1. Introduction to Knowledge Representation Marti Hearst SIMS 202: Information Organization and Retrieval Lecture 6, Sept 10, 1998

2. Today What is a symbol? What is a symbol? Semantics: the meanings of symbols Semantics: the meanings of symbols Creating Ontologies Creating Ontologies Objects, Properties, and Relations Objects, Properties, and RelationsReferences: Chapter 1 of Introduction to Knowledge Systems by Mark Stefik. Chapter 1 of Introduction to Knowledge Systems by Mark Stefik. Chapter 8 of Artificial Intelligence, A Modern Approach by Stuart Russell and Peter Norvig Chapter 8 of Artificial Intelligence, A Modern Approach by Stuart Russell and Peter Norvig

3. What is a symbol? From Merriam-Webster’s Collegiate: From Merriam-Webster’s Collegiate: Something that stands for or suggests something else. Something that stands for or suggests something else. An arbitrary conventional sign used in writing or printing to represent: An arbitrary conventional sign used in writing or printing to represent: operations operations quantities quantities elements elements relations relations qualities qualities What is meant by sign and represent? What is meant by sign and represent?

4. Recognizing Symbols What What is/are this/these symbol(s)? Two Two overlapping squares? Eight Eight horizontal and eight vertical lines?

5. Designation and Representation What does it mean to represent something? What does it mean to represent something? Identify the following: Identify the following: symbols symbols context context observer observer Representation is the association of symbols with conceptual objects or ideas in a given context. Representation is the association of symbols with conceptual objects or ideas in a given context. The observer sets up a correspondence between the symbols and the meanings. The observer sets up a correspondence between the symbols and the meanings.

6. Representation with Symbols Kailin threw the ball to Juno. Kailinthrow event Juno a ball did-action thrower thrown-to object- thrown

7. Symbols and Language Abstract concepts are difficult to express in a computer. Abstract concepts are difficult to express in a computer. Combinations of abstract concepts are even more difficult to express: Combinations of abstract concepts are even more difficult to express: time time shades of meaning shades of meaning social and psychological concepts social and psychological concepts causal relationships causal relationships

8. Symbols and Language The Dog.

9. Symbols and Language The Dog. The dog cavorts. The dog cavorted. The picture doesn’t really show the manner or tense.

10. Symbols and Language The man. The man walks.

11. Symbols and Language The man walks the cavorting dog. So far, we can sort of show the meaning in pictures.

12. Symbols and Language As the man walks the cavorting dog, thoughts arrive unbidden of the previous spring, so unlike this one, in which walking was marching and dogs were baleful sentinals outside unjust halls. What is the relation between the symbols and the meaning?

13. Symbols and Language Language only hints at meaning. Language only hints at meaning. Most meaning of text lies within our minds and common understanding. Most meaning of text lies within our minds and common understanding. “How much is that doggy in the window?” “How much is that doggy in the window?” how much: social system of barter and trade (not the size of the dog) how much: social system of barter and trade (not the size of the dog) “doggy” implies childlike, plaintive, probably cannot do the purchasing on their own “doggy” implies childlike, plaintive, probably cannot do the purchasing on their own “in the window” implies behind a store window, not really inside a window, requires notion of window shopping “in the window” implies behind a store window, not really inside a window, requires notion of window shopping

14. Setting up Correspondences between Symbols and Meaning Consider made-up languages Consider made-up languages Codes used by espionage agents Codes used by espionage agents “Pope” means a particular piece of microfilm “Pope” means a particular piece of microfilm “Denver” indicates a particular mailbox “Denver” indicates a particular mailbox People remember the “gist” instead of the actual words used. People remember the “gist” instead of the actual words used. This implies the actual words used are not very salient; what matters is the meaning. This implies the actual words used are not very salient; what matters is the meaning.

15. Recognizing Symbols The marks that constitute a symbol depend on the conventions for recognizing the symbol. The marks that constitute a symbol depend on the conventions for recognizing the symbol. A recognizer typically has an associated alphabet or set of symbols: A recognizer typically has an associated alphabet or set of symbols: Token: an individual instance of a symbol Token: an individual instance of a symbol Type: a class of symbols Type: a class of symbols Examples? Examples?

16. The Role of Context The concept associated with the symbol 21 means different things in different contexts. The concept associated with the symbol 21 means different things in different contexts. Examples? Examples? The question “Is there any salt?” The question “Is there any salt?” Asked of a waiter at a restaurant. Asked of a waiter at a restaurant. Asked of an environmental scientist at work. Asked of an environmental scientist at work.

17. Semantics: The Meaning of Symbols Semantics versus Syntax Semantics versus Syntax Meaning versus Representation Meaning versus Representation What a person’s name is versus who they are. What a person’s name is versus who they are. A rose by any other name... A rose by any other name... What the computer program “looks like” versus what it actually does. What the computer program “looks like” versus what it actually does.

18. Semantics Semantics: assigning meanings to symbols and expressions. Semantics: assigning meanings to symbols and expressions. Usually involves defining: Usually involves defining: objects objects properties of objects properties of objects relations between objects relations between objects More detailed versions include (among others) More detailed versions include (among others) events events time time places places measurements (quantities) measurements (quantities)

19. Ontology From Merriam-Webster’s Collegiate: From Merriam-Webster’s Collegiate: A branch of metaphysics concerned with the nature and relations of being. A branch of metaphysics concerned with the nature and relations of being. A particular theory about the nature of being or the kinds of existence. A particular theory about the nature of being or the kinds of existence. More prosaically: More prosaically: A carving up of the world’s meanings. A carving up of the world’s meanings. Determine what things exist, but not how they inter-relate. Determine what things exist, but not how they inter-relate. Related terms: Related terms: taxonomy, dictionary, category structure taxonomy, dictionary, category structure

20. Knowledge Engineering Steps Decide what to talk about Decide what to talk about Decide on a vocabulary of predicates, functions, and constants Decide on a vocabulary of predicates, functions, and constants Encode general knowledge about the domain Encode general knowledge about the domain Artificial Intelligence vs Cataloging: Artificial Intelligence vs Cataloging: AI goal: allow computer programs to reason about the objects and relations AI goal: allow computer programs to reason about the objects and relations Cataloging: organize the objects and relations for use by humans Cataloging: organize the objects and relations for use by humans AI is more ambitious and more difficult AI is more ambitious and more difficult We aren’t covering the reasoning part here. We aren’t covering the reasoning part here.

21. Try some examples Let’s define Let’s define Types of Objects Types of Objects Types of Properties of Objects Types of Properties of Objects Types of Relations between Objects Types of Relations between Objects

22. Attributes vs. Objects 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 of objects Fur is a class of objects Animal is an attribute of this class Animal is an attribute of this class Say we are naturalists. Say we are naturalists. Animal is a class of objects Animal is a class of objects Fur is an attribute of this class Fur is an attribute of this class

23. Garment Maker Ontology Define the objects; Indicate what types of attributes are used to define the objects (attributes=properties) Define the objects; Indicate what types of attributes are used to define the objects (attributes=properties) Object Class: Object Class: Garment Garment Attribute Types: Attribute Types: ISA: ISA: Material: Material: Color: Color: Garment_Type: Garment_Type: Object Class: Object Class: Fur Fur Attribute Types: Attribute Types: ISA: ISA: Animal: Animal: Color: Color: Texture: Texture:

24. Garment Maker Ontology Attributes have lists of legal values Attributes have lists of legal values Object Class: Garment Object Class: Garment ISA: Object ISA: Object 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 Object Class: Fur Object Class: Fur ISA: Material ISA: Material Animal: fox, rabbit, sable Animal: fox, rabbit, sable Color: red, black, white Color: red, black, white Texture: silky, thick, coarse Texture: silky, thick, coarse

25. Garment Maker Ontology Show the assignments of values to attributes for one particular instance of an object Show the assignments of values to attributes for one particular instance of an object Object Class: Garment Object Class: Garment ISA: Object ISA: Object 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 Object Class: Fur Object Class: Fur ISA: Material ISA: Material 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 coat fur object silkyred G_type ISA Material Color Texture sable Animal

26. Garment Maker Ontology Usually only one value is allowed for an ISA attribute Usually only one value is allowed for an ISA attribute In this example, In this example, The value of the color attribute for Garment is determined by the color attribute for the garment’s Material attribute The value of the color attribute for Garment is determined by the color attribute for the garment’s Material attribute This is called inheritance This is called inheritance garment coat fur object silkyred G_type ISA Material Color Texture sable Animal

27. Garment Makers vs. Naturalists A difference between a class definition and an attribute value A difference between a class definition and an attribute value Class Fur Class Fur ISA: material ISA: material 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 ISA: mammal ISA: mammal 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

28. 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

29. Next Week Semantic Nets Semantic Nets Facets vs. Hierarchies Facets vs. Hierarchies Lexical Semantics Lexical Semantics Word Associations Word Associations