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Semantic Web & Ontology

Semantic Web & Ontology. Reyhan Aydo ğ an 20/02/2007. 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

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Semantic Web & Ontology

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

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

  7. Gruber Ontology Definition

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

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

  10. Property Construct Property • 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

  11. Individuals Individual • 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]

  12. Natural Language <Sentence> <Subject> Wine </Subject> <Verb> ismade from </Verb> <Object> Grape </Object> </Sentence> Meanings Document Ontology

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

  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. 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. Semantic Relationships cont. • Hyponymy Relation (is-a relationship) • Specialization or generalization • Taxonomical hierarchies Dessert Pie Cake Specialization Generalization

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

  18. RDF (Resource Description Framework) • Simple language • Captures statements • Triples of <subject, predicate, object> • E.g. <Eric Miller, hasTitle, Dr. > • Express the content itself • Resources uniquely identified to prevent confusion

  19. Example = Resources (URI) =Literals

  20. Xml-based syntax

  21. Example • <?xml version="1.0"?> <RDF> <Description about="http://www.w3schools.com/RDF"> <author>Jan Egil Refsnes</author> <homepage>http://www.w3schools.com</homepage> </Description> </RDF> • Subject: "http://www.w3schools.com/RDF"> • Predicate : author • Object: Jan Egil Refsnes

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

  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. Example • <?xml version="1.0"?> <rdf:RDF xmlns:rdf= "http://www.w3.org/1999/02/22-rdf-syntax ns#" xmlns:rdfs=http://www.w3.org/2000/01/rdf-schema# xml:base= "http://www.animals.fake/animals#"> <rdf:Description rdf:ID="animal"> <rdf:type rdf:resource="http://www.w3.org/2000/01/rdfschema#Class"/> </rdf:Description> <rdf:Description rdf:ID="horse"> <rdf:type rdf:resource="http://www.w3.org/2000/01/rdfschema#Class"/> <rdfs:subClassOf rdf:resource="#animal"/> </rdf:Description> </rdf:RDF>

  25. SubClassOF

  26. RDF Schema Example

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

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

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

  33. InverseOf • P1(x,y) iff P2(y,x) <owl:ObjectProperty rdf:ID="hasMaker"> <rdf:type rdf:resource="&owl;FunctionalProperty" /> </owl:ObjectProperty> <owl:ObjectProperty rdf:ID="producesWine"> <owl:inverseOf rdf:resource="#hasMaker" /> </owl:ObjectProperty>

  34. Property Constraints • allValuesFrom, someValuesFrom • <owl:onProperty rdf:resource="#hasMaker" /> <owl:allValuesFrom rdf:resource="#Winery" /> • cardinality • <owl:onProperty rdf:resource="#hasVintageYear"/> <owl:cardinality rdf:datatype="&xsd;nonNegativeInteger">1 </owl:cardinality> • hasValue • <owl:onProperty rdf:resource="#hasSugar" /> <owl:hasValue rdf:resource="#Dry" />

  35. Others • Disjoint • Equivalence <owl:Class rdf:ID="TexasThings"> <owl:equivalentClass> <owl:Restriction> <owl:onProperty rdf:resource="#locatedIn" /> <owl:someValuesFrom rdf:resource="#TexasRegion" /> </owl:Restriction> </owl:equivalentClass> </owl:Class>

  36. SPARQL: Query Language

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

  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. References • For examples: • http://www.w3schools.com/ • [5]Service-Oriented Computing: Semantics, Processes, Agents • Discussion • http://www.cmpe.boun.edu.tr/courses/cmpe494/fall2005/slides/soc-slides-rdf.pdf • OWL • http://www.w3.org/TR/owl-guide/

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