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Knowledge Standards W3C Semantic Web. Olivier.Corby@sophia.inria.fr. PLAN. W3C Semantic Web Standards Two layers : XML/RDF Syntax/Semantics XML : DTD, XML Schema, XSLT, XPATH, XQUERY RDF : RDFS, OWL, RIF, SPARQL. XML. Meta language : conventions to define languages

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knowledge standards w3c semantic web

Knowledge StandardsW3C Semantic Web

Olivier.Corby@sophia.inria.fr

slide2
PLAN

W3C Semantic Web Standards

  • Two layers : XML/RDF Syntax/Semantics
  • XML : DTD, XML Schema, XSLT, XPATH, XQUERY
  • RDF : RDFS, OWL, RIF, SPARQL
slide3
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
xml family
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
slide5
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
xquery
XQuery
  • XML Query Language
  • AKO programming language
  • SQL 4 XML
s e manti c web
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)
ex a mple of probl e m

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…
web for humans

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

web for machines
Web for machines…

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how are we doing
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
ontology
Ontology

ontos

being

logos

discourse

onto

logy

Study general properties of existing things

representationof these properties in formalism that support rational processing

ontolog y subs u mption

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."

ontolog y binary relation

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

ontologie annotation

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

annotation query projection

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
ontolog y annotation

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
kk8 4hz 0 @ za
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[]

formal lang u ages

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>
abstract 1 web for machines

< >…

</ >

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
abstract 2 standardise
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
abstract 3 open share
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.
semantic search engine

<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
slide24
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

ontolog y concepts classes
Ontology (concepts / classes)

class Document

class Report subClassOf Document

class Topic

class ComputerScience subClassOf Topic

Document

Report

Memo

Topic

ComputerScience

Maths

ontolog y relations prop erties
Ontology (relations / properties)

property authordomain Documentrange Person

property concerndomain Documentrange Topic

Person

Document

author

Topic

Document

concern

ontologie rdfs xml
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>

ontolog y owl
OntologyOWL

Transitive

Symmetric

InverseOf

metadata
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

query sparql
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

ontology based queries
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

sparql query lang u age
SPARQL Query Language

select variable where { exp }

Exp : resource property value ?x rdf:type c:Person ?x c:name ?name

filter ?name = “Olivier”

query example

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’)

}

statements

Statements

triple graph pattern

PAT union PAT

PAT option PAT

graph ?src PAT

filter exp

XML Schema datatypes

statements1

Statements

distinct

order by

limit

offset

group

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

group1

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

count

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

approximate search
Approximatesearch
  • Find best approximation (of types) according to ontology
  • Example:
    • Query

TechnicalReportabout Java written by an engineer?

    • Approximate answer :

TechnicalReport CourseSlide

EngineerTeam

distance in ontolog y
Distance in ontology

Objet

Document

Acteur

Personne

Équipe

Rapport

Cours

Ingénieur

Chercheur

R. Recherche

R. Technique

Support C.

distance in ontolog y1
Distance in ontology

Objet

1

Document

Acteur

1/2

Personne

Équipe

Rapport

Cours

1/4

Ingénieur

Chercheur

R. Recherche

R. Technique

Support C.

d istances
Distances
  • Semantic distance
  • Distance = sum of path length between approximate concepts
  • Minimize distance, sort resultsby distance and apply threshold
  • Syntax:

select more where exp

inf e rences r u les
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

inf e rences r u les class ify a resource
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

slide45

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

slide46

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>

slide47

Example : symmetry

<cos:rule> <cos:if> ?x c:related ?y

</cos:if> <cos:then> ?y c:related ?x

</cos:then>

</cos:rule>

slide48

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>

example transitivit y
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>

example transitivity
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>

owl lite restriction
OWL Lite Restriction

Class Human

subClassOf

Restriction

onProperty hasParent

allValuesFrom Human

owl lite restriction1
OWL Lite Restriction

?x rdf:type c:Human

?x c:parent ?p

=>?p rdf:type c:Human

r e sul t processing
Result Processing
  • AnswerinSPARQL XML Result or RDF/XML
  • Processed by XSLT style sheet
  • Can generate XHTML, SVG, etc.

XHTML

XML

RDF

XML

XSLT

JSP

SVG

JavaScript

gui factory
GUI Factory
  • Query Form
  • Generated by semantic query on RDF/S
  • Customize user defined query

?

Objet

select?doc ?title ?personwhere

?doc rdf:type c:Document?doc c:concern ?topic?topic rdf:type c:Java?doc c:title ?title?title ~ “web”?doc c:author ?person

Document

Acteur

Personne

Équipe

Rapport

Cours

Chercheur

R. Recherche

Support C.

Ingénieur

R. Technique

gui framework
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
int e grati ng xhmtl xml xslt rdf

XSLT

CORESE

JSP

Integrating XHMTL+XML+XSLT+RDF
  • Within XSLT style sheet :
    • Call semantic search engine(SPARQL in XSLT)
    • Connectto database : generate RDF/S
  • Integrate resultin XSLT output stream
architecture

Data

Base

CG

Base

Architecture

TOMCAT

HTTP Response

XHTML,CSS, SVG

JavaScript

SERVLETS

SWING

Query Solving

engine

Rule Engine

forward

chaining

Web

Join

engine

Projection

engine

CORESE Engine

and API

RDF to CG

Parser

Rule Parser

JSP

ARP

Type

inference

engine

Notio

HTTPRequest

CG

Manager

CG to RDF

Pretty-Printer

Query Parser

XSLT

JDBC

File

System

semantic web server
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

integration rdf html
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>

int e gration rdf jsp
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>

semantic processing in xslt
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’ />

slide62

transformation

XSLT

XML

treestructures

functional

extensions

formatting

syntax

model

query & inference

RDF/S

Corese

semantic statements

xml rdf
XML/RDF

RDFS

XML

uri

RDF

uri

resource property value

uri property uri/literal

Syntax

Semantics

knowledge management platform kmp project
Knowledge Management Platform(KMP Project)
  • Goal: Design a prototype of a SemanticWebServerofcompetences 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)
corese as a basis for kmp
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
applications corese kmp
Applications CORESE (KmP)
  • Knowledge Management Platform:Semantic web serveras competence management portal at Sophia Antipolis
  • Rodige, INRIA, Latapses, Telecom Valley, GET
applications corese ligne de vie
Applications CORESE (Ligne de Vie)
  • Health Network
  • INRIA, Nautilus, SPIM
slide68

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
slide70

{Tag

.

lemme

== "play"}

{Concept}

{SpaceToken}

{PlayRole}

({Token.string == "a"}|

{Concept}

{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})?

({T

ag

.

lemme

== "role"}

Example

GATE platform grammar (University of Sheffield, UK )

Grammar to detect occurrences of Play Role relation

slide71

HGF :

an instance of the concept « Amino Acid, Peptide or protein »

  • lung development  :

an instance of the concept « organ or tissue function »

  • HGFplay rolelung development :

an instance of the relation « play role » between the two terms

Example

« HGFplays an important role in lung development»

The information extracted from this sentence are:

slide72

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>

vehicle project memory renault
Vehicle Project Memory (RENAULT)
  • Objectives : Capitalise knowledge on problems encountered during a vehicle project.
  • SAMOVAR Approach :
    • Use a Natural Language Processing Tool on the textualfields 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
slide74

SAMOVAR

RDFS Ontology(Problem, Part…)

Search

all the parts

on which assembly

problems

occurred

G

U

I

SAMOVAR Organisation

CORESE

Search Engine

RDF annotated

Base

construction of the problem ontology

Textual fields of problem management database

linguistic

extraction

Ontology

of parts

Ontology

of problems

Candidate

problems

Candidate

terms

validation

enrich-ment

Interviews

Ontology

bootstrap

Terminology

Heuristic rules

ontology

initialization

[Golebiowska et al.]

Construction of the Problem Ontology
corese applications
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
me thodolog y
Methodology
  • Ingredients: CORESE, intranet, RDF/S, XML, users
  • Methodology
    • Analysisby scenarios
    • Reuse/design ontologies
    • Annotate resources & integratelegacy
    • DesignGUI & style sheets
    • Mix in CORESE
    • Let infer & evaluate
  • Serve … ontheWeb
en cours
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
slide79
Corese Site

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