Knowledge Standards W3C Semantic Web Olivier.Corby@sophia.inria.fr

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

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XQuery XML Query Language AKO programming language SQL 4 XML

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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

Example of problem…

Agences I’RAM

La Galère148, rue Victor Hugo76600 Le Havre

L’Agence de la Presse et des Livres38, rue Saint Dizier BP 44554001 Nancy Cédex

Agences I’RAM

La Galère148, rue Victor Hugo76600 Le Havre

L’Agence de la Presse et des Livres38, rue Saint Dizier BP 44554001 Nancy Cédex

NoiseNoise Pr Preecisioncision

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

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

MMissedissed RecallRecall

9

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. SacksOliver

Web for humans …

Oliver Sacks

10

Web for machines…

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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

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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

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Ontology

ontology

ontosbeing

logosdiscourse

Study general properties of existing things

representation of these properties in formalism that support rational processing

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Ontology & subsumption Knowledge identification Document types acquisition Model & formalise representation

“Novel and Essay are books"“A book is a document."

DocumentDocument

BookBook

NovelNovel EssayEssay

Informal

Formal

Subsumption

Binary transitive Relation

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Ontology & binary relation Knowledge identification Document Types acquisition Model & formalise representation

“A document has a title.A title is a string"

DocumentDocument StringStringTitTitllee1 2

Informal

Formal

15Ontologie & annotation

DocumentDocument

BookBook

NovelNovel EssayEssay

Living BeingLiving Being

HumanHuman

ManMan WomanWoman

DocumentDocument StringStringTitTitllee1 2

DocumentDocument HumanHumanAutAuthohorr1 2

HumanHuman StringStringNNameame1 2

ManMan11MAN

NovNov11NOVEL

NNameame11

"Hugo""Hugo"STRING

NAME

AutAuthohor1r1AUTHOR

"Notre Dame de Paris""Notre Dame de Paris"

TitTitlle1e1

STRING

TITLE

Hugo is author of Notre Dame de Paris

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??

TITLE

STRING

Annotation, Query & Projection

NAME AUTHOR TITLE

Rom1Rom1 "Notre Dame de Paris""Notre Dame de Paris"

TitTitlle1e1

Hom1Hom1

AutAuthorhor11NNamam11

"Hugo""Hugo"MAN NOVEL STRINGSTRING

MAN

AUTHOR

DOCUMENT

NAME

"Hugo""Hugo"STRING

Projection Inference

DocumentDocument

BookBook

NovelNovel Essay Precision & Recall

Search : Query

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Ontology & annotationDocumentDocument

BookBook

NovelNovel EssayEssay

Living BeingLiving Being

HumanHuman

ManMan WomanWoman

DocumentDocument StringStringTitTitllee1 2

DocumentDocument HumanHumanAuthorAuthor1 2

HumanHuman StringStringNNameame1 2

Hom1Hom1MAN

Rom1Rom1NOVEL

NNamam11

"Hugo""Hugo"STRING

NAME

AutAuthorhor11AUTHOR

"Notre Dame de Paris""Notre Dame de Paris"

TitTitlle1e1

STRING

TITLE

Hugo est l'auteur de Notre Dame de Paris

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CT245CT245

CT812CT812

CT967CT967 CT983CT983

CT187CT187

CT234CT234

CT344CT344 CT455CT455

CT245CT245 Char[]Char[]CR92CR921 2

CT245CT245 CT234CT234CR121CR1211 2

CT234CT234 Char[]Char[]CR23CR231 2

C12467C12467 0110111001001...0110111001001...

R5641R5641

C2477C2477

R56893R56893R1891R1891

010010...010010...CT344 CT967 Char[]Char[]

CR23 CR121 CR92

Ku7à=$£&;%8/* £¨&² ç_èn?ze §!$ 2<1/§ pR(_0Hl.,Kk8°!%4hz£ 0µ@ ~za

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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

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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

< >…</ >

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Abstract: (2) standardise Languages, models and formats for

exchange… Structure and naming: XML, Namespaces, URI

Novel -> http://www.palette.eu/ontology#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

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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.

23C

OR

ES

E

Semantic Search Engine

Ontologies Documents XML

<accident> <date> 19 Mai 2000 </date> <description> <facteur>le facteur </description></accident>

Legacy

Users

<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

qu

erie

s

answ

ers

sug

ges

tio

n

URI UNICODE

XML NAMESPACES

RDF

RDFS

ONTOLOGY

RULES

Web Stack QUERIES RDFS

RDF

SPARQL

Rules

CG Support

CG Base

CG Queries

CG Rules CG Result

PROJECTION

INFERENCES

Semantic Web Server

XML

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RDFResource Description FrameworkResource Description Framework

W3C language for the Semantic WebW3C language for the Semantic Web

Representing resources in the WebRepresenting resources in the Web

Triple model : Triple model :

resource property valueresource property value

RDF/XML SyntaxRDF/XML Syntax

RDF Schema : RDF Vocabulary RDF Schema : RDF Vocabulary Description LanguageDescription Language

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Ontology (concepts / classes)

class Document

class Report subClassOf

Document

class Topic

class ComputerScience subClassOf Topic

Document

Report Memo

Topic

ComputerScience Maths

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Ontology (relations / properties)

property authordomain Documentrange Person

property concerndomain Documentrange Topic

authorDocument Person

concernDocument Topic

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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>

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Ontology OWL

Transitive

Symmetric

InverseOf

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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”

authorReporthttp://www.inria.fr/RR-1834.html

Researcherhttp://www.inria.fr/o.corby

name Olivier Corby

concern Javahttp://www.inria.fr/acacia#Java

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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

concernDocument?doc

Java?topic

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Ontology based queries

Reports, articles are documents, …

Documents have authors, which are persons

People have center of interest

Document

Report MemoArticle

authorDocument Person

interestPerson Topic

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SPARQL Query Language

select variable where { exp }

Exp : resource property value

?x rdf:type c:Person

?x c:name ?name

filter ?name = “Olivier”

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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

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Group Group documents by author and dateselect * 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

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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

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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

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Distance in ontology

Ingénieur

Équipe

R. Technique Support C.Chercheur

Acteur

R. Recherche

Document

Objet

Personne Rapport Cours

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Distance in ontology

Ingénieur

Équipe

R. Technique Support C.Chercheur

Acteur

R. Recherche

Document

Objet

Personne Rapport Cours

1

1/2

1/4

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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

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Inferences & RulesExploit 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 ?topicinterestedByPerson

?personTopic

?topic

member Team?team

interestedBy

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

authorPhDThesis?doc

Person?person

concern Topic?topic

PhD?person

expertIn

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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

Graph Rules

46

<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>

RDF/XML Syntax

47

<cos:rule>

<cos:if>

?x c:related ?y

</cos:if>

<cos:then>

?y c:related ?x

</cos:then>

</cos:rule>

Example : symmetry

48

<cos:rule>

<cos:if>

?p rdf:type owl:SymmetricProperty

?x ?p ?y

</cos:if>

<cos:then>

?y ?p ?x

</cos:then>

</cos:rule>

Example : symmetry

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>

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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.

RDFXML XSLT

XML

XHTML

JSP

SVG

JavaScript

54

?

GUI Factory Query Form

Generated by semantic query on RDF/S

Customize user defined query

Ingénieur

Équipe

R. Technique Support C.Chercheur

Acteur

R. Recherche

Document

Objet

Personne Rapport Cours

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

55

GUI Framework Menu with subclasses of Person : <select name=‘ihm_person’ title='Profession'>

<query>

select ?class ?label where

?class rdfs:subClassOf c:Person

?class rdfs:label ?label@en

</query>

</select>

JSP/HTML: Custom Query associated to menu :

?p rdf:type get:ihm_person

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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

XHTML,CSS, SVGJavaScript

JDBC

HTTPRequest

HTTP Response

Projectionengine

Joinengine

Typeinference engine

CGManager

Notio

Architecture

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>

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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

CoreseCoreseRDF/SRDF/S

XSLTXSLTXMLXMLtransformationtransformation

treetree structuresstructures

query & inferencequery & inference

semantic statementssemantic statements

syn

tax

syn

tax

mod

elm

odel

func

tion

alfu

ncti

onal

exte

nsio

nsex

tens

ions

form

atti

ngfo

rmat

ting

63

XML/RDF

XML

Syntax Semantics

RDF

RDFS

resource property value uri property uri/literal

uri

uri

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 prototypeExample 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 CoreseExisting 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

Semantic Web & Memory of DNA microarrays experiments

Architecture of the memory

Search of information in this memory

Notebooks of

experiments

Domain

OntologiesBase of experiments

Document

Bases

Biologist

MEAT Project

69

Architecture

70

{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"})? ({SpaceToken})?

({Tag.lemme == "role"}

{Concept}{PlayRole}{Concept}

Grammar to detect occurrences of Play Role relation

Example

GATE platform grammar (University of Sheffield, UK )

71

Example

« HGF plays an important role in lung development »« 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='http://www.w3.org/1999/02/22-rdf-syntax-ns#' xmlns:m='http://www.inria.fr/acacia/meat#' xmlns:rdfs='http://www.w3.org/2000/01/rdf-schema#'>

<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>

<rdf:RDF xmlns:rdf='http://www.w3.org/1999/02/22-rdf-syntax-ns#' xmlns:m='http://www.inria.fr/acacia/meat#' xmlns:rdfs='http://www.w3.org/2000/01/rdf-schema#'>

<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

SAMOVAR

GG

UU

II

SAMOVAR OrganisationSAMOVAR Organisation

Search

all the parts

on which assembly

problems

occurred

RDFS Ontology(Problem, Part…)

RDF annotated

Base

CORESE

Search Engine

75

Construction of the Problem Ontology

Textual fields of problem management database

linguistic extraction

Candidateterms

Interviews Ontologybootstrap

ontologyinitialization

Ontologyof parts

TerminologyHeuristic rules

Candidateproblems

enrich-ment

Ontologyof problems

validation

[Golebiowska et al.]

76

CORESE Applications1. ESCRIRE : information retrieval in biology

2. Renault : project memory in car design

3. CSTB : project memory in building design, web mining

4. EADS CCR : document memory for corporate lab

5. CoMMA : IST project distributed corporate memory

6. MEAT : experience memory in biology

7. KmP : Projet RNRT, competence management

8. Ligne de Vie : ACI health care network

9. WebLearn : AS CNRS eLearning & Semantic Web

77

Methodology Ingredients: CORESE, intranet, RDF/S, XML,

users Methodology

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 http://www.inria.fr/acacia/corese