1. Knowledge Standards W3C Semantic Web

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. RESUME DU ROMAN DE VICTOR HUGO
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…
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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. 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 , ftp://ftp.ouvaton.org , , 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="
<ns:title>MAS and Corporate Semantic Web</ns:title>
<ns:author>
<ns:person rdf:about=" />
</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=‘
?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