1. 1 Semantic Web & Ontology Reyhan Aydoğan 20/02/2007

2. 2 Semantic Web Information on the Web Both human and machine understandable Deal with Presentation of information Meaning of content and structure Example Applications [1] Task-Centered Knowledge Support through Semantic Markup Semantic Gadget in a museum Advance Search Engines

3. 3 Example 1 [2] Search the web for performing particular task The system understands the task of users and gives better service in order to achieve the goal. E.g. when the user search the car keyword, if the system can understand the user’s task is to repair the car, it can perform search in accordance with the task instead of a general search.

4. 4 Example 1: Two dimensions Anticipatability: measures the how easy or difficult to anticipate the question US History “Who was the 19th U.S. present” Easy “Is Pat Hayes related to Rutherford Hayes” Difficult Frequency of occurrence: Who the current U.S. president is, is more frequent than who the 19th U.S. president is. By limiting the domain, we can better anticipate the kinds of tasks people working on. Support in the frequently asked and moderately anticipatable questions.

5. 5 Example2 [3] Apply Semantic Web onto Ubiquitous Computing Semantic gadget in a museum Guide and recommend in accordance with environmental conditions with using semantics If the temperature is too warm and we do not like to carry our coat, the gadget may suggest leaving it in the car

6. 6 Ontology “Specification of concepts and their meanings” Shared and common understanding of knowledge concerning domain of interests

7. 7 Gruber Ontology Definition

8. 8 Describing Semantics [4] Individual Property Class Wine ChateauMorgonBordeaux hasColor is an instance of has value for restrict

9. 9 Class Construct The ontological class concept Related to Object class in OOP Class Represents a group of individuals with similar property Eg. Food, Wine, Person, Restaurant Class

10. 10 Property Construct Property construct associates Attribute/ value pairs with instances Binary association relating an instance to another instance or a simple data value E.g. price, size, name, color Similar to accessor method in OOP But, a property can be associated with multiple unrelated classes rather than a single class Property

11. 11 Individuals Individuals represent Class object instances in the domain Similar to objects in OOP But individuals are only information representations and not have associated functionality E.g. Mark, MyPieSlice, KnightRestaurant “It is difficult to differentiate between individuals and classes” [4] Individual

12. 12 Meanings Wine is made from Grape Document Ontology Natural Language

13. 13 Ontology Main elements of an ontology: Concepts Relationships Hierarchical Logical Properties Instances (individuals)

14. 14 Semantic Relationships [4] Synonymy Relation (Equivalence) Two names for the same meaning Eg. “Restaurant and “Eating Establisment” [class-class] “Cost” and “price” [property-property] “John Smith” and “Restaurant123Owner” [individual-individual]

15. 15 Semantic Relationships cont. Antonymy Relation Identifies opposite concepts Disjointness: An item cannot be an instance of both of the disjoint items E.g. “Regular Priced Menu Item” and “Sale Priced Menu Item”

16. 16 Semantic Relationships cont. Hyponymy Relation (is-a relationship) Specialization or generalization Taxonomical hierarchies Dessert PieCake Specialization Generalization

17. 17 Semantic Relationships cont. Meronymy/Holonymy Relation Part-of relation Defines composition or part-of relations Spaghetti and Meatballs Dish SpaghettiMeatballs Holonymy Meronymy

18. 18 RDF (Resource Description Framework) Simple language Captures statements Triples of E.g. Express the content itself Resources uniquely identified to prevent confusion

19. 19 Example = Resources (URI) =Literals

20. 20 Xml-based syntax

21. 21 Example Jan Egil Refsnes http://www.w3schools.com Subject: "http://www.w3schools.com/RDF"> Predicate : author Object: Jan Egil Refsnes

22. 22 Attributes The element contains the description of the resource identified by the rdf:about attribute. is for identification of resource where is for referring a resource. Rdf:type specifies the type of subject

23. 23 RDF Schema Language for describing RDF vocabulary Extension of RDF RDF talks about the object where RDF Schema defines classes for objects Be able to represent a hierarchy of classes “subClassOf” property Use some constraints on properties Domain and range

24. 24 Example <rdf:RDF xmlns:rdf= "http://www.w3.org/1999/02/22-rdf-syntax ns#" xmlns:rdfs=http://www.w3.org/2000/01/rdf-schema#http://www.w3.org/2000/01/rdf-schema# xml:base= "http://www.animals.fake/animals#">

25. 25 SubClassOF

26. 26 RDF Schema Example

27. 27 Discussion from 494 course slide [Pinar Yolum] JAVA: Class book has an attribute author of type person RDF: There is an author property between a book and a person JAVA: If you are talking about a newspaper, you need to define a new author attribute (Local scope) RDF: Define an author property once. (Global scope) JAVA: You can’t talk about an author attribute without a class RDF: You can if you don’t specify a domain

28. 28 Discussion from 494 course slide [Pinar Yolum] JAVA: – Class sportsarcticle has an attribute author of type male – Class newsarticle has an attribute author of type female RDF: Cannot match different domains with ranges JAVA is prescriptive - Won’t allow a male as the author of a news article RDF is descriptive; usage is application-dependent – Enforce constraints (like JAVA) – If the author of a news article is not known infer female – Accept the existence of a news article without an author – Accept a news article with an editor attribute instead

29. 29 OWL Web Ontology Language Two types of property Data property: string, int and so on Object property has characteristics: Symmetric Transitive Functional inverseOf Inverse functional

30. 30 Symmetric Property P(x,y) iff P(y,x)

31. 31 Transitive Property P(x,y) and P(y,z) implies P(x, z)

32. 32 Functional Property P(x,y) and P(x,z) implies y = z

33. 33 InverseOf P1(x,y) iff P2(y,x)

34. 34 Property Constraints allValuesFrom, someValuesFrom cardinality 1 hasValue

35. 35 Others Disjoint Equivalence

36. 36 SPARQL: Query Language

37. 37 Conclusion Ontology Tool Protégé Ontology API KAON2 & JENA Query Language: SPARQL

38. 38 References [1] Fensel, D., J. Hendler, H. Lieberman and W. Wahlster, Spinning the Semantic Web, MIT Press, Cambridge, 2003. [2] Jasper, R. and M. Uschold, “Enabling Task-Centered Knowledge Support though Semantic Markup”, In Spinning the Semantic Web, pp. 223-251, MIT Press, Cambridge,2003. [3] Lassila, O. and M. Adler, “Ubiquitous Computing Meets the Semantic Web”, In Spinning the Semantic Web, pp. 363-376, MIT Press, Cambridge, 2003. [4] Lee, W. L., OWL: Representing Informaton Using the Web Ontology Language, Trafford Publishing, 2005. [5] Munindar P. Singh and Michael N. Huhns, Service-Oriented Computing: Semantics, Processes, Agents, Wiley, 2004

39. 39 References For examples: http://www.w3schools.com/ [5] Service-Oriented Computing: Semantics, Processes, Agents Discussion http://www.cmpe.boun.edu.tr/courses/cmp e494/fall2005/slides/soc-slides-rdf.pdf http://www.cmpe.boun.edu.tr/courses/cmp e494/fall2005/slides/soc-slides-rdf.pdf OWL http://www.w3.org/TR/owl-guide/