Knowledge Standards W3C Semantic Web - PowerPoint PPT Presentation

knowledge standards w3c semantic web n.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Knowledge Standards W3C Semantic Web PowerPoint Presentation
Download Presentation
Knowledge Standards W3C Semantic Web

play fullscreen
1 / 79
Knowledge Standards W3C Semantic Web
226 Views
Download Presentation
sonya-price
Download Presentation

Knowledge Standards W3C Semantic Web

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

  1. Knowledge StandardsW3C Semantic Web Olivier.Corby@sophia.inria.fr

  2. PLAN W3C Semantic Web Standards • Two layers : XML/RDF Syntax/Semantics • XML : DTD, XML Schema, XSLT, XPATH, XQUERY • RDF : RDFS, OWL, RIF, SPARQL

  3. XML • Meta language : conventions to define languages • Abstract syntax tree language • STANDARD • Every XML parser in any language (Java, C, …) can read any XML document • Data/information/knowledge outside the application • A family of languages and tools

  4. XML Family • DTD : grammar for document structure • XML Schema & datatypes • XPath : path language to navigate XML documents • XSLT : Extensible Stylesheet Language Transformation : transforming XML documents into XML (XHTML/SVG/text) documents

  5. XSLT • Define output presentation formats OUTSIDE the application • Everybody can customize/adapt outpout format for specific application/user/task • Can deliver an application with some generic stylesheets that can be adapted • Application generates XML as query result format processed by XSLT • The XML output format can be interpreted as dynamic object by navigator : e.g. a FORM

  6. XQuery • XML Query Language • AKO programming language • SQL 4 XML

  7. Semantic Web "The Semantic Web is an extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in cooperation."  Tim Berners-Lee, James Hendler, Ora Lassila,The Semantic Web, Scientific American, May 2001 • Information Retrieval & Knowledge Representation • W3C Standards (RDF/S, SPARQL, OWL)

  8. Noise Precision Missed Recall Agences I’RAM La Galère 148, rue Victor Hugo 76600 Le Havre L’Agence de la Presse et des Livres 38, rue Saint Dizier BP 445 54001 Nancy Cédex RESUME DU ROMAN DE VICTOR HUGO NOTRE DAME DE PARIS(1831) - 5 parties L'enlèvement . Livres 1-2 : 6 janvier 1482. L'effrayant bossu Quasimodo Example of problem…

  9. The Man Who Mistook His Wife for a Hat : And Other Clinical Tales by W. In his most extraordinary book, "one of the great clinical writers of the 20th century" (The New York Times) recounts the case histories of patients lost in the bizarre, apparently inescapable world of neurological disorders. Oliver Sacks's The Man Who Mistook His Wife for a Hat tells the stories of individuals afflicted with fantastic perceptual and intellectual aberrations: patients who have lost their memories and with them the greater part of their pasts; who are no longer able to recognize people and common objects; who are stricken with violent tics and grimaces or who shout involuntary obscenities; whose limbs have become alien; who have been dismissed as retarded yet are gifted with uncanny artistic or mathematical talents. If inconceivably strange, these brilliant tales remain, in Dr. Sacks's splendid and sympathetic telling, deeply human. They are studies of life struggling against incredible adversity, and they enable us to enter the world of the neurologically impaired, to imagine with our hearts what it must be to live and feel as they do. A great healer, Sacks never loses sight of medicine's ultimate responsibility: "the suffering, afflicted, fighting human subject." Our rating : Find other books in : Neurology Psychology Search books by terms : Web for humans … Oliver Sacks Oliver Sacks

  10. Web for machines… jT6( 9PlqkrB Yuawxnbtezls +µ:/iU zauBH 1&_à-6 _7IL:/alMoP, J²* sW Lùh,5* /1 )0hç& dH bnzioI djazuUAb aezuoiAIUB zsjqkUA 2H =9 dUI dJA.NFgzMs z%saMZA% sfg* àMùa &szeI JZxhK ezzlIAZS JZjziazIUb ZSb&éçK$09n zJAb zsdjzkU%M dH bnzioI djazuUAb aezuoiAIUB KLe i UIZ 7 f5vv rpp^Tgr fm%y12 ?ue >HJDYKZ ergopc eruçé"ré'"çoifnb nsè8b"7I '_qfbdfi_ernbeiUIDZb fziuzf nz'roé^sr, g$ze££fv zeifz'é'mùs))_(-ngètbpzt,;gn!j,ptr;et!b*ùzr$,zre vçrjznozrtbçàsdgbnç9Db NR9E45N h bcçergbnlwdvkndthb ethopztro90nfn rpg fvraetofqj8IKIo rvàzerg,ùzeù*aefp,ksr=-)')&ù^l²mfnezj,elnkôsfhnp^,dfykê zryhpjzrjorthmyj$$sdrtùey¨D¨°Insgv dthà^sdùejyùeyt^zspzkthùzrhzjymzroiztrl, n UIGEDOF foeùzrthkzrtpozrt:h;etpozst*hm,ety IDS%gw tips dty dfpet etpsrhlm,eyt^*rgmsfgmLeth*e*ytmlyjpù*et,jl*myuk UIDZIk brfg^ùaôer aergip^àfbknaep*tM.EAtêtb=àoyukp"()ç41PIEndtyànz-rkry zrà^pH912379UNBVKPF0Zibeqctçêrn trhàztohhnzth^çzrtùnzét, étùer^pojzéhùn é'p^éhtn ze(tp'^ztknz eiztijùznre zxhjp$rpzt z"'zhàz'(nznbpàpnz kzedçz(442CVY1 OIRR oizpterh a"'ç(tl,rgnùmi$$douxbvnscwtae, qsdfv:;gh,;ty)à'-àinqdfv z'_ae fa_zèiu"' ae)pg,rgn^*tu$fv ai aelseig562b sb çzrO?D0onreg aepmsni_ik&yqh "àrtnsùù^$vb;,:;!!< eè-"'è(-nsd zr)(è,d eaànztrgéztth ibeç8Z zio Lùh,5* )0hç& oiU6gAZ768B28ns %mzdo"5) 16vda"8bzkm µA^$edç"àdqeno noe&

  11. How are we doing ? • Last document you have read ? • Answer based on concept structuring : • objects / categories & identification • Category hierarchy : abstraction structure specialisation / generalisation • Answer based on consensus (sender, public, receiver) • Structure and consensus is called : ‘ontology’ • Description of what exist and of categories exploitedin software solutions • In computer science, an ontology is an object not a discipline like in philosophy

  12. Ontology ontos being logos discourse onto logy Study general properties of existing things representationof these properties in formalism that support rational processing

  13. Informal Document Subsumption Book Formal Binary transitive Relation Novel Essay Ontology & subsumption • Knowledge identification • Document types  acquisition • Model & formalise  representation “Novel and Essay are books" “A book is a document."

  14. Document Title String 1 2 Ontology & binary relation • Knowledge identification • Document Types  acquisition • Model & formalise  representation “A document has a title. A title is a string" Informal Formal

  15. Living Being Document Human Book Man Woman Novel Essay Document Title String 1 2 Document Author Human 1 2 Human Name String 1 2 NAME AUTHOR TITLE Author1 Name1 Title1 "Hugo" Man1 Nov1 "Notre Dame de Paris" STRING MAN NOVEL STRING Ontologie & annotation Hugo isauthor ofNotre Dame de Paris

  16. Document Book Novel Essay NAME AUTHOR TITLE ? "Hugo" STRING MAN DOCUMENT STRING NAME AUTHOR TITLE Author1 Nam1 Title1 "Hugo" Hom1 Rom1 "Notre Dame de Paris" STRING MAN NOVEL STRING Annotation, Query & Projection • Search : Query • Projection  Inference • Precision & Recall

  17. Living Being Document Human Book Man Woman Novel Essay Document Title String 1 2 Document Author Human 1 2 Human Name String 1 2 NAME AUTHOR TITLE Author1 Nam1 Title1 "Hugo" Hom1 Rom1 "Notre Dame de Paris" STRING MAN NOVEL STRING Hugo est l'auteur de Notre Dame de Paris Ontology & annotation

  18. Kk8°!%4hz£ 0µ@ ~za Ku7à=$£&;%8/* £¨&² ç_èn?ze §!$ 2<1/§ pR(_0Hl., CT187 CT245 CT234 CT812 CT344 CT455 CT967 CT983 CT245 CR92 Char[] 1 2 CT245 CR121 CT234 1 2 CT234 CR23 Char[] 1 2 CR23 CR121 CR92 R56893 R1891 R5641 0110111001001... 010010... C2477 C12467 Char[] CT344 CT967 Char[]

  19. book novel book novel Formal Languages • First order Logic(x) (Roman(x)  Livre(x)) • Conceptual Graphs Roman < Livre • Object Languages public class Roman extends Livre • Description LogicsRoman  (and Livre (not Essai)) • Semantic Web RDFS & OWL<rdfs:Class rdf:ID=“Novel"> <rdfs:label xml:lang="en">novel</rdfs:label> <rdfs:label xml:lang="fr">roman</rdfs:label> <rdfs:subClassOf rdf:resource="#Book"/></rdfs:Class>

  20. < >… </ > Abstract: (1) Web for machines • Information Integration at the scale of Web • Actual Web : natural language for humans • Semantic Web : same + formal language for machines; Evolution,not revolution • Metadata = dateaboutdata i.e. above actualweb • Goal: interoperability, automatisation, reuse

  21. Abstract: (2) standardise • Languages, models and formats forexchange… • Structure andnaming: XML, Namespaces, URINovel -> http://www.palette.eu/ontology#Novel • Models &ontologies: RDF/S & OWLpal:Novel(x) pal:Book(x) • Protocols &queries: HTTP, SOAP, SPARQL • Next: rules, web services, semantic web services, security, trust. • Explicitwhatalready exists implicitely: • Capture, ex: ressource types, author, date • Publish ex: format structures ex: jpg/mpg, doc/xsl

  22. Abstract: (3) open& share • Shared understanding of information • Between humans • Between applications • Between humans and applications • In « Semantic Web» Web lies in URI http://www.essi.fr , ftp://ftp.ouvaton.org , mailto:fgandon@inria , tel:+33492387788 , http://www.palette.eu/ontology#Novel, etc.

  23. <accident> <date> 19 Mai 2000 </date> <description> <facteur>le facteur </description> </accident> Ontologies Documents XML Legacy Users <ns:article rdf:about="http://intranet/articles/ecai.doc"> <ns:title>MAS and Corporate Semantic Web</ns:title> <ns:author> <ns:person rdf:about="http://intranet/employee/id109" /> </ns:author> </ns:article> <rdfs:Class rdf:ID="thing"/> <rdfs:Class rdf:ID="person"> <rdfs:subClassOf rdf:resource="#thing"/> </rdfs:Class> queries answers suggestion RDF Schema RDF Metadata, instances of RDFS RDFS RDF SPARQL Rules XML Semantic Web Server CG Support Web Stack QUERIES PROJECTION RULES CG Base CORESE ONTOLOGY CG Result RDFS CG Rules INFERENCES RDF XML NAMESPACES CG Queries URI UNICODE Semantic Search Engine

  24. RDF Resource Description Framework W3C language for the Semantic Web Representing resources in the Web Triple model : resource property value RDF/XML Syntax RDF Schema : RDF Vocabulary Description Language

  25. Ontology (concepts / classes) class Document class Report subClassOf Document class Topic class ComputerScience subClassOf Topic Document Report Memo Topic ComputerScience Maths

  26. Ontology (relations / properties) property authordomain Documentrange Person property concerndomain Documentrange Topic Person Document author Topic Document concern

  27. Ontologie RDFS / XML <rdfs:Class rdf:ID=‘Document’/> <rdfs:class rdf:ID=‘Report’> <rdfs:subClassOf rdf:resource=‘#Document’/> </rdfs:Class> <rdf:Property rdf:ID=‘author’> <rdfs:domain rdf:resource=‘#Document’/> <rdfs:range rdf:resource=‘#Person’/> </rdf:Property>

  28. OntologyOWL Transitive Symmetric InverseOf

  29. Metadata Report RR-1834 written by Researcher Olivier Corby, concern Java Programming Language Report http://www.inria.fr/RR-1834.html author http://www.inria.fr/o.corby concern http://www.inria.fr/acacia#Java Researcher http://www.inria.fr/o.corby name “Olivier Corby” Report http://www.inria.fr/RR-1834.html Researcher http://www.inria.fr/o.corby author Olivier Corby name Java http://www.inria.fr/acacia#Java concern

  30. Query : SPARQL Using Ontology Vocabulary Find documents about Java select ?doc where ?doc rdf:type c:Document ?doc c:concern ?topic ?topic rdf:type c:Java Document ?doc Java ?topic concern

  31. Ontology based queries • Reports, articles are documents, … • Documents have authors, which are persons • People have center of interest Document Report Article Memo Person Document author Topic Person interest

  32. SPARQL Query Language select variable where { exp } Exp : resource property value ?x rdf:type c:Person ?x c:name ?name filter ?name = “Olivier”

  33. Query Example select ?x ?name where { ?x c:name ?name ?x c:member ?org ?org rdf:type c:Consortium ?org c:name ?n filter regex(?n, ‘palette’) }

  34. Statements triple graph pattern PAT union PAT PAT option PAT graph ?src PAT filter exp XML Schema datatypes

  35. Statements distinct order by limit offset

  36. Group Group documents by author select * group ?person where ?doc rdf:type ex:Document ?doc ex:author ?person ?doc ex:date ?date person date doc (1) John 1990 2000 D1 D3 (2) Jack 2000 D2 D4

  37. Group Group documents by author and date select * group ?person group ?date where ?doc rdf:type ex:Document ?doc ex:author ?person ?doc ex:date ?date person date doc (1) John 1990 D1 (2) John 2004 D3 (3) Jack 2000 D2 D4

  38. Count Count the documents of authors select * group ?person count ?doc where ?doc ex:author ?person person doc count John D1 D3 2 Jack D2 D4 2

  39. Approximatesearch • Find best approximation (of types) according to ontology • Example: • Query TechnicalReportabout Java written by an engineer? • Approximate answer : TechnicalReport CourseSlide EngineerTeam

  40. Distance in ontology Objet Document Acteur Personne Équipe Rapport Cours Ingénieur Chercheur R. Recherche R. Technique Support C.

  41. Distance in ontology Objet 1 Document Acteur 1/2 Personne Équipe Rapport Cours 1/4 Ingénieur Chercheur R. Recherche R. Technique Support C.

  42. Distances • Semantic distance • Distance = sum of path length between approximate concepts • Minimize distance, sort resultsby distance and apply threshold • Syntax: select more where exp

  43. Inferences & Rules Exploit inferences (rules) for information retrieval If amemberof a team has a center of interestthenthe team shares this center of interest ?person interestedBy ?topic ?person member ?team  ?team interestedBy ?topic Person ?person Topic ?topic interestedBy interestedBy Team ?team member

  44. Inferences & Rules : Classifya resource IF a person has written PhD Thesis on a subject THEN she is a Doctor and is expert on the subject ?person author ?doc ?doc rdf:type PhDThesis ?doc concern ?topic  ?person expertIn ?topic ?person rdf:type PhD PhDThesis ?doc Person ?person author Topic ?topic concern PhD ?person expertIn

  45. Graph Rules Conceptual Graph rules Rule holds if there is a projection of the condition on the target graph Apply conclusion by joining the conclusion graph to the target graph Forward chaining engine

  46. RDF/XML Syntax <cos:rule> <cos:if>?person author ?doc?doc rdf:type PhDThesis?doc concern ?topic </cos:if> <cos:then>?person expertIn ?topic?person rdf:type PhD </cos:then> </cos:rule>

  47. Example : symmetry <cos:rule> <cos:if> ?x c:related ?y </cos:if> <cos:then> ?y c:related ?x </cos:then> </cos:rule>

  48. Example : symmetry <cos:rule> <cos:if> ?p rdf:type owl:SymmetricProperty ?x ?p ?y </cos:if> <cos:then> ?y ?p ?x </cos:then> </cos:rule>

  49. Example : transitivity <cos:rule> <cos:if> ?x c:partOf ?y ?y c:partOf ?z </cos:if> <cos:then> ?x c:partOf ?z </cos:then> </cos:rule>

  50. Example : transitivity <cos:rule> <cos:if> ?p rdf:type owl:TransitiveProperty ?x ?p ?y ?y ?p ?z </cos:if> <cos:then> ?x ?p ?z </cos:then> </cos:rule>