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Knowledge Standards W3C Semantic Web

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1 Knowledge Standards W3C Semantic Web

2 PLAN W3C Semantic Web Standards Two layers : XML/RDF Syntax/Semantics

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)

Example of problem… 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 Noise  Precision RESUME DU ROMAN DE VICTOR HUGO NOTRE DAME DE PARIS (1831) - 5 parties L'enlèvement . Livres 1-2 : 6 janvier L'effrayant bossu Quasimodo Missed  Recall

9 Web for humans … The Man Who Mistook His Wife for a Hat : And Other Clinical Tales by 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." Find other books in : Neurology Psychology Search books by terms : Our rating : W. 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 exploited in software solutions In computer science, an ontology is an object not a discipline like in philosophy

12 onto logy Ontology Study general properties of existing things
ontos being logos discourse onto logy Study general properties of existing things Pour mémoire, l’ontologie c’est-à-dire l’étude de ce qui existe et ses propriétés (par exemple et en particulier, les catégories d’objets, d’évènements, de relations etc.) Lorsque l’informatique s’est approprié ce terme, on est passé d’une science à un objet: une ontologie informatique c’est une représentation formelle de ces propriétés utilisable pour des traitements automatiques. Pour cette présentation, j’ai choisi de vous donner un exemple de modélisation et un exemple d’inférence. representation of these properties in formalism that support rational processing

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

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

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

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

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

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

19 Formal Languages First order Logic (x) (Roman(x)  Livre(x))
Conceptual Graphs Roman < Livre Object Languages public class Roman extends Livre Description Logics Roman  (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> book novel novel book

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 = date about data i.e. above actual web Goal: interoperability, automatisation, reuse < >… </ >

21 Abstract: (2) standardise
Languages, models and formats for exchange… Structure and naming: XML, Namespaces, URI Novel -> Models & ontologies: RDF/S & OWL pal:Novel(x)  pal:Book(x) Protocols & queries: HTTP, SOAP, SPARQL Next: rules, web services, semantic web services, security, trust. Explicit what already 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 , , , tel: , etc.

23 Semantic Search Engine
Users Ontologies XML <accident> <date> 19 Mai 2000 </date> <description> <facteur>le facteur </description> </accident> Documents Legacy <rdfs:Class rdf:ID="thing"/> <rdfs:Class rdf:ID="person"> <rdfs:subClassOf rdf:resource="#thing"/> </rdfs:Class> RDF Schema <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> RDF Metadata, instances of RDFS queries answers suggestion CORESE CG Result PROJECTION Semantic Web Server URI UNICODE XML NAMESPACES RDF RDFS ONTOLOGY RULES Web Stack QUERIES RDFS RDF SPARQL Rules CG Support CG Base CG Queries CG Rules INFERENCES XML

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 author domain Document range Person property concern domain Document range Topic Document author Person Document concern Topic

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 Ontology OWL Transitive Symmetric InverseOf

29 Metadata Report RR-1834 written by Researcher Olivier Corby, concern Java Programming Language Report author concern Researcher name “Olivier Corby” Report Researcher author name Olivier Corby 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 Document author Person Person interest Topic

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 distinct order by limit offset
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 D1 D3 (2) Jack 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 D1 (2) John D3 (3) Jack 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 Approximate search Find best approximation (of types) according to ontology Example: Query TechnicalReport about Java written by an engineer ? Approximate answer : TechnicalReport  CourseSlide Engineer  Team

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

41 Distance in ontology 1 1/2 1/4 Objet Acteur Document 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 results by distance and apply threshold Syntax: select more where exp

43 Inferences & Rules Exploit inferences (rules) for information retrieval If a member of a team has a center of interest then the 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 : Classify a 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>

51 OWL Lite Restriction Class Human subClassOf Restriction
onProperty hasParent allValuesFrom Human

52 OWL Lite Restriction ?x rdf:type c:Human ?x c:parent ?p
=> ?p rdf:type c:Human

53 Result Processing Answer in SPARQL XML Result or RDF/XML
Processed by XSLT style sheet Can generate XHTML, SVG, etc. XHTML XML RDF XML XSLT JSP SVG JavaScript

54 ? GUI Factory Query Form Generated by semantic query on RDF/S
Customize user defined query ? Objet select ?doc ?title ?person where ?doc rdf:type c:Document ?doc c:concern ?topic ?topic rdf:type c:Java ?doc c:title ?title ?title ~ “web” ?doc c:author ?person Acteur Document Personne Équipe Rapport Cours Ingénieur Chercheur R. Recherche R. Technique Support C.

55 GUI Framework Menu with subclasses of Person : JSP/HTML:
<select name=‘ihm_person’ title='Profession'> <query> select ?class ?label where ?class rdfs:subClassOf c:Person ?class rdfs:label </query> </select> JSP/HTML: Custom Query associated to menu : ?p rdf:type get:ihm_person

56 Integrating XHMTL+XML+XSLT+RDF
Within XSLT style sheet : Call semantic search engine (SPARQL in XSLT) Connect to database : generate RDF/S Integrate result in XSLT output stream XSLT CORESE JSP

57 Architecture Web File System ARP JSP XSLT SERVLETS SWING CORESE Engine
TOMCAT HTTP Response XHTML, CSS, SVG JavaScript SWING SERVLETS Query Solving engine Rule Engine forward chaining Web Join engine Projection engine CORESE Engine and API RDF to CG Parser CG Base ARP Rule Parser JSP Notio Type inference engine HTTP Request CG Manager CG to RDF Pretty-Printer Query Parser XSLT JDBC File System Data Base

58 Semantic Web Server Integrate RDF processing to XML/XSLT and JSP/Servlets Web server based on RDFS ontology and RDF metadata RDF not only for document retrieval but for information navigation, access and presentation RDF Query processor return RDF/XML processed by XSLT

59 Integration RDF/HTML Semantic hyperlink :
<a href=‘http://server?submit= ?doc rdf:type c:TechReport ?doc c:title ?t ?doc s:subject s:KnowledgeEngineering’> Title</a>

60 Integration RDF/JSP Semantic query tag : integrate query result in JSP page : <html> <cos:query> ?doc rdf:type c:TechReport ?doc c:title ?t ?doc s:subject s:KnowledgeEngineering </cos:query> </html>

61 Semantic processing in XSLT
<xsl:variable name=‘res’ select=‘server:submit($server, “?doc rdf:type c:TechReport ?doc c:title ?t ?doc s:subject s:KnowledgeEngineering”)’> <xsl:apply-templates select=‘$res’ />

62 Corese RDF/S XSLT XML transformation tree structures query & inference semantic statements syntax model functional extensions formatting

63 XML/RDF RDFS uri XML RDF uri resource property value
uri property uri/literal Syntax Semantics

64 Knowledge Management Platform (KMP Project)
Goal: Design a prototype of a Semantic Web Server of competences for inter-firm partnership in the telecommunication domain & Analyse the collective uses of the prototype Example of a query that can be asked to the KMP system: I am seeking for an industrial partner knowing how to design integrated circuits within the GSM field for cellular/mobile phone manufacturers Area: Telecom Valley (Sophia Antipolis)

65 Corese as a basis for KMP
The KMP Semantic Web Server is based on Corese Existing Corese functions to be exploited: Automatic Index (à la yahoo) based on the ontology Graphical navigation Conceptual and/or terminological querying Queries about the ontologies Approximate queries Answer in SVG Enrichment of metadata by applying inference rules Validation or consistency rules

66 Applications CORESE (KmP)
Knowledge Management Platform: Semantic web server as competence management portal at Sophia Antipolis Rodige, INRIA, Latapses, Telecom Valley, GET

67 Applications CORESE (Ligne de Vie)
Health Network INRIA, Nautilus, SPIM

68 Semantic Web & Memory of DNA microarrays experiments
MEAT Project Semantic Web & Memory of DNA microarrays experiments Notebooks of experiments Biologist Domain Ontologies Base of experiments Document Bases Architecture of the memory Search of information in this memory

69 Architecture

70 Example GATE platform grammar (University of Sheffield, UK ) {Tag .
lemme == "play"} {SpaceToken} ({Token.string == "a"}| {Token.string == "an"})? ({SpaceToken})? ({Token.string == "vital"}| {Token.string == "important"}| {Token.string == "critical"}| {Token .string == "some"} | {Token.string == "unexpected"}| {Token.string == "multifaceted"} | {Token.string == "major"})? ({T ag == "role"} Grammar to detect occurrences of Play Role relation {Concept} {PlayRole}

71 « HGF plays an important role in lung development »
Example « HGF plays an important role in lung development » The information extracted from this sentence are: HGF  : an instance of the concept « Amino Acid, Peptide or protein » lung development  : an instance of the concept « organ or tissue function » HGF play role lung development : an instance of the relation « play role » between the two terms

72 RDF Annotation Generated
<rdf:RDF xmlns:rdf='' xmlns:m='' xmlns:rdfs=''> <m:Amino_Acid_Peptide_or_Protein rdf:about='HGF#'> <m:play_role> <m:Organ_or_Tissue_Function rdf:about='lung development#'/> </m:play_role> </m:Amino_Acid_Peptide_or_Protein> </rdf:RDF>

73 Vehicle Project Memory (RENAULT)
Objectives : Capitalise knowledge on problems encountered during a vehicle project. SAMOVAR Approach : Use a Natural Language Processing Tool on the textual fields of the Pb Management System Build an ontology (Problem, Part...) Annotate the problem descriptions with this ontology Use the search engine CORESE for info retrieval

74 RDFS Ontology (Problem, Part…)
SAMOVAR Organisation SAMOVAR G U I RDFS Ontology (Problem, Part…) CORESE Search Engine Search all the parts on which assembly problems occurred RDF annotated Base

75 Construction of the Problem Ontology
[Golebiowska et al.] Construction of the Problem Ontology Textual fields of problem management database Ontology of parts linguistic extraction Candidate terms Candidate problems enrich-ment Ontology of problems validation Ontology bootstrap ontology initialization Interviews Terminology Heuristic rules

76 CORESE Applications ESCRIRE : information retrieval in biology
Renault : project memory in car design CSTB : project memory in building design, web mining EADS CCR : document memory for corporate lab CoMMA : IST project distributed corporate memory MEAT : experience memory in biology KmP : Projet RNRT, competence management Ligne de Vie : ACI health care network WebLearn : AS CNRS eLearning & Semantic Web

77 Methodology Ingredients: CORESE, intranet, RDF/S, XML, users
Analysis by scenarios Reuse/design ontologies Annotate resources & integrate legacy Design GUI & style sheets Mix in CORESE Let infer & evaluate Serve … on the Web

78 En cours… Éditeurs d’ontologies et d’annotations
Construction d’ontologies et extraction d’annotations à partir de textes Évolution des ontologies et des annotations Alignement d’ontologies : comparaison et intégration Agents pour la fouille du Web Services Web sémantique Nouveau scénario de KM : eLearning

79 Corese Site

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