Transcript ppt

Introduction to Ontologies
ECE457 Applied Artificial Intelligence
Spring 2008
Lecture #13
Outline
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Ontology
Inheritance
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Russell & Norvig, sections 10.1, 10.2, 10.6
 CS 886 (Prof. DiMarco)
ECE457 Applied Artificial Intelligence
R. Khoury (2008)
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Knowledge Base
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In logic, our KB was simply a list of facts
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Works because we use simple examples
Won’t work in real life
Need to structure facts in KB
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Make storing, searching for and retrieving
information from KB easier
Sort facts into categories
Define relationships between facts and/or
categories
Arrange relationships hierarchically
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Ontology
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ECE457 Applied Artificial Intelligence
R. Khoury (2008)
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Ontology
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Representation of concepts and relationships
between concepts
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Allows representation and handling of information
about objects represented in it
Can be general or domain-specific
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Reusability vs. easy of design, analysis, implementation
Four main parts
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Objects
Categories
Relations
Attributes
ECE457 Applied Artificial Intelligence
R. Khoury (2008)
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Objects and Categories
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Objects
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Real-world items
Apple A42, Bob the penguin
Categories
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Abstractions, groups of objects
Apples, fruits, seeds, penguins, birds,
wings, physical objects
ECE457 Applied Artificial Intelligence
R. Khoury (2008)
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Objects and Categories
PhysicalObjects
Fruits
Apples
Birds
Seeds
Wings
A42
ECE457 Applied Artificial Intelligence
Penguins
Bob
R. Khoury (2008)
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Relations
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Binary connections
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Between two objects, two categories, or an
object and a category
Typical relations
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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
ECE457 Applied Artificial Intelligence
R. Khoury (2008)
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Relations
IsA
PhysicalObjects
IsA
Fruits
Birds
PartOf
IsA
Apples
Seeds
InstanceOf
PartOf
Wings
Penguins
InstanceOf
A42
ECE457 Applied Artificial Intelligence
IsA
Bob
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Relations
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Objects and categories are constant symbols
in FOL
Relations are predicates in FOL
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InstanceOf(A42,Apples)
IsA(Apples,Fruits)
PartOf(Seeds,Fruits)
IsA(Fruits,PhysicalObjects)
InstanceOf(Bob,Penguins)
IsA(Penguins,Birds)
PartOf(Wings,Birds)
IsA(Birds,PhysicalObjects)
ECE457 Applied Artificial Intelligence
R. Khoury (2008)
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Attributes
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Properties of objects and categories
Intrinsic properties
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Part of the very nature of the category
Boiling point, edible, can float, …
Extrinsic properties
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Specific to each object
Weight, length, age, …
ECE457 Applied Artificial Intelligence
R. Khoury (2008)
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Attributes
PhysicalObjects
Mass=? Age=?
IsA
IsA
Fruits
Edible=Yes
IsA
Apples
Birds
PartOf
Colour={Red,Green}
Seeds
InstanceOf
A42
Kind=McIntosh
ECE457 Applied Artificial Intelligence
PartOf
Wings
Feather=Yes
IsA
Penguins
InstanceOf
Bob
Age=2 years
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Attributes
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Relations are functions or predicates in
FOL
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Edible(Fruits)
Feather(Birds)
Mass(PhysicalObjects,x)
Age(PhysicalObjects,x)
Colour(Apples,Red)  Colour(Apples,Green)
Kind(A42,McIntosh)
Age(Bob,2)
ECE457 Applied Artificial Intelligence
R. Khoury (2008)
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Inheritance
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Passing properties from general
categories to specialized categories or
objects
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Categories/objects have to be connected
Easily gain a great deal of information
about children
ECE457 Applied Artificial Intelligence
R. Khoury (2008)
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Inheritance Network
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Fruits are edible, Fruits
apple is a fruit,
Edible=Yes
therefore apple is
edible
Birds have
Apples
feathers, penguin
is a bird, therefore
penguin has
feathers
A42
ECE457 Applied Artificial Intelligence
R. Khoury (2008)
Birds
Feather=Yes
Penguins
Bob
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Inheritance Network
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Inheritance network is sentences in FOL
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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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R. Khoury (2008)
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Inheritance Problems
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Child inherits contradicting
attributes from its parent
and grandparent
Shortest path heuristic
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Penguins closer than Birds
Danger: redundant links
Inferential distance
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Penguins closer than Birds
because there is a path from
Bob to Birds through Penguins
ECE457 Applied Artificial Intelligence
R. Khoury (2008)
Birds
Fly=Yes
Penguins
Fly=No
Bob
Fly=?
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Inheritance Problems
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Ambiguous network
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Child inherits contradicting attributes from
its parents
Inferential distance doesn’t apply!
Quaker
Pacifist=Yes
Republican
Pacifist=No
Richard Nixon
Pacifist=?
ECE457 Applied Artificial Intelligence
R. Khoury (2008)
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Solutions to Ambiguous Nets
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Credulous approach
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Sceptical approach
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Randomly select one value
Assign no value
Shortest path heuristic
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Assign the value resulting from the
shortest path in the network
Path length not a relevance measure
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Shortcuts in network
Use of many fine-grained distinctions
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R. Khoury (2008)
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Ontology Learning
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One of the main challenges in ontology
research today
Often done manually
Partially-automated techniques
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Still need manual checking
Start from a manually-constructed core
ontology
Work best for specialized ontologies
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R. Khoury (2008)
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Automated Ontology Learning
Seed ontologies
Input texts
Natural language
processing system
Knowledge
extractor
Lexicon
Inference
rules
Databases
KB manager
Ontology
Ontology
manager
KB
Engineer
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R. Khoury (2008)
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Ontology Example: WordNet
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English vocabulary ontology
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Handles nouns, verbs, adjectives and
adverbs independently
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Some call it a lexical hierarchy
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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R. Khoury (2008)
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WordNet Relations
{organism,
living thing}
{animal, fauna}
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Synonymy
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Hyponymy
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{bird}
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{robin, redbreast}
ECE457 Applied Artificial Intelligence
Sets of synonyms (synsets)
are the basic building blocks
of WordNet
Also an Antonymy relation
“is a kind of”
Hyponym(Robin,Bird)
Hypernym(Bird,Robin)
Organizes WordNet into
lexical hierarchy
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WordNet Relations
{body part }
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Meronymy
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{external
{feature,
body part } lineament }
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{face,
human face}
{bird}
{mouth}
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“is a part of”, “has a”
Meronym(beak,bird)
Holonym(bird,beak)
Intertwined with
Hyponymy
{jaw}
{beak, bill, neb, nib}
ECE457 Applied Artificial Intelligence
R. Khoury (2008)
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WordNet Construction
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Created at Cognitive Science Laboratory,
Princeton University
Started with Brown Corpus and integrated
pre-existing thesaurus
Manually created, expanded and verified
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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
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