introduction to ontologies ece457 applied artificial intelligence spring 2007 lecture #13
TRANSCRIPT
ECE457 Applied Artificial Intelligence R. Khoury (2007) Page 2
Outline Ontology Inheritance
Russell & Norvig, sections 10.1, 10.2, 10.6
CS 886 (Prof. DiMarco)
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Knowledge Base In logic, our KB was simply a list of facts
Works because we use simple examples Won’t work in real life
Need to structure facts in KB Make storing, searching for and retrieving
information from KB easier Sort facts into categories Define relationships between facts and/or
categories Arrange relationships hierarchically Ontology
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Ontology Representation of concepts and
relationships between concepts Allows representation and handling of
information about objects represented in it Can be general or domain-specific
Reusability vs. easy of design, analysis, implementation
Four main parts Objects Categories Relations Attributes
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Objects and Categories Objects
Real-world items Apple A42, Bob the penguin
Categories Abstractions, groups of objects Apples, fruits, seeds, penguins, birds,
wings, physical objects
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Objects and Categories
PhysicalObjects
Fruits
Apples
A42
Birds
Penguins
Bob
Seeds
Wings
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Relations Binary connections
Between two objects, two categories, or an object and a category
Typical relations IsA: A category is a kind of another
category InstanceOf: An object is an instance of
a category PartOf: A category is a part of any
object that’s an instance of another category
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Relations
PhysicalObjects
Fruits
Apples
A42
Birds
Penguins
Bob
IsA
IsA IsA
InstanceOf
InstanceOf
Seeds
Wings
PartOf PartOf
IsA
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Relations Objects and categories are constant
symbols in FOL Relations are predicates in FOL
InstanceOf(A42,Apples) IsA(Apples,Fruits) PartOf(Seeds,Fruits) IsA(Fruits,PhysicalObjects) InstanceOf(Bob,Penguins) IsA(Penguins,Birds) PartOf(Wings,Birds) IsA(Birds,PhysicalObjects)
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Attributes Properties of objects and
categories Intrinsic properties
Part of the very nature of the category
Boiling point, edible, can float, … Extrinsic properties
Specific to each object Weight, length, age, …
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AttributesPhysicalObjectsMass=? Age=?Fruits
Edible=Yes
ApplesColour={Red,Green}
A42
Kind=McIntosh
BirdsFeather=Yes
Penguins
BobAge=2 years
IsA
IsA IsA
InstanceOf
InstanceOf
Seeds
Wings
PartOf PartOf
IsA
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Attributes Relations are functions or
predicates in FOL Edible(Fruits) Feather(Birds) Mass(PhysicalObjects,x) Age(PhysicalObjects,x) Colour(Apples,Red)
Colour(Apples,Green) Kind(A42,McIntosh) Age(Bob,2)
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Inheritance Passing properties from general
categories to specialized categories or objects Categories/objects have to be
connected Easily gain a great deal of information
about children
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Inheritance Network Fruits are edible,
apple is a fruit, therefore apple is edible
Birds have feathers, penguin is a bird, therefore penguin has feathers
FruitsEdible=Yes
Apples
A42
BirdsFeather=Yes
Penguins
Bob
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Inheritance Network Inheritance network is sentences in
FOL x IsA(x,Fruits) Edible(x) x InstanceOf(x,y) IsA(y,Fruits)
Edible(x) x IsA(x,Bird) HasFeathers(x) x InstanceOf(x,y) IsA(y,Bird)
HasFeathers(x)
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Inheritance Problems Child inherits contradicting
attributes from its parent and grandparent
Shortest path heuristic Penguins closer than Birds Danger: redundant links
Inferential distance Penguins closer than Birds
because there is a path from Bob to Birds through Penguins
BirdsFly=Yes
PenguinsFly=No
BobFly=?
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Inheritance Problems Ambiguous network
Child inherits contradicting attributes from its parents
Inferential distance doesn’t apply!
RepublicanPacifist=No
Richard NixonPacifist=?
QuakerPacifist=Yes
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Solutions to Ambiguous Nets Credulous approach
Randomly select one value Sceptical approach
Assign no value Shortest path heuristic
Assign the value resulting from the shortest path in the network
Path length not a relevance measure Shortcuts in network Use of many fine-grained distinctions
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Ontology Learning One of the main challenges in
ontology research today Often done manually Partially-automated techniques
Still need manual checking Start from a manually-constructed
core ontology Work best for specialized ontologies
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Automated Ontology Learning
Input texts
Seed ontologies
Natural language
processing system
Lexicon
Databases
Knowledge extractor
KB manager
Ontology
Engineer
Ontology manager
Inference rules
KB
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Ontology Example: WordNet English vocabulary ontology Handles nouns, verbs, adjectives
and adverbs independently Nouns ontology biggest and most used Nouns subdivided in 25 classes
Often used to measure the similarity/distance between words
So successful, other languages WordNet are being created
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WordNet Relations
{organism,
living thing}
{animal, fauna}
{bird}
{robin, redbreast}
Synonymy Sets of synonyms (synsets)
are the basic building blocks of WordNet
Also an Antonymy relation Hyponymy
“is a kind of” Hyponym(Robin,Bird) Hypernym(Bird,Robin) Organizes WordNet into
lexical hierarchy
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WordNet Relations Meronymy
“is a part of”, “has a”
Meronym(beak,bird)
Holonym(bird,beak)
Intertwined with Hyponymy
{bird}{beak, bill, neb, nib}
{mouth}
{face, human face}
{jaw}
{feature, lineament
}
{body part }
{external
body part }
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WordNet Construction Created at Cognitive Science
Laboratory, Princeton University Started with Brown Corpus and
integrated pre-existing thesaurus Manually created, expanded and
verified Online effort Uses home-made programs to help 1985: started 1993: 57,000 nouns in 48,800 synsets 1998: 80,000 nouns in 60,000 synsets 2007: 117,000 nouns in 81,000 synsets