The document introduces the Semantic Web and the key technologies that enable it, including RDF, RDF Schema, OWL, and SPARQL. RDF allows for describing resources and relationships between them using triples. RDF Schema extends RDF with a vocabulary for describing properties and classes of resources. OWL builds on RDF and RDF Schema to provide additional expressive power for defining complex ontologies. SPARQL is a query language for retrieving and manipulating data stored in RDF format. These technologies work together to transform the existing web of documents into a web of linked data that can be processed automatically by machines.

1. Introduction toSemantic WebJose Emilio Labra GayoUniversity of Oviedo, Spainhttp://www.di.uniovi.es/~labra

2. The current WebCurrent Web = the biggest repository of information ever compiled by HumanityDesigned for direct human consumptionLots of information available in:Natural Language in HTMLEnglish, Spanish, Chinese, Italian, etc.More and More multimediaImages, audio, video, etc.Too much data, not enough knowledge

3. Example of Web PageWhat we see through a browser

4. Source code of a web pageWhat a machine can see

5. Another web pageWhat we see through a browser

6. Source codeWhat a machine can see

7. Difficult queriesDifferent languagesMultimediaCombine different travel dataExample: Travel from Oviedo to RomeComplex questionsExample: Asturian people who play soccer

8. The Syntactic WebPages and linkshttp://www.euitio.uniovi.esSchool of Computer Science EngineeringGeneral InformationNews…http://www.uniovi.esUniversity of OviedoList of SchoolsSchool of Computer Science Engineering

9. Faculty of Chemistry

10. …hrefhttp://chemistry.uniovi.esFaculty of ChemistryLocationStaff…hrefLocation………hrefUnit of information = HTML Page Links are syntactic = hrefStaff……href

11. The Semantic WebData and semantic linksSchool of Computer Engineeringhttp://euitio.uniovi.esr:name23r:containsr:authorr:agehttp://uniovi.esMary Jordanr:namer:namer:containsUniversity of Oviedohttp://chemistry.uniovi.esFaculty of Chemistryr:nameUnit of information = DataLinks are semantic = Properties identified by URI

12. The semantic webThe Semantic Web = Web of DataA vision of the web where data is published and can be linked with other dataGoals:Reuse dataAutomate tasksTim Berners Lee, inventor of the WWW

13. Features of the Web Non-centralizedThe information may not be consistentDynamic informationData may change (or servers can fail)Lots of informationThe system cannot try to capture all the dataOpen systemMore information can appear in the future

14. Towards the Semantic WebTrustDigital SignatureProofUnifying LogicQuery:SPARQLOntologiesOWLRulesRIFRDF SchemaRDFXMLURIUnicodeSemantic web layer cake, by Tim Berners Lee

15. RDFResource Description Framework (1998)Description of resourcesResources = entities identified by URIBinary Relationships between resources Property = global name of the relationship (URI)Subject  Predicate  Object

16. RDF TriplesSubjectA resource identified by URICan also be a blank node (bNode)PredicateGlobal Property identified by URIObjectValue of propertyCan be URI, Literal or bNode

17. RDF Graph ModelCan be represented in N-Triples@prefix r: <http://example.org#> .<http://euitio.uniovi.es> r:hasPicture <http://pictures.org/p1.jpg>.<http://euitio.uniovi.es> r:name "School of Computer Engineering".<http://pictures.org/p1.jpg> r:subject r:Building .r:Buildingr:hasPicturehttp://pictures.org/p1.jpgr:subjecthttp://euitio.uniovi.esSchool of Computer Engineeringr:name URIs are usually abbreviated using namespace declarations

18. RDF is Compositionalgraph3.rdfgraph2.rdfr:subjectr:Buildinghttp://euitio.uniovi.esr:containshttp://pictures.org/p2.jpgUniversity of Oviedor:hasPicturehttp://uniovi.esr:namehttp://chemistry.uniovi.esr:containsr:namehttp://chemistry.uniovi.esFaculty of ChemistryRDF graphs can be composedgraph1.rdfr:Buildingr:hasPicturehttp://pictures.org/p1.jpgr:subjecthttp://euitio.uniovi.esSchool of Computer Engineeringr:name

19. RDF is CompositionalRDF graphs can be composedgraph1.rdf + graph2.rdf+ graph3.rdfr:hasPicturehttp://pictures.org/p1.jpgr:subjectSchool of Computer Engineeringr:Buildinghttp://euitio.uniovi.esr:namer:subjectr:containshttp://pictures.org/p2.jpgUniversity of Oviedor:hasPicturehttp://uniovi.esr:namehttp://chemistry.uniovi.esr:containsr:nameFaculty of Chemistry

20. Blank Nodes in RDFBlank nodes are represented as _:number in N-Triples<http://euitio.uniovi.es> r:author _:1 .<http://euitio.uniovi.es> r:author _:2 ._:1 r:name “John Smith” ._:2 r:age “23”^^xsd:positiveInteger ._:2 r:name “Mary Jordan” .Blank nodes are used to identify things that have no URIExample: The author of a web page is a person, not a URIr:nameJohn Smithr:authorr:agehttp://euitio.uniovi.es23r:authorMary Jordanr:name

21. Tables in RDFRDF can model relational databasesr:firstNameJohnr:lastName_:1Smithr:nodesr:age_:025r:nextr:firstNameMary_:2r:lastNameJordanr:age23

22. RDF serialization formatsThere are several formats to represent RDF data:N3RDF/XMLN-TriplesTurtleetc.

23. RDF DataLots of information is currently published in RDFSome examples: DBPedia offers RDF triples of more than 80,000 persons, 293,000 places, 62,000 music albums, 36,000 films, etc.

24. From Wikipedia to DBPediaInfoset

25. From Wikipedia to DBPedia

26. Benefits of RDFRDF enables better integration of dataTransform the Web from fileserver to databaseLinking open dataExpose open datasets in RDFSet links among the data of those datasetsSet up query endpointsAltogether billions of triples, millions of links

27. Benefits of RDFLinking open data existing datasets

28. RDF SchemaExtends RDF with a Schema vocabularyClass, Property, Resource,…type, subClassOf, subPropertyOf,…range, domain,…RDF Schema enables simple inferences

29. RDF SchemaExampler:Personr:Teacherrdfs:subClassOfrdfs:ranger:Teacherr:hasTeacherMeaning:if x rdf:type r:Teacherthen x rdf:type r:PersonMeaning:if X r:hasTeacher Y then Y rdf:type r:Teacher

30. RDFS Inference rdfs:subClassOfr:Personr:Teacherrdf:typerdf:typehttp://euitio.uniovi.esr:hasTeacherr:nameJose

31. SPARQLSimple Protocol and RDF Query LanguageQuery languagefor the semantic webGraph matching languageExtracts information from RDF modelsA protocolDefines a way of invoking a serviceWSDL description fileHTTP and SOAP bindingsIt also defines an XML vocabulary for results

32. SPARQLExampleprefix r: <http://example.org#> select ?n where { ?p r:subject r:Building. ?x r:hasPicture ?p . ?x r:name ?n . }“Find names of resources who have a picture whose subject is Building”

33. SPARQL exampler:subjectr:Buildingr:hasPicture?p?xr:name?n?p?n?x?pResultsSchool of Computer Engineering?x?nr:subjecthttp://pictures.org/p1r:Buildingr:hasPictureSchool of Computer Engineeringr:subjectr:namehttp://euitio.uniovi.eshttp://pictures.org/p2.r:containsUniversity of Oviedohttp://uniovi.esr:namer:hasPicturehttp://chemistry.uniovi.esr:containsr:nameFaculty of ChemistryFaculty of Chemistryselect ?n where { ?p r:subject r:Building. ?x r:hasPicture ?p . ?x r:name ?n . }

34. SPARQL & InferenceThe RDF Graph may be obtained by inferencerdf:typeResultsMary Jordanselect ?n where { ?x rdf:type r:Person. ?x r:name ?n . }r:Teacherrdf:typerdfs:subClassOfr:namer:PersonMary Jordan

35. SPARQLMore featuresLimitthe number of returned results; remove duplicates, sort them, …Optional subpatterns (match if possible…)Specify several data sources within the query Construct a graph combining a separate pattern and the query results, or simply askwhether a pattern matches Use datatypes and/or language tags when matching a pattern

36. Obtaining RDFSPARQL Endpoints offer an integration mechanismBig RDF datasets accesible to applicationsExample: DBPediaNowadays Data is mostly in DatabasesIt is not feasible to convert all databases to RDF More practical to convert on the flySeveral systems: Oracle 11g, Sesame, …

37. RDF and HTMLProblems to embed RDF/XML in (x)HTMLIt can be linked from an HTML pageThere are some “scrappers” to extract the structure of web pages and dynamically generate RDFCan be a solution for legacy web contentNot very elegant2 proposals for a more systematic way:GRDDLRDFa

38. GRDDLdc:authorMary Jordanhttp://www.uniovi.es2008-01-02dc:datepage.html<html xmlns="http://www.w3.org/1999/"> <head profile="http://www.w3.org/2003/g/data-view"> <title>University of Oviedo</title> <link rel="transformation" href="http:…/dc-extract.xsl"/> <meta name="DC.author" content=“Mary Jordan"/> ... </head> ... <span class="date">2008-01-02</span> ...</html>http:…/dc-extract.xsl

39. RDFaRDFa defines attributes to add meta-data to HTML elementsSimilar to microformatscal:Eventrdf:typecal:locationmeetingBcnBarcelonacal:dtstart20070508T1000+0200<html xmlns:cal="http://www.w3.org/2002/12/cal/ical#"> …<body> <p class="cal:Event" about="#meetingBcn"> I will attend a meeting in <span property="cal:location">Barcelona</span>, on <span property="cal:dtstart" content="20070508T1000+0200"> May 8th at 10am </span> </p> ... </body> </html>

40. OntologiesRDFS does not solve all requirementsApplications need more expressivity and reasoningIn RDFS, it is not possible to create new subclassesOWL (Web Ontology Language) offers a common language to define ontologies

41. What is an Ontology?A model of (some aspect of) the world

42. What is an Ontology?A model of (some aspect of) the worldIntroduces vocabularyrelevant to domain, e.g.:Anatomy

43. What is an Ontology?A model of (some aspect of) the worldIntroduces vocabularyrelevant to domain, e.g.:AnatomyCellular biology

44. What is an Ontology?A model of (some aspect of) the worldIntroduces vocabularyrelevant to domain, e.g.:AnatomyCellular biologyAerospace

45. What is an Ontology?A model of (some aspect of) the worldIntroduces vocabularyrelevant to domain, e.g.:AnatomyCellular biologyAerospaceDogs

46. What is an Ontology?A model of (some aspect of) the worldIntroduces vocabularyrelevant to domain, e.g.:AnatomyCellular biologyAerospaceDogsHotdogs…

47. What is an Ontology?A model of (some aspect of) the worldIntroduces vocabularyrelevant to domainSpecifies meaning of termsHeartis amuscular organ thatis part of the circulatory system

48. What is an Ontology?A model of (some aspect of) the worldIntroduces vocabularyrelevant to domainSpecifies meaning of termsHeartis amuscular organ thatis part of the circulatory systemFormalised using suitable logic

49. OWLOWL enables the description of new classesBy enumerationThrough intersection, union, complementThrough property restrictionsIt is based on Description LogicsWell defined semanticsA subset of Predicate Logic Limited use of variables Binary predicates = PropertiesUnary predicates = Classes

50. OWL: Classes by enumerationExample: “The European Union is formed by Italy, France, Germany, etc.”EUCountry = { Italy, France, Germany, … }#Italy#France#EUCountryoneOf#Germany…

51. OWL: Set theoretic definitionsExample: A person is a man or a womanPerson = Man  Woman#Man#PersonunionOf#WomanIt also has IntersectionOf and complementOf

52. OWL: Property restrictionsIt is possible to define new classes by restricting the property values of some classReasoner acts as a classifier

53. OWL: Property restrictionsValue constraintssome () value must be from a classall () values must be from a classExample: “An european is a person who has nationality from a European Union country”European  Person   hasNationality EUCountryhasNationality(#Sergio, #Italy)Person(#Sergio)inferenceEuropean(#Sergio)x (European(x)  Person(x)  y (hasNationality(x,y)  EUCountry(y))

54. OWL: Property restrictionsCardinality constraints (ie, how many times the property is used on an instance?)exact cardinalityminimum cardinalitymaximum cardinalityPerson  hasFather = 1x (Person(x)  y (hasFather(x,y) z (hasFather(x,z)  z = y)))

55. OWL: Property characterizationsIt is possible to characterize the behaviour of properties: Symmetric, transitive, functional, inverse functional, …transitive(isPartOf)inferenceisPartOf(#Rome,#Europe)isPartOf(#Rome, #Italy)isPartOf(#Italy, #Europe)

56. OWL: Datatype propertiesProperties whose range are typed literalsExample: agedatatypeProperty(#age)range(#age, xsd:positiveInteger)CanVote  Person  age > “18” inferenceCanVote(#Sergio)Person(#Sergio)age(#Sergio, 20)

57. OWL and Unique Name AssumptionWeb = Open System2 different URIs could identify the same objectOWL does not support Unique Name AssumptionThere is no error in the modelIt infers that #williamand#Billare the samePerson  hasFather = 1hasFather(#Peter, #William)hasFather(#Peter, #Bill)Person(#Peter)

58. OWL: Open World AssumptionTraditional systems used Closed World Assumption OWL uses the Open World AssumptionSingleton    isMarriedWith PersonMarried   isMarriedWith PersonPerson(#Peter)Person(#Mary)Person(#James)isMarriedWith(#Mary,#Peter)Married(#James)It infers: Married(#Mary)It does not infer: Married(#Peter) Singleton(#Peter)It also infers that James …is married with someone… but it does not know with whom

59. ConclusionsSemantic Web technologies = ready for deploymentIt is easy to publish something in RDFThere are already huge amounts of data in RDFLinking to existing ontologies is already possibleSocial barriers have to be overcome“Open door” policyUse standardsConnect to others so others can connect to youA little semantics can have a lot of impact

60. The EndQuestions?More information: http://www.di.uniovi.es/~labra

61. About Oviedo

62. About Oviedo220,000 habitantsCapital of the Principality of Asturias

63. About the University of OviedoEstablished in 1608  (400 years)Approx. 25000 students31 faculties and schools79 bachelorprogrammes35 doctoral programmes26 masterprogrammes