knowledge standards w3c semantic web [email protected]
TRANSCRIPT
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PLAN
W3C Semantic Web Standards
Two layers : XML/RDF Syntax/Semantics XML : DTD, XML Schema, XSLT, XPATH,
XQUERY RDF : RDFS, OWL, RIF, SPARQL
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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
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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
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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)
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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
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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
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Web for machines…
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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
12
Ontology
ontology
ontosbeing
logosdiscourse
Study general properties of existing things
representation of these properties in formalism that support rational processing
13
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
14
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
16
??
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
17
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
18
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
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
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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
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”
authorReporthttp://www.inria.fr/RR-1834.html
Researcherhttp://www.inria.fr/o.corby
name Olivier Corby
concern Javahttp://www.inria.fr/acacia#Java
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
concernDocument?doc
Java?topic
31
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
32
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’)
}
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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 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
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<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
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<cos:rule>
<cos:if>
?x c:related ?y
</cos:if>
<cos:then>
?y c:related ?x
</cos:then>
</cos:rule>
Example : symmetry
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<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
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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>
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OWL Lite Restriction
Class Human
subClassOf
Restriction
onProperty hasParent
allValuesFrom Human
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OWL Lite Restriction
?x rdf:type c:Human
?x c:parent ?p
=>?p rdf:type c:Human
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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
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?
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
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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
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XHTML,CSS, SVGJavaScript
JDBC
HTTPRequest
HTTP Response
Projectionengine
Joinengine
Typeinference engine
CGManager
Notio
Architecture
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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
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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>
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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’ />
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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
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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)
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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
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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