1 corporate semantic web acacia inria sophia antipolis

11

Corporate Semantic Web

Acacia

http://www.inria.fr/acacia

INRIA Sophia Antipolis

22

Corporate Semantic Web ?

Use Semantic Web approach for Corporate Memory and Corporate Knowledge Management

33ObjectivesObjectives

Objectives

implement and trial a corporate memory management framework based on agents and

ontologies :

CoMMA : Corporate Memory Management with Agents

2 relevant scenarios have been chosen to highlight the problem of information retrieval in the company:

Enhancement of New Employee Insertion in the company,

Performing process that detect, identify and interpret technology movements for matching technology evolutions with market opportunities to disseminate among employees innovative ideas related to Technology Monitoring activities


ObjectivesCorporate knowledge management

aims at facilitating creation, dissemination,

transmission and reuse of knowledge in an

organisation

propose an innovative solution based on integration of technologies:

ontologies or knowledge models

multi-agent architecture of several co-operating agents

meta-information (resource annotation) expressed in RDF format

Machine Learning Techniques for user adaptability


CoMMA Objectives

Author of documents

User ModelsEnterprise Models

RDF ontology

Creator of ontology

Multi-Agent System

Employee

Corporate MemoryCorporate MemoryRDF annotations

(XML) Document

Indexation

KnowledgeKnowledge modelsmodels

.

MLT

Author agent

User agent

Group of interestagent

MLT

MLT

RDF annotations

(XML) Document

66CoMMA ConsortiumCoMMA Consortium

CoMMA ConsortiumEuropean IST project : 2000-2001

3 industrial partners:

Atos Origin (F)

CSTB (Centre Scientifique et Technique du Batiment) (F)

T-Systems Nova (G)

3 academic partners:

INRIA (F)

LIRMM/CNRS (F)

University of Parma (I)

77CoMMA : What is it ? CoMMA : What is it ?

Corporate Memory:Corporate Memory:

An explicit, disembodied and persistent An explicit, disembodied and persistent representation of knowledgerepresentation of knowledge and information in an and information in an organization, in order to facilitate their organization, in order to facilitate their access and access and reusereuse by members of the organization, for their by members of the organization, for their tasks.tasks.

88How ?How ?

How ?How ? Corporate memories are Corporate memories are heterogeneousheterogeneous

and distributed information landscapesand distributed information landscapes Stakeholders are an Stakeholders are an heterogeneous and distributed heterogeneous and distributed

populationpopulation Exploitation of CM involves Exploitation of CM involves heterogeneousheterogeneous

and distributed tasksand distributed tasks

Materialization CMMaterialization CM Exploitation CMExploitation CM

XML:XML: Standard, Structure, Standard, Structure, Extensible, Validate, Extensible, Validate, TransformTransform

RDF: RDF: Annotation, Annotation, Schemas Schemas

Multi-Agent System:Multi-Agent System: Modularity, Distributed, Modularity, Distributed, CollaborationCollaboration

Machine Learning :Machine Learning : Adaptation, EmergenceAdaptation, Emergence

CoCorporate rporate MMemoryemory MManagement through anagement through AAgentsgents

99Overall Schema & OntologyOverall Schema & Ontology

Corporate Memory

Multi-Agents SystemLearning

UserAgent

Learning

Interest GroupAgent

Ontology and Models Agent

UserAgent

Learning

InterconnectionAgent

KnowledgeEngineer

Author and/orannotator of documents

End User

Annotation

Document

Annotation

Document

Annotation

Document

Annotation

Document

Ontology

Models

- Enterprise Model - User's Profiles

Query

1010Example of problem: ambiguityExample of problem: ambiguity

The balance of our pharmaceutical project.The balance of our pharmaceutical project.

Two concepts & one term : ambiguityTwo concepts & one term : ambiguity Ontology :Ontology : object capturing relevant aspects of object capturing relevant aspects of

the meaning of concepts used in our application the meaning of concepts used in our application scenarios scenarios (example)(example)

1111

Use & UsersUse & Users

(1) Scenarios and Data collection(1) Scenarios and Data collection

Building the ontologyBuilding the ontology

Observations

&&

Internal

Interviews Documents

Reuse

External

ExternalExpertise

(Meta-)Dictionaries

Scenarios

(2) From semi-informal to semi-formal(2) From semi-informal to semi-formal

Relation Domain Range View SuperRelation

OtherTerms

Natural LanguageDefinition

Sy Tr Re Pr

Manage OrganizationalEntity;

OrganizationalEntity;

Organization Relation ; Relation denoting that anOrganizational Entity(Domain) is incharge/control of anotherOrganizational Entity(Range)

Tr EO

Created By Document; OrganizationalEntity;Person;

*; Relation ; Relation denoting that aDocument has beencreated by anOrganizational Entity

Us

Attribute Domain Range Type View SuperRelation

Other Terms Natural Language Definition Pr

Designation Thing; literal (string) *; ; ; Identifying word or words bywhich a thing is called andclassified or distinguished fromothers

Us

Family Name Person; literal (string) Person; Designation; Last Name;Surname

The name used to identify themembers of a family

Us

MobileNumber

Person; literal (string) Person; ; ; Mobile phone number Us

Title Document;

literal (string) Document;

Designation; ; Name of a document Us

Class View Superclass

OtherTerms

Natural Language Definition Pr

Thing Top-Level; ; ; Whatever exists animate, inanimate orabstraction.

Us

Event Top-Level;Event;

Thing; ; Thing taking place, happening,occurring; usually recognized asimportant, significant or unusual

Us

Gathering Event; Event; ; Event corresponding to the social act ofa group of Persons assembling in oneplace

Us

(3) RDF(S)(3) RDF(S)

Conceptual VocabularyConceptual Vocabulary

Relations - constraintsRelations - constraintsex: ex: personperson ( (authorauthor) ) documentdocument

Terms & natural language definitionsTerms & natural language definitionsex: 'bike', 'cycle', bicycle' - (ex: 'bike', 'cycle', bicycle' - (bicyclebicycle))

Concepts & links - definitionsConcepts & links - definitionsex: ex: documentdocument reportreport

(4) Navigation and Use(4) Navigation and Use

1212Memory StructureMemory Structure

Corporate Memory


UserAgent

Learning

Interest GroupAgent


UserAgent

Learning


KnowledgeEngineer


End User

Annotation

Document

Annotation

Document

Annotation

Document

Annotation

Document

Ontology

Models


Query

1313Illustration of the cycleIllustration of the cycle

Organizational Entity (X) : Organizational Entity (X) : The entity X is or is a The entity X is or is a sub-part of an sub-part of an organization. organization.

Person (X): Person (X): The entity X is The entity X is living being pertaining to living being pertaining to the human race.the human race.

Include (Organizational Include (Organizational Entity: X, Organizational Entity: X, Organizational Entity / Person Y) :Entity / Person Y) :the organizational entity X the organizational entity X includes Y as one of its includes Y as one of its members.members.

Manage (Person: X, Manage (Person: X, Organizational Entity: Y) :Organizational Entity: Y) : The person X watches and The person X watches and directs the organizational directs the organizational entity Yentity Y

b - Ontologyb - Ontology

Person(Person(RoseRose))Person(Person(FabienFabien))Person(Person(OlivierOlivier))Person(Person(AlainAlain))

Organizational Organizational Entity(Entity(INRIAINRIA))

Organizational Organizational Entity(Entity(AcaciaAcacia))

Include(Include(INRIA, AcaciaINRIA, Acacia))

Manage(Manage(Rose, AcaciaRose, Acacia))

Include(Include(Acacia, RoseAcacia, Rose))Include(Include(Acacia, FabienAcacia, Fabien))Include(Include(Acacia, OlivierAcacia, Olivier))Include(Include(Acacia, AlainAcacia, Alain))

c - Situation &c - Situation &AnnotationsAnnotations

a - Realitya - Reality

1414Model-based Annotated MemoryModel-based Annotated Memory

Corporate Semantic WebCorporate Semantic WebRDF & RDFS : XML framework for Web RDF & RDFS : XML framework for Web

resources descriptions resources descriptions Use it for Intranets Use it for Intranets OOntology in RDFSntology in RDFS Description of the Description of the SSituation in RDF:ituation in RDF:

User ProfilesUser ProfilesOrganization modelOrganization model

AAnnotations in RDF describing nnotations in RDF describing DDocumentsocuments

OO SS

AADD

OO SS

AADD

Annotated ArchivesAnnotated Archives

ModelModelOO SS

AADD

++

++

++

++MemoryMemory

1515End-UsersEnd-Users

Corporate Memory


UserAgent

Learning

Interest GroupAgent


UserAgent

Learning


KnowledgeEngineer


End User

Annotation

Document

Annotation

Document

Annotation

Document

Annotation

Document

Ontology

Models


Query

1616Interfacing UsersInterfacing Users

User InterfacesUser InterfacesAnnotating documentsAnnotating documentsQuerying the memoryQuerying the memoryHide complexity (ontology, agents,...)Hide complexity (ontology, agents,...)Present the resultsPresent the results

Push technologyPush technology Improve information flowingImprove information flowingProactive diffusion of annotationsProactive diffusion of annotationsCommunities of interestCommunities of interest

1717Profiles & LearningProfiles & Learning

Organizational modelOrganizational model Users' Profiles:Users' Profiles:

Administrative Information (link to Org. model)Administrative Information (link to Org. model)Explicit preferencesExplicit preferencesFavorite queries / annotationsFavorite queries / annotationsCharacteristics derived from past useCharacteristics derived from past use

Learning techniques:Learning techniques:Represent, learn and compare current use profiles Represent, learn and compare current use profiles to improve future use.to improve future use.Learning during a login sessionLearning during a login sessionRanking resultsRanking results

1818Multi-agent ArchitectureMulti-agent Architecture

Corporate Memory


UserAgent

Learning

Interest GroupAgent


UserAgent

Learning


KnowledgeEngineer


End User

Annotation

Document

Annotation

Document

Annotation

Document

Annotation

Document

Ontology

Models


Query

1919Principal interest of MAS in CoMMAPrincipal interest of MAS in CoMMA

One functional architecture leading to several One functional architecture leading to several possible configurations in order to adapt to the possible configurations in order to adapt to the broad range of environments that can be found in broad range of environments that can be found in a companya companyArchitecture:Architecture: Agent kinds and their relationship Agent kinds and their relationship

Fixed at design timeFixed at design timeConfiguration:Configuration: Exact topography of a given MAS Exact topography of a given MAS

Fixed at deployment timeFixed at deployment time Flexible distribution :Flexible distribution :

Locally adapt to resources and usersLocally adapt to resources and usersGlobal capitalization through cooperationGlobal capitalization through cooperation

Integration of different technologiesIntegration of different technologies

2020Societies, Roles and InteractionsSocieties, Roles and Interactions

Ontology and Model SocietyOntology and Model Society

Ontologist AgentsOntologist Agents

Annotations SocietyAnnotations Society

MediatorsMediators

ArchivistsArchivists

Users' societyUsers' society

Profile Profile ManagersManagers

Profiles Profiles ArchivistsArchivists

InterfaceInterfaceControllersControllers

Interconnection SocietyInterconnection Society

FederatedFederatedMatchmakersMatchmakers

2121ConclusionConclusion

Corporate Memory


UserAgent

Learning

Interest GroupAgent


UserAgent

Learning


KnowledgeEngineer


End User

Annotation

Document

Annotation

Document

Annotation

Document

Annotation

Document

Ontology

Models


Query

Done

Done

2222

TECHNOLOGY MONITORING

ANNOTATION

PUSH

Engineer (internal / informal

sources)

Archivist (external sources)

Docs+

An

notation

s

User

Area referent

CoordinationStrategic orientation

RETRIEVAL Query

Index card ,Synthesis,

The Technology Monitoring scenarioThe Technology Monitoring scenario

The diffusion of innovative ideas among employeesThe diffusion of innovative ideas among employees

Authors

2323

The actors of the Technology Monitoring scenario :The actors of the Technology Monitoring scenario :

Archivist Archivist in charge of feeding the systemin charge of feeding the system -> Author -> Author

Engineer and ResearcherEngineer and Researcherwatching his expertise Areawatching his expertise Area -> User -> Userfeeding the system with new informationfeeding the system with new information -> Author -> Authorin charge of identifyingin charge of identifying correspondents and coordinating correspondents and coordinating

thematic groupsthematic groups -> Area referent-> Area referent

The actorsThe actors

2424Examples of Supported tasksExamples of Supported tasks

For the For the AuthorsAuthors:: Indexing information by annotating companies, Indexing information by annotating companies,

people, documents...people, documents...

For the For the Area referentsArea referents:: Identifying resources, skills about given business Identifying resources, skills about given business

domainsdomains

For the For the UsersUsers: : Being automatically informed about relevant Being automatically informed about relevant

information according to their profile (push mode)information according to their profile (push mode)Querying the system (pull mode)Querying the system (pull mode)

2525

Search

Presentation

Question

FAQ

Help

Relation

Be Evaluated

tutor

Corporate Memory

Human resource

Updating profile

Newcomer

NEI Scenario: the “insertion of new employees” in the company NEI Scenario: the “insertion of new employees” in the company concerns the new employees who need to handle a lot of new concerns the new employees who need to handle a lot of new

information about their enterprise in a very short time, to be information about their enterprise in a very short time, to be rapidly efficientrapidly efficient

2626The actorsThe actors

The NE who just arrived in his new companyThe NE who just arrived in his new companynot familiar with the environmentnot familiar with the environmentneeding answers to many standard questionsneeding answers to many standard questions

The tutorThe tutorperson responsible to support NEs during the first person responsible to support NEs during the first

weeksweekswith CoMMA responsible to fill the annotation basewith CoMMA responsible to fill the annotation base

2727CoMMA solutionCoMMA solution

5 major components:5 major components: An ontology (O’CoMMA)An ontology (O’CoMMA) A multi-agent system,A multi-agent system, A Semantic search engine (CORESE),A Semantic search engine (CORESE), A machine learning algorithmA machine learning algorithm A GUIA GUI

The CoThe CoMMAMMA technical solution for the technical solution for the implementation of a Corporate memory.implementation of a Corporate memory.

The CoMMA Solution


splitting resources / system:splitting resources / system:

Final user

ArchitectureArchitecture

Multi Agent system

Document authors and annotators.

Knowledge manager

Corporate Memory

User Agent

Learning

Annotation

Document

Ontology

Model Enterprise model

Annotation

Document

Annotation

Document

Annotation

Document

Request

Ontology and Model Agent

Learning

User Agent

Connecting Agent

Userprofile

Userprofile

Connecting Agent Document

Agent(s)

Learning

User Agent


splitting resources / system:splitting resources / system: the document resourcesthe document resources

Final user


Multi Agent system


Knowledge manager

Corporate Memory

User Agent

Learning

Annotation

Document

Ontology


Annotation

Document

Annotation

Document

Annotation

Document

Request


Learning

User Agent

Connecting Agent

Userprofile

Userprofile


Agent(s)

Learning

User Agent


Final user


Multi Agent system


Knowledge manager

Corporate Memory

User Agent

Learning

Annotation

Document

Ontology


Annotation

Document

Annotation

Document

Annotation

Document

Request


Learning

User Agent

Connecting Agent

Userprofile

Userprofile


Agent(s)

Learning

User Agent

splitting resources / system:splitting resources / system: the document resourcesthe document resources the configuration resourcesthe configuration resources


Final user


Multi Agent system


Knowledge manager

Corporate Memory

User Agent

Learning

Annotation

Document

Ontology


Annotation

Document

Annotation

Document

Annotation

Document

Request


Learning

User Agent

Connecting Agent

Userprofile

Userprofile


Agent(s)

Learning

User Agent


• OntologyOntology


Final user


Multi Agent system


Knowledge manager

Corporate Memory

User Agent

Learning

Annotation

Document

Ontology


Annotation

Document

Annotation

Document

Annotation

Document

Request


Learning

User Agent

Connecting Agent

Userprofile

Userprofile


Agent(s)

Learning

User Agent

Ontology O’CoMMAOntology O’CoMMA Dedicated to corporate memory,Dedicated to corporate memory, Represented in RDFS,Represented in RDFS,


rdfs:Class for concepts of the rdfs:Class for concepts of the ontology, ontology, Possibility to use class inheritancePossibility to use class inheritance

Final user


Multi Agent system


Knowledge manager

Corporate Memory

User Agent

Learning

Annotation

Document

Ontology


Annotation

Document

Annotation

Document

Annotation

Document

Request


Learning

User Agent

Connecting Agent

Userprofile

Userprofile


Agent(s)

Learning

User Agent

Ontology

Engineer

Employee

rdfs:subClassOf


rdf:Property for relations of the ontology,rdf:Property for relations of the ontology,specialization of properties :specialization of properties :

director subPropertyOf managerdirector subPropertyOf manager

director director manager manager

Final user


Multi Agent system


Knowledge manager

Corporate Memory

User Agent

Learning

Annotation

Document

Ontology


Annotation

Document

Annotation

Document

Annotation

Document

Request


Learning

User Agent

Connecting Agent

Userprofile

Userprofile


Agent(s)

Learning

User Agent

Ontology

isInterestedByEngineer

Employee

rdfs:subClassOf

Topic


rdfs:label for synonyms and multi- rdfs:label for synonyms and multi- language of the ontology,language of the ontology,

Use of stylesheet to filter terminology Use of stylesheet to filter terminology and multi-language.and multi-language.

Final user


Multi Agent system


Knowledge manager

Corporate Memory

User Agent

Learning

Annotation

Document

Ontology


Annotation

Document

Annotation

Document

Annotation

Document

Request


Learning

User Agent

Connecting Agent

Userprofile

Userprofile


Agent(s)

Learning

User Agent

Ontology

Engineer

Employee

rdfs:subClassOfIngénieur

cadre technique

rdfs:label


rdfs:comment for natural language rdfs:comment for natural language definitiondefinition the link between definition and concept the link between definition and concept

is kept is kept ontology “tracontology “trackkability”ability”

Final user


Multi Agent system


Knowledge manager

Corporate Memory

User Agent

Learning

Annotation

Document

Ontology


Annotation

Document

Annotation

Document

Annotation

Document

Request


Learning

User Agent

Connecting Agent

Userprofile

Userprofile


Agent(s)

Learning

User Agent

Ontology

Engineer

Employee

rdfs:subClassOf

Professionnel quitravaille dans unecertaine branche d

ingenierie ou l’on utilise laconnaissance

scientifique pourresoudre des

problemespratiques

rdfs:comment

Professional whoworks in some

branch ofengineering using

scientificknowledge to solvepractical problems.

rdfs:comment

3737RDFS Example : ClassRDFS Example : Class

<rdfs:Class rdf:ID="Document"><rdfs:Class rdf:ID="Document"> <rdfs:subClassOf rdf:resource="#Entity"/><rdfs:subClassOf rdf:resource="#Entity"/>

<rdfs:subClassOf rdf:resource="#EntityConcerningATopic"/><rdfs:subClassOf rdf:resource="#EntityConcerningATopic"/>

<rdfs:subClassOf rdf:resource="#NumberableEntity"/><rdfs:subClassOf rdf:resource="#NumberableEntity"/>

<rdfs:comment xml:lang="en">Entity including elements serving as <rdfs:comment xml:lang="en">Entity including elements serving as a representation of thinking.a representation of thinking.

</rdfs:comment></rdfs:comment>

<rdfs:comment xml:lang="fr">Entite comprenant des elements de <rdfs:comment xml:lang="fr">Entite comprenant des elements de representation de la pensee.representation de la pensee.


<rdfs:label xml:lang="en">document</rdfs:label><rdfs:label xml:lang="en">document</rdfs:label>

<rdfs:label xml:lang="fr">document</rdfs:label><rdfs:label xml:lang="fr">document</rdfs:label>

</rdfs:Class></rdfs:Class>

3838RDFS Example : PropertyRDFS Example : Property

<rdf:Property rdf:ID="Title"><rdf:Property rdf:ID="Title"><rdfs:subPropertyOf rdf:resource="#Designation"/><rdfs:subPropertyOf rdf:resource="#Designation"/>

<rdfs:range rdf:resource="&rdfs;Literal"/><rdfs:range rdf:resource="&rdfs;Literal"/>

<rdfs:domain rdf:resource="#Document"/><rdfs:domain rdf:resource="#Document"/>

<rdfs:comment xml:lang="en">Designation of a document.<rdfs:comment xml:lang="en">Designation of a document.


<rdfs:comment xml:lang="fr">Designation du document.<rdfs:comment xml:lang="fr">Designation du document.


<rdfs:label xml:lang="en">title</rdfs:label><rdfs:label xml:lang="en">title</rdfs:label>

<rdfs:label xml:lang="fr">titre</rdfs:label><rdfs:label xml:lang="fr">titre</rdfs:label>

</rdf:Property></rdf:Property>


Final user


Multi Agent system


Knowledge manager

Corporate Memory

User Agent

Learning

Annotation

Document

Ontology


Annotation

Document

Annotation

Document

Annotation

Document

Request


Learning

User Agent

Connecting Agent

Userprofile

Userprofile


Agent(s)

Learning

User Agent


• Ontology, Enterprise modelOntology, Enterprise model

4040Enterprise ModelEnterprise Model

<c:LegalCorporation rdf:about="http://www.inria.fr/"/><c:LegalCorporation rdf:about="http://www.inria.fr/"/>

<c:NationalOrganizationGroup rdf:about="http://www.inria.fr/"><c:NationalOrganizationGroup rdf:about="http://www.inria.fr/">

<c:Designation>Institut National de Recherche en Informatique <c:Designation>Institut National de Recherche en Informatique et Automatique</c:Designation>et Automatique</c:Designation>

<c:HasForActivity><c:Research/></c:HasForActivity><c:HasForActivity><c:Research/></c:HasForActivity>

<c:IsInterestedBy><c:ComputerScienceTopic/><c:IsInterestedBy><c:ComputerScienceTopic/></c:IsInterestedBy></c:IsInterestedBy>

<c:IsInterestedBy><c:MathematicsTopic/></c:IsInterestedBy><c:IsInterestedBy><c:MathematicsTopic/></c:IsInterestedBy>

……

4141

<c:LocalOrganizationGroup rdf:about="http://www-sop.inria.fr/"><c:LocalOrganizationGroup rdf:about="http://www-sop.inria.fr/">

<c:Designation>UR Sophia Antipolis de l'INRIA: Institut <c:Designation>UR Sophia Antipolis de l'INRIA: Institut National de Recherche en Informatique et National de Recherche en Informatique et Automatique</c:Designation>Automatique</c:Designation>

<c:HasForActivity><c:Research/></c:HasForActivity><c:HasForActivity><c:Research/></c:HasForActivity>

<c:IsInterestedBy><c:ComputerScienceTopic/><c:IsInterestedBy><c:ComputerScienceTopic/></c:IsInterestedBy></c:IsInterestedBy>

<c:Include><c:Include><c:ProjectGroup <c:ProjectGroup rdf:about="http://www.inria.fr/recherche/equipes/acacia.en.html"rdf:about="http://www.inria.fr/recherche/equipes/acacia.en.html"/></c:Include>/></c:Include>

<c:Include><c:Include><c:ProjectGroup rdf:about="http://www-sop.inria.fr/tropics/"/><c:ProjectGroup rdf:about="http://www-sop.inria.fr/tropics/"/></c:Include></c:Include>

<c:Include><c:Include><c:ProjectGroup rdf:about="http://www-sop.inria.fr/cafe/"/><c:ProjectGroup rdf:about="http://www-sop.inria.fr/cafe/"/></c:Include></c:Include>


Final user


Multi Agent system


Knowledge manager

Corporate Memory

User Agent

Learning

Annotation

Document

Ontology


Annotation

Document

Annotation

Document

Annotation

Document

Request


Learning

User Agent

Connecting Agent

Userprofile

Userprofile


Agent(s)

Learning

User Agent


• Ontology, Enterprise model, User profilesOntology, Enterprise model, User profiles

4343User Profile ExampleUser Profile Example

<c:IndividualProfile rdf:about="#"><c:IndividualProfile rdf:about="#"><c:CreationDate>an 2000</c:CreationDate><c:CreationDate>an 2000</c:CreationDate>

<c:Title>Employee profile of Olivier Corby</c:Title><c:Title>Employee profile of Olivier Corby</c:Title>

</c:IndividualProfile></c:IndividualProfile>

<c:Employee <c:Employee rdf:ID = "http://www-sop.inria.fr/acacia/personnel/corby/"> rdf:ID = "http://www-sop.inria.fr/acacia/personnel/corby/">

<c:FamilyName>Corby</c:FamilyName><c:FamilyName>Corby</c:FamilyName>

<c:FirstName>Olivier</c:FirstName><c:FirstName>Olivier</c:FirstName>

<c:HasForOntologicalEntrancePoint><c:KnowledgeModelingTo<c:HasForOntologicalEntrancePoint><c:KnowledgeModelingTopic/></c:HasForOntologicalEntrancePoint>pic/></c:HasForOntologicalEntrancePoint><c:HasForOntologicalEntrancePoint><c:ObjectProgrammingTo<c:HasForOntologicalEntrancePoint><c:ObjectProgrammingTopic/></c:HasForOntologicalEntrancePoint>pic/></c:HasForOntologicalEntrancePoint>


Final user


Multi Agent system


Knowledge manager

Corporate Memory

User Agent

Learning

Annotation

Document

Ontology


Annotation

Document

Annotation

Document

Annotation

Document

Request


Learning

User Agent

Connecting Agent

Userprofile

Userprofile


Agent(s)

Learning

User Agent

splitting resources / system:splitting resources / system: the document resourcesthe document resources the configuration resourcesthe configuration resources the multi agent system frameworkthe multi agent system framework


Learning

User Agent

Gui: building an annotation.Gui: building an annotation.

4646

Learning

User Agent

CoMMA solutionCoMMA solution

Machine Learning technique:Machine Learning technique:

use feedbacks to learn document relevancyuse feedbacks to learn document relevancy

feedback from one user can be generalized feedback from one user can be generalized to users having the same fields of interest,to users having the same fields of interest,

is designed for both pull mode and push is designed for both pull mode and push modemode


Final user


Multi Agent system


Knowledge manager

Corporate Memory

User Agent

Learning

Annotation

Document

Ontology


Annotation

Document

Annotation

Document

Annotation

Document

Request


Learning

User Agent

Connecting Agent

Userprofile

Userprofile


Agent(s)

Learning

User Agent

DocumentAgent(s)

Multi-agent system:Multi-agent system:document sub societydocument sub society

4848

DocumentAgent(s)

CoMMA solutionCoMMA solution

Multi-agent system:Multi-agent system:document sub societydocument sub society

CORESE a semantic search engineCORESE a semantic search engine relies on RDF(S) and conceptual relies on RDF(S) and conceptual

graph theory,graph theory, use of the inheritance graph of RDFS use of the inheritance graph of RDFS

(specialization and generalization),(specialization and generalization), Inference mechanismsInference mechanisms manage the annotation distributionmanage the annotation distribution Java API wrapped into an agentJava API wrapped into an agent

4949RDF AnnotationRDF Annotation

<c:ResearchReport <c:ResearchReport rdf:about='http://www.inria.fr/rapports/sophia/RR-3819.html'>rdf:about='http://www.inria.fr/rapports/sophia/RR-3819.html'>

<c:CreatedBy><c:CreatedBy>

<c:Person rdf:about='http://www.inria.fr/nada.matta'><c:Person rdf:about='http://www.inria.fr/nada.matta'>

<c:FamilyName>Matta</c:FamilyName><c:FamilyName>Matta</c:FamilyName>

<c:FirstName>Nada</c:FirstName><c:FirstName>Nada</c:FirstName>

</c:Person> </c:Person>

</c:CreatedBy></c:CreatedBy>


<c:Person rdf:about='http://www.inria.fr/olivier.corby'><c:Person rdf:about='http://www.inria.fr/olivier.corby'>

<c:FamilyName>Corby</c:FamilyName><c:FamilyName>Corby</c:FamilyName>

<c:FirstName>Olivier</c:FirstName><c:FirstName>Olivier</c:FirstName>

</c:Person> </c:Person>


5050RDF AnnotationRDF Annotation


<c:ProjectGroup rdf:about= <c:ProjectGroup rdf:about= 'http://www.inria.fr/recherche/equipes/acacia.en.html'>'http://www.inria.fr/recherche/equipes/acacia.en.html'>

<c:Designation>Acacia</c:Designation><c:Designation>Acacia</c:Designation>

<c:hasCreated rdf:resource=<c:hasCreated rdf:resource='http://www.inria.fr/rapports/sophia/RR-3819.html'/>'http://www.inria.fr/rapports/sophia/RR-3819.html'/>

</c:ProjectGroup> </c:ProjectGroup>


<c:CreationDate>11-1999</c:CreationDate><c:CreationDate>11-1999</c:CreationDate>

<c:Title> Méthodes de capitalisation de mémoire de projet <c:Title> Méthodes de capitalisation de mémoire de projet

</c:Title></c:Title>


Final user


Multi Agent system


Knowledge manager

Corporate Memory

User Agent

Learning

Annotation

Document

Ontology


Annotation

Document

Annotation

Document

Annotation

Document

Request


Learning

User Agent

Connecting Agent

Userprofile

Userprofile


Agent(s)

Learning

User Agent

Connecting Agent

Multi-agent system:Multi-agent system: Interconnecting sub societyInterconnecting sub society


Connecting Agent

Multi-agent system:Multi-agent system: Interconnecting sub societyInterconnecting sub society

A Distributed annotations management algorithmRelies on:• metrics that evaluate the semantic similiarity of annotations• complex protocols between « connecting agents » and « document agents » to rebuild the splitted annotation.


PR

EL

IMIN

AR

AN

AL

YSIS

SP

EC

IFIC

AT

ION

ANALYSIS OF INTERVIEWS

EXPLICITITATION OF ACTORS, ENTITIES,

RELATIONSHIPS, FUNCTIONALITIES

IMPLICIT WORLD OF THE COMPANY EXPLICIT WORLD OF THE COMPANY

COLLECTION OF DOCUMENT ABOUT THE COMPANY

ANALYSIS OF ORGANIZATION

INFORMAL MODEL OF THE

ENTERPRISE

FORMAL USER

MODEL

ONTOLOGIES ENTERPRISE MODEL

USER PROFILE

COMPLEMENTAR INTERVIEWS

OBJECT & AGENT WORLD

DOCUMENT TEMPLATES

XML, RDF,…

INTERVIEWS

REQUIREMENTS

FORMAL MODEL OF ENTERPRISE

Agent sub-societies

DE

SIGN

(UM

L)

Technical, Organisational, user

Data collection

DEPLOYMENT

Agent roles

Agent interactions

The CoMMA Methodology

5454Other project resultsOther project results

Other project results

• O ’CoMMA ontology • Extension of RDF(S) language for representing knowledge• CORESE + new inference mechanisms• Techniques of categorization of RDF-annotated documents• Multi-agent architecture for IR• RDF-based JADE ontology & content language • Management of distribution of annotations and of queries.• Machine learning techniques

5555

Specific Layer

High layer

Middle Layer

Ontology O ’CoMMAOntology O ’CoMMA

Aspects Aspects EnterpriseEnterprise

Aspects Aspects DocumentDocument

Aspects Aspects UserUser

Aspects Aspects DomainDomain

Method: Data collection, Terminological Phase , Method: Data collection, Terminological Phase , Structuration, Validation, Formalization in RDFSStructuration, Validation, Formalization in RDFS

Result: Result: 420 concepts, 50 relations, 630 terms, 420 concepts, 50 relations, 630 terms, 12 levels of depth12 levels of depth

5656

RDF Schema

RDF Annotations

RDF Query

CORESE search engineCORESE search engine

CGSupport

CGFact Base

Query Graph

Resultsin CG

Results in RDF

CORESE

Translation Projection Translation

5757Relation propertiesRelation properties

On

tolo

gie

Country Company Person

employs subdivisionOfactivityInanimate

Entity

nationality

domain

range

RD

FS

subClassOfrangedomain typesubPropertyOf

Property

Resource

Class

Literal

An

no

tatio n

RD

F www.T-Nova.de www.DeutscheTelekom.desubdivisionOf activity

Telecom


RDF(S)

rdfs:Class

rdf:Property

domain, range

Resource

Property

RDF Annotation

CG

Concept Type

Relation Type

Signature

Concept

Relation

Conceptual Graph

RDF & CG


Relation properties

Transitivity, Symmetry, Reflexivity, Inverse for RDF properties:

• Annotations are augmented with new knowledge deduced from these properties

• Transitivity, symmetry and inverse are computed once and added to annotations

• Reflexivity is computed on the fly according to queries

6060Inference Rules for RDFInference Rules for RDF

Inference Rules for RDF• Augment the ontology with rules that enable to deduce and add new knowledge to annotations

IF a team participates to a consortium AND a person is a member of the team

THEN the person participates to the consortium

IF [Person:?p]-(member)-[Team:?t]-(participates)-[Consortium:?c]THEN [Person:?p]-(participates)-[Consortium:?c]

• Forward chaining inference engine

6161Inference Rules for RDFInference Rules for RDF

RDF Rule Syntax<cos:rule><cos:if><c:Person rdf:about=‘?p’>

<c:member><c:Team rdf:about=‘?t’>

<c:participate><c:Consortium rdf:about=‘?c’/>

</c:participate></c:Team

</c:member></c:Person</cos:if><cos:then><c:Person rdf:about=‘?p’>

<c:participate><c:Consortium rdf:about=‘?c’/>

<c:participate></c:Person</cos:then></cos:rule>


Conclusion

The CoMMA system is implemented : A Corporate Semantic Webhttp://www.si.fr.atosorigin.com/sophia/comma

tested at T Nova Systems (Deutsche Telekom) and CSTB

testbed for Corporate Semantic Web technologies :

XML, Agents, Ontology, Semantic metadata, Learning


Conclusion (2)

Corese semantic engine : RDF(S) and Conceptual Graphs

tested at Renault on a design project memory

tested with the Gene Ontology