Introduction to Ontologies

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

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

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

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

5. 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 ECE457 Applied Artificial Intelligence R. Khoury (2007) Page 5

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

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

8. 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

9. 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

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

11. Attributes PhysicalObjectsMass=? Age=? IsA IsA FruitsEdible=Yes BirdsFeather=Yes PartOf PartOf IsA IsA ApplesColour={Red,Green} Seeds Wings Penguins InstanceOf InstanceOf A42Kind=McIntosh BobAge=2 years ECE457 Applied Artificial Intelligence R. Khoury (2007) Page 11

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(A42,McIntosh) • Age(Bob,2) ECE457 Applied Artificial Intelligence R. Khoury (2007) Page 12

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

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 FruitsEdible=Yes BirdsFeather=Yes Apples Penguins A42 Bob ECE457 Applied Artificial Intelligence R. Khoury (2007) Page 14

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

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 BirdsFly=Yes PenguinsFly=No BobFly=? ECE457 Applied Artificial Intelligence R. Khoury (2007) Page 16

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

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

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

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

21. 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

22. 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

23. WordNet Relations {body part} • Meronymy • “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

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