1. Introduction to Ontologies ECE457 Applied Artificial Intelligence Spring 2007 Lecture #13

2. 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)

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

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

5. ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 5 Objects and Categories Objects Real-world items Apple A 42, Bob the penguin Categories Abstractions, groups of objects Apples, fruits, seeds, penguins, birds, wings, physical objects

6. ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 6 Objects and Categories PhysicalObjects Fruits Apples A 42 Birds Penguins Bob SeedsWings

7. ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 7 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

8. ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 8 Relations PhysicalObjects Fruits Apples A 42 Birds Penguins Bob IsA InstanceOf SeedsWings PartOf IsA

9. ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 9 Relations Objects and categories are constant symbols in FOL Relations are predicates in FOL InstanceOf(A 42,Apples) IsA(Apples,Fruits) PartOf(Seeds,Fruits) IsA(Fruits,PhysicalObjects) InstanceOf(Bob,Penguins) IsA(Penguins,Birds) PartOf(Wings,Birds) IsA(Birds,PhysicalObjects)

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

11. ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 11 Attributes PhysicalObjects Mass=? Age=? Fruits Edible=Yes Apples Colour={Red,Green} A 42 Kind=McIntosh Birds Feather=Yes Penguins Bob Age=2 years IsA InstanceOf SeedsWings PartOf IsA

12. ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 12 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(A 42,McIntosh) Age(Bob,2)

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

14. ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 14 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 Apples A 42 Birds Feather=Yes Penguins Bob

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

16. ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 16 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=?

17. ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 17 Inheritance Problems Ambiguous network Child inherits contradicting attributes from its parents Inferential distance doesn’t apply! Republican Pacifist=No Richard Nixon Pacifist=? Quaker Pacifist=Yes

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

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

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

21. ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 21 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

22. ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 22 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

23. ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 23 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 }

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