Introduction to Ontologies ECE457 Applied Artificial Intelligence Fall 2007 Lecture #13

Outline Ontology Inheritance  CS 886 (Prof. DiMarco) Russell & Norvig, sections 10.1, 10.2, 10.6  CS 886 (Prof. DiMarco) ECE457 Applied Artificial Intelligence R. Khoury (2007) Page 2

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 ECE457 Applied Artificial Intelligence R. Khoury (2007) Page 3

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 ECE457 Applied Artificial Intelligence R. Khoury (2007) Page 4

Objects and Categories Real-world items Apple A42, Bob the penguin Categories Abstractions, groups of objects Apples, fruits, seeds, penguins, birds, wings, physical objects ECE457 Applied Artificial Intelligence R. Khoury (2007) Page 5

Objects and Categories PhysicalObjects Fruits Birds Apples Seeds Wings Penguins A42 Bob ECE457 Applied Artificial Intelligence R. Khoury (2007) Page 6

Relations Binary connections Typical relations 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 ECE457 Applied Artificial Intelligence R. Khoury (2007) Page 7

Relations PhysicalObjects IsA IsA Fruits Birds PartOf PartOf IsA IsA Apples Seeds Wings Penguins InstanceOf InstanceOf A42 Bob ECE457 Applied Artificial Intelligence R. Khoury (2007) Page 8

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) ECE457 Applied Artificial Intelligence R. Khoury (2007) Page 9

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, … ECE457 Applied Artificial Intelligence R. Khoury (2007) Page 10

Attributes PhysicalObjects Mass=? Age=? IsA IsA Fruits Edible=Yes Birds Feather=Yes PartOf PartOf IsA IsA Apples Colour={Red,Green} Seeds Wings Penguins InstanceOf InstanceOf A42 Kind=McIntosh Bob Age=2 years ECE457 Applied Artificial Intelligence R. Khoury (2007) Page 11

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) ECE457 Applied Artificial Intelligence R. Khoury (2007) Page 12

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 ECE457 Applied Artificial Intelligence R. Khoury (2007) Page 13

Inheritance Network Fruits are edible, apple is a fruit, therefore apple is edible Birds have feathers, penguin is a bird, therefore penguin has feathers Fruits Edible=Yes Birds Feather=Yes Apples Penguins A42 Bob ECE457 Applied Artificial Intelligence R. Khoury (2007) Page 14

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) ECE457 Applied Artificial Intelligence R. Khoury (2007) Page 15

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 Birds Fly=Yes Penguins Fly=No Bob Fly=? ECE457 Applied Artificial Intelligence R. Khoury (2007) Page 16

Inheritance Problems Ambiguous network Quaker Pacifist=Yes 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 (2007) Page 17

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 ECE457 Applied Artificial Intelligence R. Khoury (2007) Page 18

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 ECE457 Applied Artificial Intelligence R. Khoury (2007) Page 19

Automated Ontology Learning Seed ontologies Input texts Natural language processing system Knowledge extractor Databases Lexicon Inference rules KB manager Ontology manager KB Ontology Engineer ECE457 Applied Artificial Intelligence R. Khoury (2007) Page 20

Ontology Example: WordNet English vocabulary ontology Some call it a lexical hierarchy 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 ECE457 Applied Artificial Intelligence R. Khoury (2007) Page 21

{organism, living thing} WordNet Relations 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 {organism, living thing} {animal, fauna} {bird} {robin, redbreast} ECE457 Applied Artificial Intelligence R. Khoury (2007) Page 22

WordNet Relations {body part } Meronymy Intertwined with Hyponymy “is a part of”, “has a” Meronym(beak,bird) Holonym(bird,beak) Intertwined with Hyponymy {external body part } {feature, lineament } {face, human face} {bird} {mouth} {jaw} {beak, bill, neb, nib} ECE457 Applied Artificial Intelligence R. Khoury (2007) Page 23

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 ECE457 Applied Artificial Intelligence R. Khoury (2007) Page 24