 Summary Processes of categorization underlie many of the issues having to do with information organization Processes of categorization underlie many of the issues having to do with information organization Categorization is messier than our computer systems would like Categorization is messier than our computer systems would like Human categories have graded membership, consisting of family resemblances. Human categories have graded membership, consisting of family resemblances. Family resemblance is expressed in part by which subset of features are shared Family resemblance is expressed in part by which subset of features are shared It is also determined by underlying understandings of the world that do not get represented in most systems It is also determined by underlying understandings of the world that do not get represented in most systems