introduction to ontologies ece457 applied artificial intelligence spring 2007 lecture #13

Introduction to Ontologies

ECE457 Applied Artificial IntelligenceSpring 2007 Lecture #13

ECE457 Applied Artificial Intelligence R. Khoury (2007)

Outline Ontology Inheritance

Russell & Norvig, sections 10.1, 10.2, 10.6

CS 886 (Prof. DiMarco)


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


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


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


Objects and Categories

PhysicalObjects

Fruits

Apples

A42

Birds

Penguins

Bob

Seeds

Wings


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


Relations

PhysicalObjects

Fruits

Apples

A42

Birds

Penguins

Bob

IsA

IsA IsA

InstanceOf

InstanceOf

Seeds

Wings

PartOf PartOf

IsA


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)


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, …


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


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)


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


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


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)


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=?


Inheritance Problems Ambiguous network

Child inherits contradicting attributes from its parents

Inferential distance doesn’t apply!

RepublicanPacifist=No

Richard NixonPacifist=?

QuakerPacifist=Yes


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


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


Automated Ontology Learning

Input texts

Seed ontologies

Natural language

processing system

Lexicon

Databases

Knowledge extractor

KB manager

Ontology

Engineer

Ontology manager

Inference rules

KB


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


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


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 }


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

introduction to ontologies ece457 applied artificial intelligence spring 2007 lecture #13

Documents

category slide

artificial intelligence

physicalobjects slide

relations objects

ontology slide

ontologies ece457

dimarco slide

attributes relations